OLA_case_study.ipynb

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          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=16g-OJ_4GnG0P8FEFgTa50RMubVSBvIZl\n",
            "To: /content/ola_driver_scaler.csv\n",
            "\r  0% 0.00/1.13M [00:00<?, ?B/s]\r100% 1.13M/1.13M [00:00<00:00, 13.4MB/s]\n"
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      "source": [
        "!gdown 16g-OJ_4GnG0P8FEFgTa50RMubVSBvIZl"
      ]
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
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      "outputs": []
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        "data = pd.read_csv('/content/ola_driver_scaler.csv')\n",
        "data = pd.DataFrame(data)"
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        "data['LastWorkingDate'] = pd.to_datetime(data['LastWorkingDate'])"
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          "text": [
            "<ipython-input-7-dd2e659bcc52>:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  data['LastWorkingDate'] = pd.to_datetime(data['LastWorkingDate'])\n"
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              "Index(['Unnamed: 0', 'MMM-YY', 'Driver_ID', 'Age', 'Gender', 'City',\n",
              "       'Education_Level', 'Income', 'Dateofjoining', 'LastWorkingDate',\n",
              "       'Joining Designation', 'Grade', 'Total Business Value',\n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 19104 entries, 0 to 19103\n",
            "Data columns (total 14 columns):\n",
            " #   Column                Non-Null Count  Dtype         \n",
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            " 4   Gender                19052 non-null  float64       \n",
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            " 10  Joining Designation   19104 non-null  int64         \n",
            " 11  Grade                 19104 non-null  int64         \n",
            " 12  Total Business Value  19104 non-null  int64         \n",
            " 13  Quarterly Rating      19104 non-null  int64         \n",
            "dtypes: datetime64[ns](1), float64(2), int64(8), object(3)\n",
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        "data['Dateofjoining'] = pd.to_datetime(data['Dateofjoining'])\n",
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            "<ipython-input-11-1407db1ea250>:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  data['Dateofjoining'] = pd.to_datetime(data['Dateofjoining'])\n",
            "<ipython-input-11-1407db1ea250>:2: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  data['MMM-YY'] = pd.to_datetime(data['MMM-YY'])\n"
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            " 11  Total Business Value  19104 non-null  int64         \n",
            " 12  Quarterly Rating      19104 non-null  int64         \n",
            "dtypes: datetime64[ns](3), float64(2), int64(7), object(1)\n",
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              "          MMM-YY  Driver_ID   Age  Gender City  Education_Level  Income  \\\n",
              "0     2019-01-01          1  28.0     0.0  C23                2   57387   \n",
              "1     2019-02-01          1  28.0     0.0  C23                2   57387   \n",
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              "4     2020-12-01          2  31.0     0.0   C7                2   67016   \n",
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              "19099 2020-08-01       2788  30.0     0.0  C27                2   70254   \n",
              "19100 2020-09-01       2788  30.0     0.0  C27                2   70254   \n",
              "19101 2020-10-01       2788  30.0     0.0  C27                2   70254   \n",
              "19102 2020-11-01       2788  30.0     0.0  C27                2   70254   \n",
              "19103 2020-12-01       2788  30.0     0.0  C27                2   70254   \n",
              "\n",
              "      Dateofjoining LastWorkingDate  Joining Designation  Grade  \\\n",
              "0        2018-12-24             NaT                    1      1   \n",
              "1        2018-12-24             NaT                    1      1   \n",
              "2        2018-12-24      2019-03-11                    1      1   \n",
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              "4        2020-11-06             NaT                    2      2   \n",
              "...             ...             ...                  ...    ...   \n",
              "19099    2020-06-08             NaT                    2      2   \n",
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              "19102    2020-06-08             NaT                    2      2   \n",
              "19103    2020-06-08             NaT                    2      2   \n",
              "\n",
              "       Total Business Value  Quarterly Rating  \n",
              "0                   2381060                 2  \n",
              "1                   -665480                 2  \n",
              "2                         0                 2  \n",
              "3                         0                 1  \n",
              "4                         0                 1  \n",
              "...                     ...               ...  \n",
              "19099                740280                 3  \n",
              "19100                448370                 3  \n",
              "19101                     0                 2  \n",
              "19102                200420                 2  \n",
              "19103                411480                 2  \n",
              "\n",
              "[19104 rows x 13 columns]"
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 19104,\n  \"fields\": [\n    {\n      \"column\": \"MMM-YY\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-01-01 00:00:00\",\n        \"max\": \"2020-12-01 00:00:00\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"2020-03-01 00:00:00\",\n          \"2019-10-01 00:00:00\",\n          \"2019-01-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Driver_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 810,\n        \"min\": 1,\n        \"max\": 2788,\n        \"num_unique_values\": 2381,\n        \"samples\": [\n          1663,\n          1264,\n          1618\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.2579116861907345,\n        \"min\": 21.0,\n        \"max\": 58.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          58.0,\n          41.0,\n          24.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4933670037660394,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 29,\n        \"samples\": [\n          \"C22\",\n          \"C5\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education_Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30914,\n        \"min\": 10747,\n        \"max\": 188418,\n        \"num_unique_values\": 2383,\n        \"samples\": [\n          44273,\n          35370\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dateofjoining\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2013-04-01 00:00:00\",\n        \"max\": \"2020-12-28 00:00:00\",\n        \"num_unique_values\": 869,\n        \"samples\": [\n          \"2019-09-14 00:00:00\",\n          \"2018-01-06 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LastWorkingDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-12-31 00:00:00\",\n        \"max\": \"2020-12-28 00:00:00\",\n        \"num_unique_values\": 493,\n        \"samples\": [\n          \"2020-05-03 00:00:00\",\n          \"2019-10-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Joining Designation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total Business Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1128312,\n        \"min\": -6000000,\n        \"max\": 33747720,\n        \"num_unique_values\": 10181,\n        \"samples\": [\n          431090,\n          720180\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quarterly Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[data['Driver_ID'] == 2784]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 801
        },
        "id": "Ca_c99WeBNYc",
        "outputId": "74ad29a2-1adf-4982-812d-1ccd7c08af33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          MMM-YY  Driver_ID   Age  Gender City  Education_Level  Income  \\\n",
              "19055 2019-01-01       2784  33.0     0.0  C24                0   82815   \n",
              "19056 2019-02-01       2784  33.0     0.0  C24                0   82815   \n",
              "19057 2019-03-01       2784  33.0     0.0  C24                0   82815   \n",
              "19058 2019-04-01       2784  33.0     0.0  C24                0   82815   \n",
              "19059 2019-05-01       2784  33.0     0.0  C24                0   82815   \n",
              "19060 2019-06-01       2784  33.0     0.0  C24                0   82815   \n",
              "19061 2019-07-01       2784  33.0     0.0  C24                0   82815   \n",
              "19062 2019-08-01       2784  33.0     0.0  C24                0   82815   \n",
              "19063 2019-09-01       2784  33.0     0.0  C24                0   82815   \n",
              "19064 2019-10-01       2784  33.0     0.0  C24                0   82815   \n",
              "19065 2019-11-01       2784  33.0     0.0  C24                0   82815   \n",
              "19066 2019-12-01       2784  33.0     0.0  C24                0   82815   \n",
              "19067 2020-01-01       2784  34.0     0.0  C24                0   82815   \n",
              "19068 2020-02-01       2784  34.0     0.0  C24                0   82815   \n",
              "19069 2020-03-01       2784  34.0     0.0  C24                0   82815   \n",
              "19070 2020-04-01       2784  34.0     0.0  C24                0   82815   \n",
              "19071 2020-05-01       2784  34.0     0.0  C24                0   82815   \n",
              "19072 2020-06-01       2784  34.0     0.0  C24                0   82815   \n",
              "19073 2020-07-01       2784  34.0     0.0  C24                0   82815   \n",
              "19074 2020-08-01       2784  34.0     0.0  C24                0   82815   \n",
              "19075 2020-09-01       2784  34.0     0.0  C24                0   82815   \n",
              "19076 2020-10-01       2784  34.0     0.0  C24                0   82815   \n",
              "19077 2020-11-01       2784  34.0     0.0  C24                0   82815   \n",
              "19078 2020-12-01       2784  34.0     0.0  C24                0   82815   \n",
              "\n",
              "      Dateofjoining LastWorkingDate  Joining Designation  Grade  \\\n",
              "19055    2015-10-15             NaT                    2      3   \n",
              "19056    2015-10-15             NaT                    2      3   \n",
              "19057    2015-10-15             NaT                    2      3   \n",
              "19058    2015-10-15             NaT                    2      3   \n",
              "19059    2015-10-15             NaT                    2      3   \n",
              "19060    2015-10-15             NaT                    2      3   \n",
              "19061    2015-10-15             NaT                    2      3   \n",
              "19062    2015-10-15             NaT                    2      3   \n",
              "19063    2015-10-15             NaT                    2      3   \n",
              "19064    2015-10-15             NaT                    2      3   \n",
              "19065    2015-10-15             NaT                    2      3   \n",
              "19066    2015-10-15             NaT                    2      3   \n",
              "19067    2015-10-15             NaT                    2      3   \n",
              "19068    2015-10-15             NaT                    2      3   \n",
              "19069    2015-10-15             NaT                    2      3   \n",
              "19070    2015-10-15             NaT                    2      3   \n",
              "19071    2015-10-15             NaT                    2      3   \n",
              "19072    2015-10-15             NaT                    2      3   \n",
              "19073    2015-10-15             NaT                    2      3   \n",
              "19074    2015-10-15             NaT                    2      3   \n",
              "19075    2015-10-15             NaT                    2      3   \n",
              "19076    2015-10-15             NaT                    2      3   \n",
              "19077    2015-10-15             NaT                    2      3   \n",
              "19078    2015-10-15             NaT                    2      3   \n",
              "\n",
              "       Total Business Value  Quarterly Rating  \n",
              "19055               1226590                 3  \n",
              "19056                203290                 3  \n",
              "19057               4495040                 3  \n",
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              "19060                     0                 1  \n",
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              "19062                270710                 1  \n",
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              "19072                979270                 3  \n",
              "19073                252000                 3  \n",
              "19074               1260090                 3  \n",
              "19075                400000                 3  \n",
              "19076               3087830                 4  \n",
              "19077                     0                 4  \n",
              "19078                505480                 4  "
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              "      <td>1226590</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19056</th>\n",
              "      <td>2019-02-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>203290</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19057</th>\n",
              "      <td>2019-03-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4495040</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19058</th>\n",
              "      <td>2019-04-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>100450</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19059</th>\n",
              "      <td>2019-05-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>226010</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19060</th>\n",
              "      <td>2019-06-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19061</th>\n",
              "      <td>2019-07-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>102770</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19062</th>\n",
              "      <td>2019-08-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>270710</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19063</th>\n",
              "      <td>2019-09-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>271380</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19064</th>\n",
              "      <td>2019-10-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>990000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19065</th>\n",
              "      <td>2019-11-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>591710</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19066</th>\n",
              "      <td>2019-12-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>194010</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19067</th>\n",
              "      <td>2020-01-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1309620</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19068</th>\n",
              "      <td>2020-02-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>850050</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19069</th>\n",
              "      <td>2020-03-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4128460</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19070</th>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>150260</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19071</th>\n",
              "      <td>2020-05-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>153800</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19072</th>\n",
              "      <td>2020-06-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>979270</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19073</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>252000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19074</th>\n",
              "      <td>2020-08-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1260090</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19075</th>\n",
              "      <td>2020-09-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>400000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19076</th>\n",
              "      <td>2020-10-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3087830</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19077</th>\n",
              "      <td>2020-11-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19078</th>\n",
              "      <td>2020-12-01</td>\n",
              "      <td>2784</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C24</td>\n",
              "      <td>0</td>\n",
              "      <td>82815</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>505480</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d0cebe13-c90f-4427-b15b-80d3ab0f747b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d0cebe13-c90f-4427-b15b-80d3ab0f747b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d0cebe13-c90f-4427-b15b-80d3ab0f747b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
              "<div id=\"df-dca050e7-b4fc-46f4-978e-5c3f37ff5914\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dca050e7-b4fc-46f4-978e-5c3f37ff5914')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"data[data['Driver_ID'] == 2784]\",\n  \"rows\": 24,\n  \"fields\": [\n    {\n      \"column\": \"MMM-YY\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-01-01 00:00:00\",\n        \"max\": \"2020-12-01 00:00:00\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"2019-09-01 00:00:00\",\n          \"2020-05-01 00:00:00\",\n          \"2019-01-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Driver_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2784,\n        \"max\": 2784,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2784\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5107539184552492,\n        \"min\": 33.0,\n        \"max\": 34.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          34.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"C24\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education_Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 82815,\n        \"max\": 82815,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          82815\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dateofjoining\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2015-10-15 00:00:00\",\n        \"max\": \"2015-10-15 00:00:00\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2015-10-15 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LastWorkingDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"NaT\",\n        \"max\": \"NaT\",\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Joining Designation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 3,\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total Business Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1246169,\n        \"min\": 0,\n        \"max\": 4495040,\n        \"num_unique_values\": 23,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quarterly Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 3,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[data['Gender'] != 0.0] # num of female drivers."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "VZM29X4W_2fq",
        "outputId": "7c357d04-30e4-4fc7-fa12-95b6f7cfa3d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          MMM-YY  Driver_ID   Age  Gender City  Education_Level  Income  \\\n",
              "13    2020-08-01          6  31.0     1.0  C11                1   78728   \n",
              "14    2020-09-01          6  31.0     1.0  C11                1   78728   \n",
              "15    2020-10-01          6  31.0     1.0  C11                1   78728   \n",
              "16    2020-11-01          6  31.0     1.0  C11                1   78728   \n",
              "17    2020-12-01          6  31.0     1.0  C11                1   78728   \n",
              "...          ...        ...   ...     ...  ...              ...     ...   \n",
              "19092 2019-02-01       2787  28.0     1.0  C20                2   69498   \n",
              "19093 2019-03-01       2787  28.0     1.0  C20                2   69498   \n",
              "19094 2019-04-01       2787  28.0     1.0  C20                2   69498   \n",
              "19095 2019-05-01       2787  28.0     1.0  C20                2   69498   \n",
              "19096 2019-06-01       2787  28.0     1.0  C20                2   69498   \n",
              "\n",
              "      Dateofjoining LastWorkingDate  Joining Designation  Grade  \\\n",
              "13       2020-07-31             NaT                    3      3   \n",
              "14       2020-07-31             NaT                    3      3   \n",
              "15       2020-07-31             NaT                    3      3   \n",
              "16       2020-07-31             NaT                    3      3   \n",
              "17       2020-07-31             NaT                    3      3   \n",
              "...             ...             ...                  ...    ...   \n",
              "19092    2018-07-21             NaT                    1      1   \n",
              "19093    2018-07-21             NaT                    1      1   \n",
              "19094    2018-07-21             NaT                    1      1   \n",
              "19095    2018-07-21             NaT                    1      1   \n",
              "19096    2018-07-21      2019-06-20                    1      1   \n",
              "\n",
              "       Total Business Value  Quarterly Rating  \n",
              "13                        0                 1  \n",
              "14                        0                 1  \n",
              "15                        0                 2  \n",
              "16                  1265000                 2  \n",
              "17                        0                 2  \n",
              "...                     ...               ...  \n",
              "19092                250000                 2  \n",
              "19093                319740                 2  \n",
              "19094                     0                 1  \n",
              "19095                     0                 1  \n",
              "19096                     0                 1  \n",
              "\n",
              "[8030 rows x 13 columns]"
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              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-27a675fe-0e21-457d-a3e2-d7fc925e7eef button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data[data['Gender'] != 0\",\n  \"rows\": 8030,\n  \"fields\": [\n    {\n      \"column\": \"MMM-YY\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-01-01 00:00:00\",\n        \"max\": \"2020-12-01 00:00:00\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"2019-04-01 00:00:00\",\n          \"2019-06-01 00:00:00\",\n          \"2020-08-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Driver_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 793,\n        \"min\": 6,\n        \"max\": 2787,\n        \"num_unique_values\": 1005,\n        \"samples\": [\n          2578,\n          1771,\n          1932\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.33908741448797,\n        \"min\": 21.0,\n        \"max\": 58.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          55.0,\n          21.0,\n          43.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 29,\n        \"samples\": [\n          \"C9\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education_Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30182,\n        \"min\": 10747,\n        \"max\": 148588,\n        \"num_unique_values\": 1023,\n        \"samples\": [\n          36529\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dateofjoining\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2013-04-01 00:00:00\",\n        \"max\": \"2020-12-27 00:00:00\",\n        \"num_unique_values\": 581,\n        \"samples\": [\n          \"2020-04-04 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LastWorkingDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-12-31 00:00:00\",\n        \"max\": \"2020-12-28 00:00:00\",\n        \"num_unique_values\": 350,\n        \"samples\": [\n          \"2019-02-10 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Joining Designation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total Business Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1155477,\n        \"min\": -3791250,\n        \"max\": 33747720,\n        \"num_unique_values\": 4588,\n        \"samples\": [\n          1482700\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quarterly Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = data"
      ],
      "metadata": {
        "id": "MO84-Jr2NCpp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Driver_ID').agg({\n",
        "    'Grade': 'mean'\n",
        "}).nlargest(10, 'Grade')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "-Ndj8Q89KsNU",
        "outputId": "9d46e654-5386-4622-c19f-65726b9f71e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Grade\n",
              "Driver_ID       \n",
              "117          5.0\n",
              "153          5.0\n",
              "308          5.0\n",
              "451          5.0\n",
              "709          5.0\n",
              "1271         5.0\n",
              "1616         5.0\n",
              "1731         5.0\n",
              "1761         5.0\n",
              "1832         5.0"
            ],
            "text/html": [
              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "        document.querySelector('#df-82a302a6-6989-47c9-b995-d1992194fea0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-82a302a6-6989-47c9-b995-d1992194fea0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"})\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Driver_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 716,\n        \"min\": 117,\n        \"max\": 1832,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1761,\n          153,\n          1271\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 5.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# number of unique driver ids., num unique grades\n",
        "data['Driver_ID'].nunique(), data['Grade'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i_MITs3L8_NL",
        "outputId": "923ca2af-bce9-40eb-9da5-eb9cb74b7377"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2381, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "gCmpuOzVOkMh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cols = {\n",
        "    \"MMM-YY\": \"last\",\n",
        "    \"Driver_ID\": \"last\",\n",
        "    \"Age\": \"last\",\n",
        "    \"Gender\": \"last\",\n",
        "    \"City\": \"last\",\n",
        "    \"Education_Level\": \"last\",\n",
        "    \"Income\": \"last\",\n",
        "    \"Dateofjoining\": \"last\",\n",
        "    \"LastWorkingDate\": \"last\",\n",
        "    \"Joining Designation\": \"last\",\n",
        "    \"Grade\": \"last\",\n",
        "    \"Total Business Value\": \"sum\",\n",
        "    \"Quarterly Rating\": \"last\"\n",
        "}"
      ],
      "metadata": {
        "id": "ZMbNoqvt-59-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# checking age difference statistics\n",
        "old = data.groupby('Driver_ID').agg({\n",
        "    'Age': 'first'\n",
        "})\n",
        "\n",
        "latest = data.groupby('Driver_ID').agg({\n",
        "    'Age': 'last'\n",
        "})\n",
        "\n",
        "(latest - old).describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "444-ZQ1WGjUj",
        "outputId": "1dc8bee2-7037-4eb2-b5db-ca2aa8c749ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Age\n",
              "count  2381.000000\n",
              "mean      0.572449\n",
              "std       0.691106\n",
              "min       0.000000\n",
              "25%       0.000000\n",
              "50%       0.000000\n",
              "75%       1.000000\n",
              "max       2.000000"
            ],
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              "      <td>2381.000000</td>\n",
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              "      <td>0.572449</td>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"(latest - old)\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 841.595553304366,\n        \"min\": 0.0,\n        \"max\": 2381.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2381.0,\n          0.5724485510289794,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.kdeplot(latest - old)"
      ],
      "metadata": {
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 89
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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eY3JDZ+RwCiivawXQnnT4mmeQHxuKiYioFyRNbrZs2YK5c+ciOTkZKpUK69atO+P1a9euxUUXXYT4+HhERkZi2rRp+PLLL/0TbJCqbGyF3SlAq1bBFGnwy3OmxLiSqNrmNjRZ7X55TiIiChySJjcWiwUZGRlYsWJFt67fsmULLrroInz++efIzs7G7NmzMXfuXOzevdvHkQavUveOpUSjARq1yi/PGWkIgTE0BACbiomIqOe0Uj75pZdeiksvvbTb17/44osd/v3UU0/hk08+wf/+9z9MmDDBy9ER0D5Ir3+Uf5akRKkxoagvbUNxTQtGJEb69bmJiEjZJE1u+srpdKKxsRExMTFdXmO1WmG1Wj3/bmho8EdoAaPEXbnp76d+G1FqdBj2lzaw74aIiHpM0Q3Fzz33HJqamvCrX/2qy2uWLVsGo9Ho+UpNTfVjhMonJjcpfq/ciKeDM7khIqKeUWxy88477+DRRx/F+++/j4SEhC6vW7JkCerr6z1fxcXFfoxS+TzLUn6u3Hi2g3PWDRER9ZAil6Xee+893Hrrrfjggw+QmZl5xmv1ej30er2fIgs8pe7KSf8o/8y4EYnJTXk9kxsiIuoZxVVu3n33XSxatAjvvvsuLr/8cqnDCWiCIEhWuUl2L4OV8WRwIiLqIUkrN01NTTh27Jjn3/n5+cjJyUFMTAwGDBiAJUuWoLS0FG+99RYA11LUwoUL8fe//x1Tp06F2WwGAISGhsJoNEryMwSyGosNrW1OAECS0T8zbkRiclPb3IZmmx1hOkUWGYmISAKSVm527tyJCRMmeLZxZ2VlYcKECVi6dCkAoLy8HEVFRZ7r//3vf8Nut+Puu+9GUlKS5+uee+6RJP5AJ1Zt4iP0MIRo/PrckYYQROhdCQ2rN0RE1BOS/jk8a9YsCILQ5fdXr17d4d+bN2/2bUDUgTjAz1/HLpwqOSoURyoaUVrXiqEJEZLEQEREyqO4nhvyH6kG+ImSo1xLYazcEBFRTzC5oS5JNcBPxKZiIiLqDSY31CWxcuPvAX4iMbkpZXJDREQ9wOSGulQqceWmPys3RETUC0xuqEvtPTf+HeAnEpOqsrpWSZ6fiIiUickNdarJakd9SxsA6Xtuyutb4HR2vauOiIjoZExuqFPiUlCkQYtwvTQTA0wReqhVQJtDQHWT9ex3ICIiApMb6oKY3CRL1EwMAFqNGomRru3gbComIqLuYnJDnSqvd/W5+PvYhVNxxxQREfUUkxvqVLk7mUiSsHIDcNYNERH1HJMb6lSZu3KTLJPKDXdMERFRdzG5oU6V17srN0ZpKzf9o9hzQ0REPcPkhjpV7q6UJEVJW7lpn3XD5IaIiLqHyQ2dRhAElNVLe2imiA3FRETUU0xu6DR1zW1obXMCABIl7rkRl8XqmtvQYnNIGgsRESkDkxs6jVi1iQvXQa/VSBpLpEGLMJ0rBnMDm4qJiOjsmNzQacSdSVI3EwOASqXyVI/EJmciIqIzYXJDp2nfKSXtkpRIjMNcz8oNERGdHZMbOo1YuZHy6IWTJUaKB2gyuSEiorNjckOnYeWGiIiUjMkNnaZ9xo1MKjeenhsmN0REdHZMbug04m4pqY9eECW7BwmaG9hQTEREZ8fkhjpwOgVUNMiscuPuueGyFBERdQeTG+qgusmKNocAtQowReilDgdAe89NdZMNVjsH+RER0ZkxuaEOxNPAEyIM0Grk8faICguBXuuKpaLeKnE0REQkd/L49CLZKHef4ST1gZknU6lUnuoNB/kREdHZMLmhDsQdSXLZBi4Sd0zxCAYiIjobJjfUgaeZWAZHL5xMjIfbwYmI6GyY3FAHYvKQGCnTyg2TGyIiOgsmN9SBmDwkymxZij03RETUXUxuqANzg0x7biJZuSEiou5hckMegiB4kgeTzJal2HNDRETdxeSGPGosNtgcTgAyTG7cW9Ormqxoc8dIRETUGSY35CEuScWF66HTyuutEROmg06jhiAAlY0c5EdERF2T1ycYSaq9mVgexy6cTK1WweSOy8ymYiIiOgMmN+TRvg1cXjNuREmR7LshIqKzkzS52bJlC+bOnYvk5GSoVCqsW7furPfZvHkzJk6cCL1ej6FDh2L16tU+jzNYVMh0p5SIs26IiKg7JE1uLBYLMjIysGLFim5dn5+fj8svvxyzZ89GTk4O7r33Xtx666348ssvfRxpcCiX6YwbkZh0ldUxuSEioq5ppXzySy+9FJdeemm3r1+5ciUGDRqE559/HgAwcuRI/PDDD3jhhRcwZ84cX4UZNMwynU4saj9fij03RETUNUX13Gzbtg2ZmZkdbpszZw62bdvW5X2sVisaGho6fFHn5DrAT9Q+pZiVGyIi6pqikhuz2QyTydThNpPJhIaGBrS0dP7X/LJly2A0Gj1fqamp/ghVkTwD/GSa3CS6B/mx54aIiM5EUclNbyxZsgT19fWer+LiYqlDkqXG1jY0We0A5LssJVZuKhutsHOQHxERdUHSnpueSkxMREVFRYfbKioqEBkZidDQzrcv6/V66PXym9siN+JOqUiDFv308nxbxIXroVWrYHcKqG6yybbxmYiIpKWoys20adOwadOmDrdt3LgR06ZNkyiiwCH3nVIAoFGrPMdC8HRwIiLqiqTJTVNTE3JycpCTkwPAtdU7JycHRUVFAFxLSgsWLPBc/9vf/hZ5eXn485//jMOHD+Pll1/G+++/jz/84Q9ShB9Q2pMbeQ7wE3HWDRERnY2kyc3OnTsxYcIETJgwAQCQlZWFCRMmYOnSpQCA8vJyT6IDAIMGDcL69euxceNGZGRk4Pnnn8drr73GbeBeUOFOFpJk2m8jSuSOKSIiOgtJmytmzZoFQRC6/H5n04dnzZqF3bt3+zCq4FTeIO+dUiIx+RK3rRMREZ1KUT035Dueyo3MkxtWboiI6GyY3BCAkw/NlHdyk+SZdcOGYiIi6hyTGwLQvswj591SACs3RER0dkxuCK1tDtRYbADkvywlxlfR0Aqns+t+LSIiCl5MbgiVDVYAgF6rhjE0ROJoziw+Qg+1CmhzCKi2WKUOh4iIZIjJDXkG4iUZDVCpVBJHc2YhGjXiI1wTpznrhoiIOsPkhhTTbyMSBw2y74aIiDrD5IY8FRC575QSJXNKMRERnQGTG1LM0Qsi8XypMm4HJyKiTsjz+GfyK/FEcLnvlBJ5dkyxckMnyS6sxXdHKiEAMIaG4FeTUxFpkHeDPBH5BpMb8lRuTApZlvIcnskjGMjt490luO+DvXCcNB5g7a5SvHXLFMSF6yWMjIikwGUp8vSuKKVyI/YGseeGAODtnwqR9f4eOJwCLhgej4XTBiIuXI+D5Q341b+2eXYDElHwYHIT5OwOJ6qaXPNilJLceI5gaGg948GrFPh2F9XioXX7IQjAwmkDsfrmyXj0qjF4/45zkWw0IK/Kgt+/u5vvE6Igw+QmyFU32eBwCtCoVYhVSPk+IdIVZ2ubE/UtbRJHQ1IRBAF/++IwAOCq8cn465WjoVa75jQNjg/HmjumQa9VY0dBLTYdqpQyVCLyMyY3QU4s2Zsi9NCo5T3AT2QI0SCmnw4AZ90Es825Vfg5vwY6rRp/vmTEaQMoU2PCsOi8QQCAZ7483KEfh4gCG5ObIFehsAF+Ik/fDZuKg5LTKeBpd9Vm4bSB6B/V+RiDO2cOQaRBi9yKJny8u9SfIRKRhJjcBLn2GTcKS244yC+ofbHfjMPmRkTotbhr1tAurzOGheDu2a7vv7Axl9UboiDB5CbItU8nVsYAPxGTm+D2/s5iAMDN56Uh2r1E2ZWF09MQFRaC0roWbD1e7Y/wiEhiTG6CnFlhA/xE3A4evCobWvH90SoAwDUTU856vSFEg7njkgG4Zt8QUeBjchPkPAP8lJbcuOMtZ89N0PkkpwxOAZg0MBppcf26dZ+rJ/YHAGzYb0aT1e7L8IhIBpjcBDmlDfAT8QiG4PXRrhIA7QlLd4xPjcKguH5oaXNgw36zr0IjIplgchPEBEHwLEsp5URwkRgvp88Gl4NlDThsboROo8YVY5O7fT+VSoWrJ7iSoY93l/gqPCKSiV4lN3l5ed6OgyRQ29wGm90JQDnnSonEZamGVjuabVxmCBZr3VWbC0cmwBjWs0Mx57mTm63HT6CsjkkxUSDrVXIzdOhQzJ49G2+//TZaW7ksoFRi1SMuXAedVllFvAhDCPrpNADYVBxMNh6qAOCaSNxTqTFhOGdgNAQB2HSYE4uJAlmvPtF27dqFcePGISsrC4mJibjjjjuwfft2b8dGPqbUAX4ibgcPLgXVFhSeaEaIRoXzh8X36jFmj0gAAHx3pMqboRGRzPQquRk/fjz+/ve/o6ysDKtWrUJ5eTnOP/98jBkzBsuXL0dVFX9xKIFngJ/ClqREJx+gSYFv8xFXteWcgTEI12t79Rgzh7uSoq3Hq2G1O7wWGxHJS5/WIrRaLa6++mp88MEHePrpp3Hs2DHcd999SE1NxYIFC1BeXu6tOMkHzAqdTiwyeZqKmdwEg825rj+aZqb3rmoDAKOTIxEfoUezzYGdBbXeCo2IZKZPyc3OnTtx1113ISkpCcuXL8d9992H48ePY+PGjSgrK8NVV13lrTjJB9q3gStrOrHIsx2clZuA19rmwLbjJwAAs/qQ3KhUKk/1RqwEEVHg6VVys3z5cowdOxbTp09HWVkZ3nrrLRQWFuKJJ57AoEGDMGPGDKxevRq7du3ydrzkReJyjtJ2SonEwYOs3AS+n/NrYLU7kRhpQLopok+PJSY33+Vy+ZwoUPVq4fqVV17Bb37zG9x8881ISkrq9JqEhAS8/vrrfQqOfEupA/xESTyCIWiIVZZZ6fFQqVR9eqwZw+KgVgG5FU0oq2tBchcnihORcvWqcrNx40bcf//9pyU2giCgqKgIAKDT6bBw4cK+R0g+o/SeG89uKS5LBTyxytKXJSlRVJgOEwZEAwA2c9cUUUDqVXIzZMgQVFeffrpuTU0NBg0a1OegyPearHY0us/YUepuKTG5qW6yos3hlDga8pXKhlbkVVmgUgHThsR55TFnDHM9zk95J7zyeEQkL71KbgRB6PT2pqYmGAzK/KAMNmLVJsKgRb9ebquVWkyYDjqNGoIAVDZapQ6HfGR7QQ0AYGRiJIyhPZtK3JUpaTEAgB0FNV3+PiMi5erRp1pWVhYA146DpUuXIiwszPM9h8OBn3/+GePHj/dqgOQbZoXPuAEAtVqFhEg9SmpbYK5vQX/2TgSkHfmu5GbKoBivPeaEAdHQqlUor29FSW0LUmPCzn4nIlKMHiU3u3fvBuCq3Ozbtw86nc7zPZ1Oh4yMDNx3333ejZB8Qjx6Qan9NqIkowEltS3cMRXAtrvn0UxO815yE6rTYEx/I3KK67CjoIbJDVGA6VFy8+233wIAFi1ahL///e+IjIz0SVDke+JsGKXulBIlGkMB1HLHVICqb2nDYXMDAGDyoGivPvaUQTGe5ObqiSlefWwiklavem7eeOMNJjYKp/SjF0SJkXoA3A4eqHYV1kIQgLTYMCREePe9KlaCtruXvYgocHS7cnP11Vdj9erViIyMxNVXX33Ga9euXdvtAFasWIFnn30WZrMZGRkZeOmllzBlypQur3/xxRfxyiuvoKioCHFxcbj22muxbNkyNjL3UPs2cGX3qYjxl3M7eEASm4m9uSQlOmegqxJ0vMqCE01WxIbrvf4cRCSNbic3RqPRMzzLaDR65cnXrFmDrKwsrFy5ElOnTsWLL76IOXPm4MiRI0hISDjt+nfeeQcPPPAAVq1ahenTpyM3Nxc333wzVCoVli9f7pWYgoU5UJal3JWnClZuApLYTOyL5Ca6nw7DEsJxtLIJOwpqccmYRK8/BxFJo9vJzRtvvNHp/+6L5cuX47bbbsOiRYsAACtXrsT69euxatUqPPDAA6ddv3XrVpx33nm4/vrrAQBpaWm47rrr8PPPP3slnmAiVm6UevSCKJFHMASs1jYH9pbUAwAme3Gn1MkmD4pxJzc1TG6IAkivem5aWlrQ3Nzs+XdhYSFefPFFfPXVV91+DJvNhuzsbGRmZrYHo1YjMzMT27Zt6/Q+06dPR3Z2NrZv3w4AyMvLw+eff47LLrusy+exWq1oaGjo8BXsrHYHTlhsAJRfuRHjr2xshdPJeSWBZF9pPWwOJ+LC9UiL9c1uJnHezc4C9t0QBZJeJTdXXXUV3nrrLQBAXV0dpkyZgueffx5XXXUVXnnllW49RnV1NRwOB0wmU4fbTSYTzGZzp/e5/vrr8dhjj+H8889HSEgIhgwZglmzZuHBBx/s8nmWLVsGo9Ho+UpNTe3mTxm4KhtcA+/0WjWiwrwzFE0q8RF6qFRAm0PwJGwUGPYU1wEAJgyI6vN5Ul2ZMCAKAHCovBE2O6dcEwWKXiU3u3btwowZMwAAH374IRITE1FYWIi33noL//jHP7wa4Mk2b96Mp556Ci+//DJ27dqFtWvXYv369Xj88ce7vM+SJUtQX1/v+SouLvZZfEpRVtc+48ZXHxr+EqJRIz6cO6YC0R73ktT41CifPceAmDBEhYXA5nB6tpwTkfL1au5+c3MzIiIiAABfffUVrr76aqjVapx77rkoLCzs1mPExcVBo9GgoqKiw+0VFRVITOx87fvhhx/GTTfdhFtvvRUAMHbsWFgsFtx+++34y1/+ArX69FxNr9dDr+cuiJOVK/w08FMlGg2obLTC3NCKsfBOsztJT6zcjEvx3X9TlUqFcSlR2JJbhT3FdRiXEuWz5yIi/+lV5Wbo0KFYt24diouL8eWXX+Liiy8GAFRWVnZ7/o1Op8OkSZOwadMmz21OpxObNm3CtGnTOr1Pc3PzaQmMRqMB0PV5V3Q6MblJVvg2cJG4Y8rsnrpMyldrsaGoxtXXN65/lE+fa7w7eRIrRUSkfL1KbpYuXYr77rsPaWlpmDp1qicZ+eqrrzBhwoRuP05WVhZeffVVvPnmmzh06BDuvPNOWCwWz+6pBQsWYMmSJZ7r586di1deeQXvvfce8vPzsXHjRjz88MOYO3euJ8mhsxOPXkiKCozKjViBMnPWTcDYU1IHABgU1w9GH/eFidUasVJERMrXq2Wpa6+9Fueffz7Ky8uRkZHhuf3CCy/EL3/5y24/zvz581FVVYWlS5fCbDZj/Pjx2LBhg6fJuKioqEOl5qGHHoJKpcJDDz2E0tJSxMfHY+7cuXjyySd782MErbI6cVkqMCo3Jm4HDzjiFvAMHy5Jicalup7jWFUTmqx2hOt79WuRiGSk1/8vTkxMPK035kyThbuyePFiLF68uNPvbd68ucO/tVotHnnkETzyyCM9fh5q56ncBEjPjadyw+QmYLT320T5/LkSIgxINhpQVt+KfSX1mDYk1ufPSUS+1avkxmKx4G9/+xs2bdqEyspKOJ0dt1Dm5eV5JTjyDXN9gFVuIrksFUgEQfD0v2T4cKfUyTJSo1BWb8bekjomN0QBoFfJza233orvvvsON910E5KSkhS/nTiYtLa1D/BLDpieG1eSZq5vhSAIfD8qXFl9K6qbrNCqVRid7J8DejNSo/DFfrOn14eIlK1Xyc0XX3yB9evX47zzzvN2PORjYtUmNEQDY6iyB/iJxN1SzTYHGlrtAfNzBStxSSo9MQKGEP9sFBC3m+8p5o4pokDQq91S0dHRiInxzVkv5FtlJ/XbBEqFI1SnQbR7R005t4MrnthM7M+ZM2P7G6FSAaV1LTjRZPXb8xKRb/QquXn88cexdOnSDudLkTJ4+m0CZElKJC5NidOXSbkOlLmSmzH9/bMkBQARhhCkxfZzPz8nFRMpXa+WpZ5//nkcP34cJpMJaWlpCAnpuAywa9curwRH3lceYM3EouQoAw6WN3i2uZMyCYKAg+7kYnSyf6dNj0qORH61BQfKGnDB8Hi/PjcReVevkpt58+Z5OQzyF7GykRwg28BFyVGuZI3LUspW0WDFCYsNahUwIjHCr889OjkS6/eWeypHRKRcvUpuOGdGucTKTWKAVW7ESlQ5KzeKJiYWQ+LD/dZMLBqV5FoGO8hlKSLF61XPDQDU1dXhtddew5IlS1BTUwPAtRxVWlrqteDI+8oDtOdG3NZeyp4bRWtfkvJfv41IXAbLP2GBxWr3+/MTkff0qnKzd+9eZGZmwmg0oqCgALfddhtiYmKwdu1aFBUV4a233vJ2nOQl4rJNoByaKfJUbjilWNEOSNRvAwDxEXokROhR2WjFYXMDJg3kjlAipepV5SYrKws333wzjh49CoOhvQJw2WWXYcuWLV4LjryrxeZAXXMbACAx4Hpu2o9gcDp5QrxSHSh3LUtJUbk5+Xm5Y4pI2XqV3OzYsQN33HHHabf3798fZrO5z0GRb4gzbvrpNIg0BNbhgKZIA1QqwOZweiYwk7LUt7ShuMb1Hh0lWXLjqhgdKGVyQ6RkvUpu9Ho9GhpO/z9/bm4u4uO5hVKu2mfchAbMAD9RiEaNhAg9AM66USqx36Z/VCiiwnSSxOCp3JRzxxSRkvUqubnyyivx2GOPoa3NtcShUqlQVFSE+++/H9dcc41XAyTvET/0A+U08FO1990wuVEicaeUVEtSrud2VW5yzU1oczjPcjURyVWvkpvnn38eTU1NiI+PR0tLC2bOnImhQ4ciIiICTz75pLdjJC9pH+AXmMlN/yhxSjGbipVIquF9J0uNCUWEQQubw4mjFU2SxUFEfdOrxguj0YiNGzfixx9/xJ49e9DU1ISJEyciMzPT2/GRF5V7zpUKrJ1SIjFpY+VGmQ6Wu5IbqfptAFcVemRSJLbn1+BQeYOksRBR7/U4uXE6nVi9ejXWrl2LgoICqFQqDBo0CImJiRAEIeB6OQKJWLlJDrAZN6IksXLD7eCK0+Zw4niVq1Li78nEpxqZGIHt+TU4UtEoaRxE1Hs9WpYSBAFXXnklbr31VpSWlmLs2LEYPXo0CgsLcfPNN+OXv/ylr+IkLxCn9wZq5UY8UoINxcpTUG1Bm0NAP53Gs7wolfREV7XmUDl3TBEpVY8qN6tXr8aWLVuwadMmzJ49u8P3vvnmG8ybNw9vvfUWFixY4NUgyTvK6gO7odhzvhR7bhTnsNlVJRmeGAG1Wtrqb7q7cnTEzMoNkVL1qHLz7rvv4sEHHzwtsQGAX/ziF3jggQfw3//+12vBkfc0We1obHWNlE+S+C9jXxGPlKhsbIWdO10URUwk0k3SLkkB7clNZaMVtZyZRKRIPUpu9u7di0suuaTL71966aXYs2dPn4Mi7zO7qzYRBi3C9YE1wE8U10+PEI0KTgGoaLRKHQ71gNjfki5xvw0AhOu1SI1x/QFwmNUbIkXqUXJTU1MDk8nU5fdNJhNqa2v7HBR5n7g9OtDOlDqZWq3yHCvBvhtl8VRuZJDcAEC6ydV3c9jMvhsiJepRcuNwOKDVdv1Xv0ajgd3O03TlSNweHWhnSp1KTN6Y3CiHxWpHUU0zAHksSwHtO7bYd0OkTD1anxAEATfffDP0en2n37dauRQgV57KTYBuAxd5moq5HVwxjla6toDHhesRG9757xZ/G5HkSm64LEWkTD1KbhYuXHjWa7hTSp4850oF8LIUcNIgP1ZuFOOIe+lH6vk2JxNjya1ohNMpSL6Di4h6pkfJzRtvvOGrOMjHyoJkWUrcCVbK7eCK4dkGLpMlKQBIi+0HnVaNZpsDxbXNGBjbT+qQiKgHenW2FCmPZzpxgFdu+kfxCAalyXXvlJJT5UarUWNofDgALk0RKRGTmyAgCIJnmSYpwHtu2k8GZ+VGKeS2U0ok9t2wqZhIeZjcBIFGqx0WmwNA4E4nFomVqRqLDa1tDomjobOpbrKiuskGlQoYZgqXOpwOuGOKSLmY3AQB8TgCY2gIwnSBOcBPFBmqRZhOA4DbwZUg1504DIgJk91703PGFGfdECkOk5sgEOhnSp1MpVJxO7iCHJbRsQunEis3BdUWVgGJFIbJTRAo98y4CexmYlESpxQrhlz7bQAgIUKP6LAQOAXgmHsWDxEpA5ObIGAOosoN0N53w8qN/MnpTKlTqVQqT1zcMUWkLExugkCZZ4BfcCQ34o4wVm7kzekUZLkN/GQj3H03R9h3Q6QoTG6CgPghH+jTiUXi8lsZKzeyVlLbgmabAzqNWrZD8kawckOkSExugkCpO7npHx0kyY24LMXKjayJS1JDEsIRopHnryIuSxEpk+S/UVasWIG0tDQYDAZMnToV27dvP+P1dXV1uPvuu5GUlAS9Xo/hw4fj888/91O0yuN0Cp6G4v7B0lDsmVLMyo2cyfFMqVOJR0JUNVpRY7FJHA0RdZekyc2aNWuQlZWFRx55BLt27UJGRgbmzJmDysrKTq+32Wy46KKLUFBQgA8//BBHjhzBq6++iv79+/s5cuWoarLC5nBCrQr8c6VEYuWmyWpHQ2ubxNFQV+R4ptSp+um1GBATBgA4zL4bIsWQNLlZvnw5brvtNixatAijRo3CypUrERYWhlWrVnV6/apVq1BTU4N169bhvPPOQ1paGmbOnImMjAw/R64cJbXuAzMjDbIt/XtbqE6D6LAQAGwqljO5NxOLPH035VyaIlIKyT7tbDYbsrOzkZmZ2R6MWo3MzExs27at0/t8+umnmDZtGu6++26YTCaMGTMGTz31FByOrgdsWa1WNDQ0dPgKJsHWbyPynDHF08FlyWZ3Iq/KAkCe28BPxmMYiJRHsuSmuroaDocDJpOpw+0mkwlms7nT++Tl5eHDDz+Ew+HA559/jocffhjPP/88nnjiiS6fZ9myZTAajZ6v1NRUr/4cclfqrtwES7+NKFncDs7TwWXpeFUT7E4BEQat7EcUiMcwHK5gckOkFIpap3A6nUhISMC///1vTJo0CfPnz8df/vIXrFy5ssv7LFmyBPX19Z6v4uJiP0YsvdK6ZgDBW7nhspQ8iUtS6aYIqFQqiaM5M7GydLSiEU6nIHE0RNQdkp1UFxcXB41Gg4qKig63V1RUIDExsdP7JCUlISQkBBqNxnPbyJEjYTabYbPZoNPpTruPXq+HXq/3bvAK0l65CZM4Ev8Sk7kyLkvJ0mEZH7twqrTYMOi0ajTbHCipbcGA2OD6/xKREklWudHpdJg0aRI2bdrkuc3pdGLTpk2YNm1ap/c577zzcOzYMTidTs9tubm5SEpK6jSxoeDtuUlx/7wltc0SR0KdkfOZUqfSatQYGh8OgDumiJRC0mWprKwsvPrqq3jzzTdx6NAh3HnnnbBYLFi0aBEAYMGCBViyZInn+jvvvBM1NTW45557kJubi/Xr1+Opp57C3XffLdWPIGuCIARtz01KtOuva3G3GMnLERmfBt4Zsak4l303RIog2bIUAMyfPx9VVVVYunQpzGYzxo8fjw0bNniajIuKiqBWt+dfqamp+PLLL/GHP/wB48aNQ//+/XHPPffg/vvvl+pHkLX6ljZYbK6dZMGW3Ig/r7mhFTa7EzqtotrLAlpja5unoqiEyg0ADOekYiJFkTS5AYDFixdj8eLFnX5v8+bNp902bdo0/PTTTz6OKjCIVYvYfjqE6jRnuTqwxIXroNeqYbU7Ya5vZZ+EjIjVD1OkHlFhylhOTmflhkhR+OdsAAvWfhsAUKlU7LuRqSPmJgDtW6yVQFw+y6uywGZ3nuVqIpIak5sAFqz9NiL23ciTEs6UOlWS0YAIgxZ2p4C86iapwyGis2ByE8A8lZugTW5YuZEj8TRwOZ8pdSqVSuWp3nBSMZH8MbkJYJ7KTRAuSwHtPzcrN/IhCIInOVBS5QZo77thUzGR/DG5CWCs3LiXpTilWDaqGq2obW6DWgUMTQiXOpwe8TQVM7khkj0mNwGsLIgbioH2ZalSVm5kQ1ySSovtB0OIsnbwictSrNwQyR+TmwBlsdpxwmID0F7BCDZiclNe34I2B3e4yIGSJhOfSoy5tK4Fja1tEkdDRGfC5CZAiX0mkQYtjKEhEkcjjbh+eui0ajgFwFzPM6bkQElnSp0qKkwHU6TrnLrcCu6YIpIzJjcBqrjGtUMomIfXqdUqpLj7jYq5Y0oWTj4NXInE2Twc5kckb0xuAlSRO7lJDdIlKRF3TMmHwym0JzcKrNwAQLrJ1QTN7eBE8sbkJkCJlYrUmOBObsR+IzYVS6+ophmtbU7otWoMjO0ndTi9IlZumNwQyRuTmwBVXOP6ME8N0p1SohRWbmRDTAiGmcKhUaskjqZ3PIP8KhohCILE0RBRV5jcBKgSVm4AcEqxnHh2SpmUc6bUqYaZwqFSATUWG6qbbFKHQ0RdYHITgARB8DQUM7lx/fzi60HSOVKhvDOlTmUI0SDNvaTGpSki+WJyE4BqLDZYbA4AwTudWDTAndyVN7TyNGeJicnAcAUnN0DHpSkikicmNwGo2N1fYorUK24KrLfFhesQptNAELg0JaXWNgcKTrhefyVXboD25Ew83ZyI5IfJTQAq5jZwD5VK5aneFHFpSjLHKpvgcAqICgtBQoRe6nD6ZEQiTwcnkjsmNwFI3AY+IMj7bURi3xH7bqRz8vA+lUqZO6VEw93LUrkVTXA6uWOKSI6Y3AQg8UM8hckNgPYkr/AEkxupKPlMqVOlxYZBp1Wjpc3ByddEMsXkJgBxxk1HA2O5LCU1JZ8pdSqtRo2h8ZxUTCRnTG4CEKcTd5TKnhvJtc+4UX5yA7DvhkjumNwEGIdT8Bw1wOTG5eSGYk6V9b9aiw3mBtep7COSlDvA72SeHVPcDk4kS0xuAkx5fQvsTgEhGhUSIw1ShyMLKdGhUKmAZpsDJyycKutvh9xbpgfEhCFcr5U4Gu9IZ+WGSNaY3AQYsd+mf1SoYs/v8Ta9VoMkd6LHpSn/O1zuSgCUPt/mZOLyWn61BVa7Q+JoiOhUTG4CTOEJCwAo9tRlX/H03XDHlN8dKncfuxAgS1IAkGQ0IMKghd0pIK/KInU4RHQKJjcBRpwCmxbLfpuTcceUdMSdUqOSAqdyo1KpPNWbXPbdEMkOk5sAw8pN5zilWBp2h9PTdDsiMXAqN0B7381h9t0QyQ6TmwCTX+1KbtLiWLk5GZelpFFwwgKb3YkwnSbgJmZzOziRfDG5CSCCIHim8KaxctOBWMli5ca/DpW3D+9TB1iDu3gMA5MbIvlhchNAqhqtaGlzQK0CUnhoZgdi1cDc0IrWNu5u8RdPM3GALUkB7ctSpXUtaGxtkzgaIjoZk5sAIjYT948OhU7L/7Qniw4LQYR7xgqrN/4TiM3EoqgwHUyRrhPOcyuaJI6GiE7GT8AAUuBuJuaS1OlUKhUGxbteF7EviXwvELeBnyzdXZHi0hSRvDC5CSDtO6W4JNUZMeljcuMfdc02lNe7jl0IhAMzO5NuEg/QbJA4EiI6GZObAFLAZuIzGhTnel0KmNz4hbgklRIdikhDiMTR+IbYS3SIlRsiWWFyE0AKuSx1RmJyk8fkxi8CuZlYNNK93HaovIGHshLJCJObACEIAgqr3ZUbzrjpFCs3/iWeKRWIzcSioQnhCNGo0NhqR0lti9ThEJGbLJKbFStWIC0tDQaDAVOnTsX27du7db/33nsPKpUK8+bN822ACnDCYkOj1Q4Vt4F3Kc2d3FQ2WtFktUscTeATTwMP1GZiANBp1Ria4ErexEoVEUlP8uRmzZo1yMrKwiOPPIJdu3YhIyMDc+bMQWVl5RnvV1BQgPvuuw8zZszwU6TyJi5JJRtDYQjRSByNPBlDQxDbTweA1RtfczgFzw6ikQGc3ADAyCQxuWHfDZFcSJ7cLF++HLfddhsWLVqEUaNGYeXKlQgLC8OqVau6vI/D4cANN9yARx99FIMHD/ZjtPJV4F6S4k6pMxOrN9wx5VsFJyyw2p0IDQm8YxdONcqdvB0sr5c4EiISSZrc2Gw2ZGdnIzMz03ObWq1GZmYmtm3b1uX9HnvsMSQkJOCWW27xR5iKUMADM7uFfTf+IS7RpCdGQBNgxy6capSnqZiVGyK50Er55NXV1XA4HDCZTB1uN5lMOHz4cKf3+eGHH/D6668jJyenW89htVphtVo9/25oCMx18bwq14f1kHgmN2cyiJUbvxCbiUcGcDOxSFx2K6ppRmNrGyICdNs7kZJIvizVE42Njbjpppvw6quvIi4urlv3WbZsGYxGo+crNTXVx1FK43iVa/z7kIRwiSORN09yc4LJjS8FwzZwUXQ/HZKMBgDts32ISFqSVm7i4uKg0WhQUVHR4faKigokJiaedv3x48dRUFCAuXPnem5zOp0AAK1WiyNHjmDIkCEd7rNkyRJkZWV5/t3Q0BBwCY7DKXhmtwyNZ3JzJpxS7B+Hg6SZWDQyKRLl9a04WNaAyWkxUodDFPQkrdzodDpMmjQJmzZt8tzmdDqxadMmTJs27bTrR4wYgX379iEnJ8fzdeWVV2L27NnIycnpNGnR6/WIjIzs8BVoSmtbYLM7odOqkRwVKnU4sibOAKprbkOtxSZxNIGpvqUNpXWumS+BeuzCqUadNMyPiKQnaeUGALKysrBw4UKcc845mDJlCl588UVYLBYsWrQIALBgwQL0798fy5Ytg8FgwJgxYzrcPyoqCgBOuz2YiEtSg+P6BXzzZl+F6bRIjDTA3NCK/BMWRLu3hpP3HHZ/wPePCoUxNDj6T0YyuSGSFcmTm/nz56OqqgpLly6F2WzG+PHjsWHDBk+TcVFREdRqRbUG+Z2n34ZLUt0yKK6fK7mpsmDigGipwwk4B90f8MHQTCwaldx+xlSbw4kQDX9nEUlJ8uQGABYvXozFixd3+r3Nmzef8b6rV6/2fkAK057ccKdUdwxNCMe2vBM45n7dyLv2l7qSmzH9jRJH4j8DY8IQrteiyWrH8aqmoGikJpIz/nkRAI5XureBc6dUtwwzuV6noxVMbnzhQJlrmN2Y5OBJbtRqFUa7qzdickdE0mFyEwC4LNUz4o6yY5XctuttrW0OHK10vR+DqXIDtP+8+0s5qZhIakxuFK7WYsMJ964fcYYLndlQd+WmqKYZrW0OiaMJLIfKG+BwCogL18EUqZc6HL8a01+s3DC5IZIakxuFy6t2/ZWcZDSgn14WLVSyFx+uhzE0BE6B8268bX9Ze7+NShVcO/fEZbiD7gSPiKTD5EbhPP02XJLqNpVKhaHu/iRxCYW840Bp8PXbiAbHhyM0RINmm4NJM5HEmNwo3PFq7pTqjWHu5OZYBftuvGm/2EzcP/h2C2nUKs+WcLGpmoikweRG4bhTqnfEyg23g3uPze7EEfexC6ODsHIDAGOS2XdDJAdMbhTuqHvHD5elesazLMXt4F6TW9GINoeAqLAQpEQH5zEgo907pvYxuSGSFJMbBWu22VFU0wwgeM7w8ZZhJtfrlV9tQZvDKXE0gWH/Sf02wdZMLBrrTm4OlDbAyaZiIskwuVGw3IomCAIQF65DXHhwbbvtq2SjAWE6DexOAYUnmqUOJyCI1QpxmF0wGpoQDp1WjUZr+x8eROR/TG4ULNfd38CqTc+dvGOKw/y8Y2+JK7kZlxIlbSASCtGoPSeE7ympkzYYoiDG5EbBDruTm+EmJje9wb4b72ltc3hOxM5IDc5mYtH41CgAwJ5i9t0EA0EQYLHaIQhchpQTTn1TsFz3NuYRrNz0yrAE1+t2hNvB++xgeQPs7snE/aOCs5lYJCZ3rNwEJkEQsKuoDu/vKMamw5Wobba5p3LrMX1ILOaMTsSlYxKhVgdn35lcMLlRMFZu+kZMCsXXkXpvT3EdACAjJSpom4lFGe5luf2l9WhzOBGiYYE8UJTUNuO+D/bgp7ya075X3WTFp3vK8OmeMoxPjcLSuaMwcUC0BFESwORGsU40WVHdZAXA5Ka3Rrp7I/KqmtDa5oAhRCNxRMrlSW7cSzLBLC22HyINWjS02nHE3Bh0B4gGqk9ySvHQx/vRaLVDr1Xj8nFJuHZSCgbHhSNMr8HBsgZsPlKF/2wrQE5xHa55ZSsevHQkbp0xKOgTfinwTwqFEpdSBsSE8UypXjJF6hEd5jpjin03fbPH3UzM5AZQq1We14FLU4Hh9R/ycc97OWi02jFpYDQ2/mEmlv9qPKYPiUOi0YBIQwjOHRyLBy4dgW/vm4VfTugPQQCe/PwQ/vrpAZ41JgEmNwp1hEtSfaZSqTzVG7EZlnqurtnmOUspI4VVCqB9aSqnqE7SOKjvXvs+D49/dhAAcPsFg7Hm9nMxIDasy+sTIg1Y/qsMPHT5SADAm9sKcf9He9lw7GdMbhSKzcTeISY3B5nc9Jq4BTwtNgxRYTqJo5EHVm4Cw4fZJXhi/SEAwO9+MRRLLh0BbTd6qFQqFW6dMRj/vH4CNGoVPswuwUvfHPN1uHQSJjcK5WkmZnLTJ2JyyMpN77Hf5nTijqmjlU1ostoljoZ6Y09xHR78eB8A4M5ZQ5B10fAe985cMS4Zj101GgCwfGMu1u4q8Xqc1DkmNwokCIJngB8rN31z8rIUy8a9I1YnMoJ4eN+pEiIM6B8VCkEA9pVw3o3SVDdZ8du3s2GzO5E50oQ/XZze66bgG6YOxB0XDAYALFm7D0c5esIvmNwoUOGJZlhsDui0agyK6yd1OIo2zBQOrVqFhlY7yupbpQ5HcQRBQI6ncsN+m5OJr8fu4lqJI6GeEAQB932wB+X1rRgc3w/L52f0eWbN/ZeMwIxhcbDanfj9ezlobXN4KVrqCpMbBdpf5vpLcGRiBGdo9JFeq/GcqH6YS1M9VniiGdVNNug0aoxOZnJzMnHGya5CJjdK8v7OYmw+UgWdVo2VN05CpCGkz4+pVqvw/P9lIKafDofKG/DMhiNeiJTOhJ+MCuQ5oJDzM7xiZBL7bnprp/uDe2yKkXOCTjFpoCu5yS6s5ZKnQpTUNuPxz1wNxPddPNyru1ETIg149tpxAIBVP+Zje/7pgwDJe5jcKNCBUteH8FgmN14xwtN3w7XwnsoudP2CPmcgJ7GeanSyEXqtGrXNbTheZZE6HDoLQRCwZO0+NLln2dxy/mCvP8eFI0349eRUAMCDH++Dze70+nOQC5MbhREEwVO5GcNlAK/gdvDe21ngqtxMYnJzGp1W7dlBJiaBJF+f7zPj+6PV0GnVePbacdD46GyoBy4dgbhwHY5VNuHfW4775DmIyY3ilNS2oL6lDSEaFYYnhksdTkAYk+xKbvKrLWhobZM4GuWoa7bhaKVrsjOTm86JFS0xCSR5sljteGK9a1DfnTOHYHC87363RoXp8PAVowAA//jmGApPsKrnC0xuFGa/u2oz3BQBvZY9Dt4QG673nGS9n9t2u21XkesDe3BcP8SG6yWORp7OSWvvuyH5+ue3x1Be34rUmFDcOWuIz5/vyoxkzBgWB5vdiSfdQwLJu5jcKIy4U4r9Nt413r18kMOJst3GJamzE3dM5VVbcMJ90C3JS0G1Ba99nwcAWHrFaL80xqtUKjwydxQ0ahW+OliBrcerff6cwYbJjcLsdzcT86Rh7xrnPhNpbzErN90l7pQSqxN0uqgwHYYmuJY4dvGcKVl65svDaHMIuGB4PDJHJvjteYcmROCGqQMAAE98doiHa3oZkxsFEQTBsyzF5Ma7xrmn6+5l5aZbbHan59iFSQNjpA1G5jx9N2wqlp3swlp8vs8MtQp48LIRvZ5C3Fv3Zg5HhEGLg+UN+CibRzN4E5MbBTE3tOKExQaNWsVjF7xsbIoRKhVQVt+KqkYuH5zNvtI6WO1ORIeFYEg8p2SfyTlpruSPc03kRRAEPPW5q9/l2kkpGJEY6fcYYvrpcM+FwwAAL3ydy8nFXsTkRkH2uJdMhiWEc2Cal4XrtRjq3iHB6s3ZbTt+AgBw7uBYv/+1qzTnDnYlN3tL6nmIpox8dbAC2YW1MISokXVRumRx3HjuQCQZDSivb8U7PxdJFkegYXKjILvdu1MmsoHTJ8SlKXG5hbq2Lc+V3EwbEitxJPKXEh2G1JhQOJwCdhSweiMHTqeAFzbmAgB+c94gJBoNksViCNF4qjcrvj0GCxNgr2ByoyDidtJJA5jc+MJ490GHe7gd/Iysdodnp9R0JjfdMn1wHID2ihdJ64v9Zhw2NyJCr8XtF3h/EnFPXTMpBWmxYThhsWH11gKpwwkITG4Uwmp3YK+7mZhbb33j5KZingXUtZwiV79NfITec+gonZlY4WJyIz2HU8CLX7urNucPQlSYTuKIgBCNGn+4aDgA4F/fHUd9C4eJ9hWTG4U4UNYAm92J2H46DIwNkzqcgDQiKQI6jessoKKaZqnDka2t7LfpMTG5OVBWzw8uiX22twxHK5sQadDiN+cPkjocj7njkpFuikBDqx2vbsmTOhzFY3KjELsK2/tt+IHiG3qtBmP6u3ZM7OC4/C55+m0Gc0mqu0yRBgyO6wenwF1TUrI7nPj710cBALfNGAxjaIjEEbVTq1XIuthVvVn1Yz6qOfSxT2SR3KxYsQJpaWkwGAyYOnUqtm/f3uW1r776KmbMmIHo6GhER0cjMzPzjNcHCrHfZiL7bXxqyiDXB/YOfgB1qrXNgRz3MDr22/SMWL3hNFrpfJJThrxqC6LCQrBIRlUb0cWjTMhIMaLZ5sArm3moZl9IntysWbMGWVlZeOSRR7Br1y5kZGRgzpw5qKys7PT6zZs347rrrsO3336Lbdu2ITU1FRdffDFKS0v9HLn/CILgmQbLfhvfmjLI9fpu566WTmUX1sLmcCLJaODyaA+x70ZabQ4n/vGNq2pzxwVDEK7XShzR6VQqFf54sWtb+n9+KkR5fYvEESmX5MnN8uXLcdttt2HRokUYNWoUVq5cibCwMKxatarT6//73//irrvuwvjx4zFixAi89tprcDqd2LRpk58j95+S2hZUNVqhVas8xwSQb0waGAOVynVCeGVjq9ThyM6Wo1UAXB/UXB7tmXPdy3iHzY18b0ng412lKDzRjNh+OiyYNlDqcLo0Y1gcpgyKgc3uxMvfsnrTW5ImNzabDdnZ2cjMzPTcplarkZmZiW3btnXrMZqbm9HW1oaYmM5HwFutVjQ0NHT4Uhrx9OXR/Y0c3udjxtAQz6TSHfnsuznVd0dcyc3M4fESR6I8ceF6z4G34utI/mGzt1dtfjtzCPrJsGojUqlUyHLvnHpvRxFK61i96Q1Jk5vq6mo4HA6YTKYOt5tMJpjN5m49xv3334/k5OQOCdLJli1bBqPR6PlKTU3tc9z+JjYgcr6Nf0xxHwTJgWsdlde34LC5EWoVcMEwJje9MSvd9bptzmVy408fZpegpLYF8RF63HiufKs2onMHx2L6kFi0OQSs+PaY1OEokuTLUn3xt7/9De+99x4+/vhjGAydT5hcsmQJ6uvrPV/FxcV+jrLv2rfe8oBCfxCbin9mU3EHYrUhIzUK0f2knw2iRGJy831uFewOp8TRBAer3YF/uqs2d80aglCdMqrf4tyb93cUo5ijKXpM0uQmLi4OGo0GFRUVHW6vqKhAYmLiGe/73HPP4W9/+xu++uorjBs3rsvr9Ho9IiMjO3wpSVldC/KrLVCrgHO5O8UvJrubig+bGziT5CSb3cnNrOEJEkeiXONTo2EMDUFDqx05PObDL9bsKEZZfStMkXpcN2WA1OF02+S0GMwYFge7U8A/v2H1pqckTW50Oh0mTZrUoRlYbA6eNm1al/d75pln8Pjjj2PDhg0455xz/BGqZH485to2Oi4lCpEG+cxkCGQJEQYMiusHQQCyC1m9AVw7TX5wvxfF6gP1nEatwoxhrqMYNrPvxuda2xyeZZ3Fs4cqrmfx3kxX9ebDXSUoPGGROBplkXxZKisrC6+++irefPNNHDp0CHfeeScsFgsWLVoEAFiwYAGWLFniuf7pp5/Gww8/jFWrViEtLQ1msxlmsxlNTU1S/Qg+JS5JnTeUVRt/mjrItQT44zFu2wWAnQW1aLLaEdtP52mKpd6Zle6qfG3O7XzcBXnPOz8XoaLBimSjAb+arLx+y0kDozFzeDwcTgEvsXrTI5InN/Pnz8dzzz2HpUuXYvz48cjJycGGDRs8TcZFRUUoLy/3XP/KK6/AZrPh2muvRVJSkufrueeek+pH8BlBEDyVm/OGxEkcTXC5wL0b6Ds2fgJo/yC+YHg81GpuAe8LcafZ/tIGbgn3oRabAy+7B+H97sJh0GuVVbURib03a3eVIL+a1ZvuksV+uMWLF2Px4sWdfm/z5s0d/l1QUOD7gGTiWGUTKhut0GvVmMjhfX513pA4qFWu/waldS3oHxUqdUiSEQQBGw+6+uJmj2C/TV/FR+gxLsWIvSX12HSoUlF9IEry9k+FqG6yIjUmFNdOSpE6nF4bnxqFC0ckYNPhSvxj01G8MH+81CEpguSVG+qaWLWZnBajuLVipTOGhWCCe+v9liCv3hytbEJelQU6jRqz2W/jFXNGuzZMfLG/eyMvqGcsVjtWfueu2vxiGEI0yv6oE3tvPskpxbHKwGzB8DZl/xcPcD+6+22ms99GEuLyQbAPXPtin+sDeMawOESwqd0rLh3jSm62HqtGfTN35Hnb6q0FOGGxYWBsGK6e0F/qcPpsbIoRF40ywSkA/9h0VOpwFIHJjUy1tjk8lZsZQ/nXshTEvpsfj1WjLYhnknyx39XzdsmYM49noO4bHB+OdFME7E4BXx+qOPsdqNvqmm2eqs29mcOgVXjVRnRv5jAAwP/2liG3olHiaOQvMP6rB6Btx0+g2eZAYqQBY/orazZPoBjb34josBA0WoN3Jkl+tQWHzY3QqlW4aJTp7HegbhOTRTF5JO94ZfNxNLbaMSIxAldlKL9qIxqdbMQloxMhCMDfv2b15myY3MjUV+4GzsxRCTygUCKumSTucflHgnPbrvjBO21ILKLCOJXYmy4d60puthytRpPVLnE0gaG8vgWrtxYAAO6/ZETA7ey79yJX9Wb9vnLsLamTNhiZY3IjQ86TStUXjeJSgJTEgXVfHwzO5GaDu+GVS1Lel26KwKC4frDZnfjmcHC+v7ztxY1HYbU7MWVQTEAOmxyRGIlfunuInvr8EARBkDgi+WJyI0N7SupQ1WhFuF7L86QkduEIE0I0KhypaAy6XQrHq5qwt6QeGrUKFzPJ9jqVSoXL3NWbT3aXShyN8h2rbMIH2a6zA++/ZETAVrz/ePFw6LRq/JRXw6T4DJjcyJA4U2RmerxiB08FCmNYCM4b6hqg+MW+4OqN+Ci7BIBr11h8hF7iaALTLye45q9szq1CVaNV4miU7bkvj8ApABeNMmFSAM8FS4kOw6Lz0gAAf/viMA9g7QKTGxkSk5uL2cApC5eNTQLgWucOFg6ngI/d1YRrJip3AJrcDU0Ix/jUKDicAj7JYfWmt3KK67DhgBlqFfCnOelSh+Nzd80aiuiwEBytbMIH7j9CqCMmNzJzrLIRRyuboFWrPGfQkLQuHmWCVq3CYXMj8qqCY2lq6/FqlNe3whgaggtH8n3oS+L03A+zS9hD0QuCIODpLw4DAK6emILhpgiJI/I9Y2gIfvcLV3Px8o25sLAh/TRMbmTmo12uv95mpSfAGMqBaXIQFabDdPfS1OdBUr0Rl6TmZiRxOraPzR2XDJ1WjcPmRhwoa5A6HMX56mAFtuWdgE6j9syCCQY3njsQA2PDUNVoxavf50kdjuwwuZERh1PAOs9SQODMZwgEl7l3C63fF/jj8htb27DhgOvnvHaS8k5SVhpjWIhnhtCHXGLokdY2B55YfxAAcNsFg5ASHSZxRP6j06rx5zkjAAD/3pLHQ1hPweRGRn7KO4Hy+lZEGrT4BZcCZGXO6ESEaFQ4VN6A/aX1UofjUx9ml6C1zYlhCeHISDFKHU5QEJemPtpVwiWGHnj9h3wU17TAFKnHXbOGSh2O3102NhETBkSh2ebAMxuOSB2OrDC5kZH2pYBk7pKSmeh+OlzsPuzwvR1FEkfjO06ngDfdQ9AWTE8L2O20cjNzWDzSYsPQ2GrH2l2s3nSHub4VK749BgBYculI9NNrJY7I/1QqFR6+YhQA1x8lOwtqJI5IPpjcyITFavecEHw1d6fI0vVTBgAAPtldhmZbYP51/e2RShScaEakQculUT9Sq1VYOD0NAPDG1gI4nWwsPpu/fnoAzTYHJg2MxlXjk6UORzITB0Tj15Ndy8cPrdvPreFuTG5k4rO9ZWhpcyAtNgwTB0RJHQ51YtrgWAyICUOj1Y71ewOzsfiNHwsAAL+eMgBhuuD7S1hK/3dOKiL0WuRVWbDlaHCfRH82Gw9WYMMBM7RqFZ785ZigrzD++ZIRiAoLwWFzo+f4iWDH5EYGBEHA6z/kAwCumzIg6P+PKldqtQq/nuL6C+nd7YG3NJVb0YgfjlVDrQIWTBsodThBJ1yvxf+d43p/iUkmna7JasfST/YDAG6dMRgjEnmwcEw/He6/xNVc/PxXuSg60SxxRNJjciMDPxyrRm5FE8J0GvzavfRB8nTtpBRo1SrsKqrDgbLAaix+ZfNxAMDFoxKDateJnNw8PQ0qFfBdblXAN6731jMbDqO8vhWpMaG458Lg2fp9NvPPScXUQTFoaXPg/o/2Bv3SJpMbGXjte1fV5lfnpHK2jcwlRBg8h0iu/C5wZkscq2zEOveE3LtnB9+uE7kYEBuGKzNc/SMvbMyVOBr5+eFoNd7aVggAeOqXYxGq48YLkVqtwtPXjIMhRI1teSfwTgBWl3uCyY3EjlU24rvcKqhU8JwXQvImbjldv7cM+dUWiaPxjhe+PgpBAOaMNmEst39L6p4Lh0GtAjYdrsTuolqpw5GN+pY2/OnDPQCAm84diBnDAu/U775Ki+uHP7ln3yz7/BAKAuT3U28wuZGY+Nf/RSNNGBjbT+JoqDtGJUfiwhEJcArASvdSjpIdLGvA+r3lUKmAP1w0XOpwgt7g+HDPjsnlrN4AcPUlPvLJfpTXtyItNgxLLhshdUiydfP0NEwZFAOLzYHfv7cbNntw7p5iciOh3IpGz0yL384aInE01BN3uZdu1u4uQVldi8TR9J4gCHj2S9e5PJePTWJzpkzcc+EwaNUqfH+0GluPV0sdjuTe21GMdTll0KhVeP5XGdzJdwYatQovzh8PY2gI9pbU47mvgnO4H5MbCT2z4Qic7qWAiQOipQ6HemDSwGhMGxyLNoeg6N6Irw5W4NsjVQjRqFi1kZHUmDBcP9W1uWDpJweC9q9vADhQVo9HPj0AALjv4nRMGhgjcUTylxwVimeuHQfAdTTD1wcrJI7I/5jcSGRHQQ2+PlQBtQqeNVJSlvvmpAMAPsguQU5xnbTB9EKzzY5H3R8at18wGEPiwyWOiE72x4vSEReuw7HKJs+oiGBTa7Hhrv/ugs3uxIUjEnDHBYOlDkkx5oxOxEL3SId71+TgaEWjxBH5F5MbCTicAp5cfwgAMH9yKoYm8ENFiSYNjMbV7im+f/30gOK2Xv5j0zGU1bciJToUi2dzS63cGMNC8OBlIwEA/9h0FCW1wTW7xGp34I7/ZKPwRDNSokPx/K8yoFZzBlhPPHTFKEwZFIMmqx23vbUT9c1tUofkN0xuJLB6awFyiuvQT6fBPRdyKUDJHrhkBPrpNMgprsNHCjoTKLuwBq9+72pmf/TK0dxSK1O/nNDfM7vkTx/shUNhCXRvCYKABz7ah+0FNYjQa7Hq5smICtNJHZbihGjUeOWGiegfFYqCE8247T870drmkDosv2By42cF1RZPA+eDl49EotEgcUTUFwmRBvzOPUjs8c8OolQBzcV1zTb8/t0cOJwCrsxIxoUjTVKHRF1QqVRYdvVYhOk02JZ3wnNQZCATBFdl++PdpdCoVXj5xokYboqQOizFig3X47WF5yBCr8X2/BosfmcX2oLg/CkmN37kcAr480d70drmxPQhsZ6DGEnZbjl/EDJSo9DQase97+2W9cF1giDg/o/2orSuBQNjw/DkL8dIHRKdxeD4cDwxz/Xf6cWvc7E9P3BPfnbt3juC19w9Rk/9cgzn2XjByKRIvLbwHOi1anx9qBL3fbBH1r+nvIHJjR89s+EwtufXIEynwdPXjOMZUgEiRKPGS7+egHC9FjsKavHSN/L96/rFr4/iywMVCNGo8M/rJiLCwInYSnD1xBRcPbE/nAJw13+zUXgi8IazCYKAZ748gpfds6Meu2o05k/mH4DeMnVwLFZcPxEatQqf5JTh7nd2wWoP3CUqJjd+8mF2Cf61xdXj8LdrxiE1hmf3BJIBJ1VB/r7pKD5xH2UgJ2//VIi/bzoKAHj0yjGcRKwwj181BiOTIlHdZMPCVdtR3WSVOiSvsdmd+OMHezznmz10+UgsmJYmbVABKHOUCa/cMBE6jRpfHqjArW/uRENrYDYZM7nxg63HqvHg2n0AgN/9Yqjn7BgKLFeN74+bp6cBAO77YA+25FZJG9BJ1u0uxcPuk5TvuXCYZ4YKKUc/vRZvLprsaQ5d9MYO1FpsUofVZ9VNVtz8xnas3eXqsXnmmnG4dQa3fPvKxaMT8caiyQjTafD90WrMW/Ej8qqapA7L65jc+Ni3RyqxaPUO2BxOzBltwh8yuTsqkC29YhSuGJeENoeA376dLYsE59Utebh3TQ4EAbhuygDcm8lt30qVEGnAW7dMQUw/HfaV1uOalVsVvUV8R0ENLv/H99h6/ATCdBq8tvAc/GpyqtRhBbzzhsZhze3TkGQ0IK/KgqtW/Ij/7SmTOiyvUgmCEBx7C90aGhpgNBpRX1+PyEjfjpr/dE8Z/vh+DtocAjJHmrDihgnQa7nlNtDZ7E7c8uYOfH+0Ghq1Ck/OG4NfS9A83trmwJPrD+E/P7lOUf7NeYPw0OUjOSskABytaMSCVdtRXt8KU6QeL98wCZMGKmfKebPNjuVf5WLVj/lwCsDQhHCsvHEihiZwV5Q/VTVacefb2dhZ6Dqg9cqMZDx21WjZbrvvyec3kxsfsNodeOKz9g+Vy8cl4cX54xGiYaEsWFjtDtz/4V6sy3H9NXTdlFQ8eNlIvzXwHjY34J53c3DEPZX0wctG4LYZg9nEHkDK61uwcNV25FY0QaNW4d4Lh+Gu2UOhkXHyKggCvjxgxlOfH0ZRjavidPXE/nj8qjHop+d5UVJoczjx0jfHsOLbY3A4BUSHhSDr4nRcP2WA7N5LTG7OwNfJzU95J/DXTw/gsNn1oXLXrCH448XpsnuTkO8JgoAXvj6Kf7ibePtHheKvV45G5sgEnyUZNRYb/rHpKN7+qRB2p4C4cD2e/b9xmJ2e4JPnI2k1trbh4XX7PUn0yKRIPHzFSEwfEidxZB05nQK+O1qFv3991HNUSbLRgCevHsv3pkzsLqrFAx/t8/xBNDQhHItnD8UV45Kglckf5kxuzsBXyU1ZXQue+vwQPttbDgCIDgvB8vnj+X9cwtbj1bj/o70ornEN+MtIMWLxL4Zhdnq8135pFFRb8J+fCvH+jmI0Wu0AgItHmfDU1WMRF673ynOQfK3dVYK/fnoADa2u//az0uNx24zBmD4kVtJqXa3Fhs/2luHNbYU4VulqWg3TaXDrjMG4/YLBCGe1RlbsDif++3MRlm/MRX2LaxdVakwofj15AP7vnBQkREg7dFZxyc2KFSvw7LPPwmw2IyMjAy+99BKmTJnS5fUffPABHn74YRQUFGDYsGF4+umncdlll3XruXyV3Hx1wIzb/5MNtQq4fuoAZF2Ujph+8ly3JP+zWO146ZtjeHNrAVrc48/jI/S4KiMZM9PjMTktBoaQ7vdjOZwCjlU24bvcSnx1oALZRbUQ/588OjkSf7lsJKYPlddf7+RbNRYbXvw6F//9uchzTMOwhHDMm9Afl49NQlpcP7/EUV7fgm8PV+GbwxX4LrcKbQ5XLOF6LeZPTsUdMwdL/iFJZ9bQ2ob/bCvE6z/ko8a9I0+jVuHcwTG4ZHQiZqUnSDLORFHJzZo1a7BgwQKsXLkSU6dOxYsvvogPPvgAR44cQULC6VWPrVu34oILLsCyZctwxRVX4J133sHTTz+NXbt2YcyYs09b9VVyIwgCnt5wBHMzkjA6mfNDqHNVjVa8+n0ePswu8fzSAACdRo2hCeEYbgpHSnQYYsN1iDCEQCzsNLbaUdfchpLaZhScaMbBsgY0uSs0olnp8Vg4PQ0zh8WzaTiIFVRb8MaP+Xh/Z4knkQaAATFhmDY4FuMHRGFUUiQGx/frUw+Y3eFEZaMV+dUWHDY3Yn9pPXYW1ngqlKJRSZG4ZlIK/u+cFERyaKSitNgcWL+vHO9uL0K2u+lYNCAmDOcMjMbIpEj3VwRifVwlVlRyM3XqVEyePBn//Oc/AQBOpxOpqan43e9+hwceeOC06+fPnw+LxYLPPvvMc9u5556L8ePHY+XKlWd9Pn/uliLqis3uxLdHKvHlATO2HjsBc0Nrjx+jn06DiQOjcdEoEy4aZUKSMdQHkZJS1be04cv9Zvxvbxm2Hj/R6aGb4XotEo0GJBkNiI/QIzREA71WA51WDb1WDUEQ0NLmQGubEy1tDlisdpgbWmGub0VFQys6O8dTpQLGp0bhF+kJuHh0ItITuQMqEBSesODLA2Z8daACOcV1sHfyHz8+Qo+BMWFIiQ7FqORI3H7BEK/G0JPPb0kXPG02G7Kzs7FkyRLPbWq1GpmZmdi2bVun99m2bRuysrI63DZnzhysW7eu0+utVius1vZJnvX19QBcLxKRlKalhmFa6mAIwiCU1LbgaGUTjlc1orLBipomGxptds9SU7hegwh9CJKMoUiNCcXQhHAMM0Wc1KjehoaGwJw0Sr2jAnBJuhGXpBvRZLVjV2EtdhTW4HB5I46UN6CmuQ0NVtfvwtzi3j2HVq1CcpQBw0zhGJ4QifEDojA2xXhShUbg79oAER0C/Hp8PH49Pt71fiqqxcHSBhypaMARcyOKa1tQUd2MiupabAdwPMWIX4/37rlg4nupOzUZSZOb6upqOBwOmEwdTyU2mUw4fPhwp/cxm82dXm82mzu9ftmyZXj00UdPuz01lYOiiIj6Kh/Aj1IHQbJTDMB4n28eu7GxEUbjmds/Ar5VfcmSJR0qPU6nEzU1NYiN7f4ugoaGBqSmpqK4uJhLWT3A1633+Nr1Dl+33uNr1zt83XqnN6+bIAhobGxEcvLZjzCSNLmJi4uDRqNBRUVFh9srKiqQmJjY6X0SExN7dL1er4de37HJKSoqqlfxRkZG8s3bC3zdeo+vXe/wdes9vna9w9etd3r6up2tYiOSdDKPTqfDpEmTsGnTJs9tTqcTmzZtwrRp0zq9z7Rp0zpcDwAbN27s8noiIiIKLpIvS2VlZWHhwoU455xzMGXKFLz44ouwWCxYtGgRAGDBggXo378/li1bBgC45557MHPmTDz//PO4/PLL8d5772Hnzp3497//LeWPQURERDIheXIzf/58VFVVYenSpTCbzRg/fjw2bNjgaRouKiqCWt1eYJo+fTreeecdPPTQQ3jwwQcxbNgwrFu3rlszbnpLr9fjkUceOW15i86Mr1vv8bXrHb5uvcfXrnf4uvWOr183yefcEBEREXmTPE7DIiIiIvISJjdEREQUUJjcEBERUUBhckNEREQBhclNF2pqanDDDTcgMjISUVFRuOWWW9DU1HTG+8yaNQsqlarD129/+1s/RSyNFStWIC0tDQaDAVOnTsX27dvPeP0HH3yAESNGwGAwYOzYsfj888/9FKn89OS1W7169WnvLYPB4Mdo5WHLli2YO3cukpOToVKpujxT7mSbN2/GxIkTodfrMXToUKxevdrnccpNT1+3zZs3n/Z+U6lUXR5zE6iWLVuGyZMnIyIiAgkJCZg3bx6OHDly1vvx91zvXjtv/p5jctOFG264AQcOHMDGjRvx2WefYcuWLbj99tvPer/bbrsN5eXlnq9nnnnGD9FKY82aNcjKysIjjzyCXbt2ISMjA3PmzEFlZWWn12/duhXXXXcdbrnlFuzevRvz5s3DvHnzsH//fj9HLr2evnaAa5Lnye+twsJCP0YsDxaLBRkZGVixYkW3rs/Pz8fll1+O2bNnIycnB/feey9uvfVWfPnllz6OVF56+rqJjhw50uE9l5CQ4KMI5em7777D3XffjZ9++gkbN25EW1sbLr74Ylgsli7vw99zLr157QAv/p4T6DQHDx4UAAg7duzw3PbFF18IKpVKKC0t7fJ+M2fOFO655x4/RCgPU6ZMEe6++27Pvx0Oh5CcnCwsW7as0+t/9atfCZdffnmH26ZOnSrccccdPo1Tjnr62r3xxhuC0Wj0U3TKAED4+OOPz3jNn//8Z2H06NEdbps/f74wZ84cH0Ymb9153b799lsBgFBbW+uXmJSisrJSACB89913XV7D33Od685r583fc6zcdGLbtm2IiorCOeec47ktMzMTarUaP//88xnv+9///hdxcXEYM2YMlixZgubmZl+HKwmbzYbs7GxkZmZ6blOr1cjMzMS2bds6vc+2bds6XA8Ac+bM6fL6QNWb1w4AmpqaMHDgQKSmpuKqq67CgQMH/BGuovE91zfjx49HUlISLrroIvz4I8/+rq+vBwDExMR0eQ3fc53rzmsHeO/3HJObTpjN5tPKr1qtFjExMWdcc77++uvx9ttv49tvv8WSJUvwn//8BzfeeKOvw5VEdXU1HA6HZ5K0yGQydfkamc3mHl0fqHrz2qWnp2PVqlX45JNP8Pbbb8PpdGL69OkoKSnxR8iK1dV7rqGhAS0tLRJFJX9JSUlYuXIlPvroI3z00UdITU3FrFmzsGvXLqlDk4zT6cS9996L884774wT8fl77nTdfe28+XtO8uMX/OmBBx7A008/fcZrDh061OvHP7knZ+zYsUhKSsKFF16I48ePY8iQIb1+XKJp06Z1OBx2+vTpGDlyJP71r3/h8ccflzAyCkTp6elIT0/3/Hv69Ok4fvw4XnjhBfznP/+RMDLp3H333di/fz9++OEHqUNRnO6+dt78PRdUyc0f//hH3HzzzWe8ZvDgwUhMTDytsdNut6OmpgaJiYndfr6pU6cCAI4dOxZwyU1cXBw0Gg0qKio63F5RUdHla5SYmNij6wNVb167U4WEhGDChAk4duyYL0IMGF295yIjIxEaGipRVMo0ZcqUoP1gX7x4sWdjSUpKyhmv5e+5jnry2p2qL7/ngmpZKj4+HiNGjDjjl06nw7Rp01BXV4fs7GzPfb/55hs4nU5PwtIdOTk5AFwl3kCj0+kwadIkbNq0yXOb0+nEpk2bOmTeJ5s2bVqH6wFg48aNXV4fqHrz2p3K4XBg3759Afne8ia+57wnJycn6N5vgiBg8eLF+Pjjj/HNN99g0KBBZ70P33MuvXntTtWn33NeaUsOQJdccokwYcIE4eeffxZ++OEHYdiwYcJ1113n+X5JSYmQnp4u/Pzzz4IgCMKxY8eExx57TNi5c6eQn58vfPLJJ8LgwYOFCy64QKofwefee+89Qa/XC6tXrxYOHjwo3H777UJUVJRgNpsFQRCEm266SXjggQc81//444+CVqsVnnvuOeHQoUPCI488IoSEhAj79u2T6keQTE9fu0cffVT48ssvhePHjwvZ2dnCr3/9a8FgMAgHDhyQ6keQRGNjo7B7925h9+7dAgBh+fLlwu7du4XCwkJBEAThgQceEG666SbP9Xl5eUJYWJjwpz/9STh06JCwYsUKQaPRCBs2bJDqR5BET1+3F154QVi3bp1w9OhRYd++fcI999wjqNVq4euvv5bqR5DEnXfeKRiNRmHz5s1CeXm556u5udlzDX/Pda43r503f88xuenCiRMnhOuuu04IDw8XIiMjhUWLFgmNjY2e7+fn5wsAhG+//VYQBEEoKioSLrjgAiEmJkbQ6/XC0KFDhT/96U9CfX29RD+Bf7z00kvCgAEDBJ1OJ0yZMkX46aefPN+bOXOmsHDhwg7Xv//++8Lw4cMFnU4njB49Wli/fr2fI5aPnrx29957r+dak8kkXHbZZcKuXbskiFpa4hblU7/E12rhwoXCzJkzT7vP+PHjBZ1OJwwePFh44403/B631Hr6uj399NPCkCFDBIPBIMTExAizZs0SvvnmG2mCl1BnrxmADu8h/p7rXG9eO2/+nlO5gyAiIiIKCEHVc0NERESBj8kNERERBRQmN0RERBRQmNwQERFRQGFyQ0RERAGFyQ0REREFFCY3REREFFCY3BAREVFAYXJDREREAYXJDREREQUUJjdEREQUUJjcEBERUUD5f+LyqO2ogBq8AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "qri = data.groupby('Driver_ID').agg({\n",
        "    'Quarterly Rating': 'first'\n",
        "})\n",
        "\n",
        "qrl = data.groupby('Driver_ID').agg({\n",
        "    'Quarterly Rating': 'last'\n",
        "})\n",
        "\n",
        "(qrl - qri).describe()\n",
        "\n",
        "diff = qrl - qri\n",
        "diff"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "ADprE4nFNq57",
        "outputId": "aa6e423b-bda8-4a41-8358-ff28bd0802ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Quarterly Rating\n",
              "Driver_ID                  \n",
              "1                         0\n",
              "2                         0\n",
              "4                         0\n",
              "5                         0\n",
              "6                         1\n",
              "...                     ...\n",
              "2784                      1\n",
              "2785                      0\n",
              "2786                     -1\n",
              "2787                     -1\n",
              "2788                      1\n",
              "\n",
              "[2381 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-72a4b3e3-0e80-4a27-961f-b7bbc8446b1d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Quarterly Rating</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Driver_ID</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2784</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2785</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2786</th>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2787</th>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2788</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2381 rows × 1 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-72a4b3e3-0e80-4a27-961f-b7bbc8446b1d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-72a4b3e3-0e80-4a27-961f-b7bbc8446b1d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-72a4b3e3-0e80-4a27-961f-b7bbc8446b1d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-71e68c16-bf39-4419-8268-465e4480f19e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-71e68c16-bf39-4419-8268-465e4480f19e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-71e68c16-bf39-4419-8268-465e4480f19e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_fa8d07e4-6b5e-4e69-9c46-db0587ca8025\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('diff')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_fa8d07e4-6b5e-4e69-9c46-db0587ca8025 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('diff');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "diff",
              "summary": "{\n  \"name\": \"diff\",\n  \"rows\": 2381,\n  \"fields\": [\n    {\n      \"column\": \"Driver_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 806,\n        \"min\": 1,\n        \"max\": 2788,\n        \"num_unique_values\": 2381,\n        \"samples\": [\n          1663,\n          1264,\n          1618\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quarterly Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -3,\n        \"max\": 3,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.kdeplot(diff)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "BcLFStMuOIkz",
        "outputId": "7bc7607b-ddd4-47e9-fa25-cb27077e3c55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.groupby('Driver_ID').agg({\n",
        "    'LastWorkingDate': 'last',\n",
        "    'Dateofjoining': 'first',\n",
        "    'City': 'first',\n",
        "    'Gender': 'first',\n",
        "    'Education_Level': 'first',\n",
        "    'Income': 'mean',\n",
        "    'Joining Designation': 'first',\n",
        "    \"MMM-YY\": \"last\",\n",
        "    \"Total Business Value\": \"sum\",\n",
        "    'Grade': 'mean',\n",
        "})\n",
        "\n",
        "df['start_age'] = data.groupby('Driver_ID').agg({\n",
        "    'Age': 'first'\n",
        "})\n",
        "\n",
        "df['end_age'] = data.groupby('Driver_ID').agg({\n",
        "    'Age': 'last'\n",
        "})\n",
        "\n",
        "df.reset_index(inplace=True)\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "dgh_Bj3z-7q-",
        "outputId": "44a6a429-b158-47b7-aaa2-f066ac7aa160"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Driver_ID LastWorkingDate Dateofjoining City  Gender  Education_Level  \\\n",
              "0             1      2019-03-11    2018-12-24  C23     0.0                2   \n",
              "1             2             NaT    2020-11-06   C7     0.0                2   \n",
              "2             4      2020-04-27    2019-12-07  C13     0.0                2   \n",
              "3             5      2019-03-07    2019-01-09   C9     0.0                0   \n",
              "4             6             NaT    2020-07-31  C11     1.0                1   \n",
              "...         ...             ...           ...  ...     ...              ...   \n",
              "2376       2784             NaT    2015-10-15  C24     0.0                0   \n",
              "2377       2785      2020-10-28    2020-08-28   C9     1.0                0   \n",
              "2378       2786      2019-09-22    2018-07-31  C19     0.0                0   \n",
              "2379       2787      2019-06-20    2018-07-21  C20     1.0                2   \n",
              "2380       2788             NaT    2020-06-08  C27     0.0                2   \n",
              "\n",
              "       Income  Joining Designation     MMM-YY  Total Business Value  Grade  \\\n",
              "0     57387.0                    1 2019-03-01               1715580    1.0   \n",
              "1     67016.0                    2 2020-12-01                     0    2.0   \n",
              "2     65603.0                    2 2020-04-01                350000    2.0   \n",
              "3     46368.0                    1 2019-03-01                120360    1.0   \n",
              "4     78728.0                    3 2020-12-01               1265000    3.0   \n",
              "...       ...                  ...        ...                   ...    ...   \n",
              "2376  82815.0                    2 2020-12-01              21748820    3.0   \n",
              "2377  12105.0                    1 2020-10-01                     0    1.0   \n",
              "2378  35370.0                    2 2019-09-01               2815090    2.0   \n",
              "2379  69498.0                    1 2019-06-01                977830    1.0   \n",
              "2380  70254.0                    2 2020-12-01               2298240    2.0   \n",
              "\n",
              "      start_age  end_age  \n",
              "0          28.0     28.0  \n",
              "1          31.0     31.0  \n",
              "2          43.0     43.0  \n",
              "3          29.0     29.0  \n",
              "4          31.0     31.0  \n",
              "...         ...      ...  \n",
              "2376       33.0     34.0  \n",
              "2377       34.0     34.0  \n",
              "2378       44.0     45.0  \n",
              "2379       28.0     28.0  \n",
              "2380       29.0     30.0  \n",
              "\n",
              "[2381 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-53315abb-5441-4671-8d42-384c1b44e6ae\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Driver_ID</th>\n",
              "      <th>LastWorkingDate</th>\n",
              "      <th>Dateofjoining</th>\n",
              "      <th>City</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Education_Level</th>\n",
              "      <th>Income</th>\n",
              "      <th>Joining Designation</th>\n",
              "      <th>MMM-YY</th>\n",
              "      <th>Total Business Value</th>\n",
              "      <th>Grade</th>\n",
              "      <th>start_age</th>\n",
              "      <th>end_age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2019-03-11</td>\n",
              "      <td>2018-12-24</td>\n",
              "      <td>C23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>57387.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-03-01</td>\n",
              "      <td>1715580</td>\n",
              "      <td>1.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2020-11-06</td>\n",
              "      <td>C7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>67016.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-12-01</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>31.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>2020-04-27</td>\n",
              "      <td>2019-12-07</td>\n",
              "      <td>C13</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>65603.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>350000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>43.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>2019-03-07</td>\n",
              "      <td>2019-01-09</td>\n",
              "      <td>C9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>46368.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-03-01</td>\n",
              "      <td>120360</td>\n",
              "      <td>1.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2020-07-31</td>\n",
              "      <td>C11</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>78728.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2020-12-01</td>\n",
              "      <td>1265000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>31.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2376</th>\n",
              "      <td>2784</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>C24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>82815.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-12-01</td>\n",
              "      <td>21748820</td>\n",
              "      <td>3.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2377</th>\n",
              "      <td>2785</td>\n",
              "      <td>2020-10-28</td>\n",
              "      <td>2020-08-28</td>\n",
              "      <td>C9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>12105.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-10-01</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2378</th>\n",
              "      <td>2786</td>\n",
              "      <td>2019-09-22</td>\n",
              "      <td>2018-07-31</td>\n",
              "      <td>C19</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>35370.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2019-09-01</td>\n",
              "      <td>2815090</td>\n",
              "      <td>2.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>45.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2379</th>\n",
              "      <td>2787</td>\n",
              "      <td>2019-06-20</td>\n",
              "      <td>2018-07-21</td>\n",
              "      <td>C20</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>69498.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-06-01</td>\n",
              "      <td>977830</td>\n",
              "      <td>1.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2380</th>\n",
              "      <td>2788</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2020-06-08</td>\n",
              "      <td>C27</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>70254.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-12-01</td>\n",
              "      <td>2298240</td>\n",
              "      <td>2.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2381 rows × 13 columns</p>\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2381,\n  \"fields\": [\n    {\n      \"column\": \"Driver_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 806,\n        \"min\": 1,\n        \"max\": 2788,\n        \"num_unique_values\": 2381,\n        \"samples\": [\n          1663,\n          1264,\n          1618\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LastWorkingDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-12-31 00:00:00\",\n        \"max\": \"2020-12-28 00:00:00\",\n        \"num_unique_values\": 493,\n        \"samples\": [\n          \"2020-05-03 00:00:00\",\n          \"2019-10-01 00:00:00\",\n          \"2019-07-19 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dateofjoining\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2013-04-01 00:00:00\",\n        \"max\": \"2020-12-28 00:00:00\",\n        \"num_unique_values\": 869,\n        \"samples\": [\n          \"2019-09-14 00:00:00\",\n          \"2018-01-06 00:00:00\",\n          \"2016-05-13 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 29,\n        \"samples\": [\n          \"C22\",\n          \"C5\",\n          \"C24\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4919972321279086,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education_Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28298.214011922697,\n        \"min\": 10747.0,\n        \"max\": 188418.0,\n        \"num_unique_values\": 2339,\n        \"samples\": [\n          58943.0,\n          96076.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Joining Designation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MMM-YY\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-01-01 00:00:00\",\n        \"max\": \"2020-12-01 00:00:00\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"2020-03-01 00:00:00\",\n          \"2019-01-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total Business Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9127115,\n        \"min\": -1385530,\n        \"max\": 95331060,\n        \"num_unique_values\": 1629,\n        \"samples\": [\n          479380,\n          1628780\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9322571335084214,\n        \"min\": 1.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          3.0454545454545454,\n          2.0416666666666665\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.840686217594289,\n        \"min\": 21.0,\n        \"max\": 58.0,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          51.0,\n          41.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.9833754182849646,\n        \"min\": 21.0,\n        \"max\": 58.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          58.0,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.merge(df, diff[['Quarterly Rating']], on='Driver_ID')\n",
        "df.rename(columns={'Quarterly Rating': 'rating_increase'}, inplace=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "kTOgE44KfQPt",
        "outputId": "d5da562b-8bab-4708-baa0-6488e8371b44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Driver_ID LastWorkingDate Dateofjoining City  Gender  Education_Level  \\\n",
              "0             1      2019-03-11    2018-12-24  C23     0.0                2   \n",
              "1             2             NaT    2020-11-06   C7     0.0                2   \n",
              "2             4      2020-04-27    2019-12-07  C13     0.0                2   \n",
              "3             5      2019-03-07    2019-01-09   C9     0.0                0   \n",
              "4             6             NaT    2020-07-31  C11     1.0                1   \n",
              "...         ...             ...           ...  ...     ...              ...   \n",
              "2376       2784             NaT    2015-10-15  C24     0.0                0   \n",
              "2377       2785      2020-10-28    2020-08-28   C9     1.0                0   \n",
              "2378       2786      2019-09-22    2018-07-31  C19     0.0                0   \n",
              "2379       2787      2019-06-20    2018-07-21  C20     1.0                2   \n",
              "2380       2788             NaT    2020-06-08  C27     0.0                2   \n",
              "\n",
              "       Income  Joining Designation     MMM-YY  Total Business Value  Grade  \\\n",
              "0     57387.0                    1 2019-03-01               1715580    1.0   \n",
              "1     67016.0                    2 2020-12-01                     0    2.0   \n",
              "2     65603.0                    2 2020-04-01                350000    2.0   \n",
              "3     46368.0                    1 2019-03-01                120360    1.0   \n",
              "4     78728.0                    3 2020-12-01               1265000    3.0   \n",
              "...       ...                  ...        ...                   ...    ...   \n",
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              "        document.querySelector('#id_c8a2ccb3-fc6d-4bf1-86ec-44201257ff59 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2381,\n  \"fields\": [\n    {\n      \"column\": \"Driver_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 806,\n        \"min\": 1,\n        \"max\": 2788,\n        \"num_unique_values\": 2381,\n        \"samples\": [\n          1663,\n          1264,\n          1618\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LastWorkingDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-12-31 00:00:00\",\n        \"max\": \"2020-12-28 00:00:00\",\n        \"num_unique_values\": 493,\n        \"samples\": [\n          \"2020-05-03 00:00:00\",\n          \"2019-10-01 00:00:00\",\n          \"2019-07-19 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dateofjoining\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2013-04-01 00:00:00\",\n        \"max\": \"2020-12-28 00:00:00\",\n        \"num_unique_values\": 869,\n        \"samples\": [\n          \"2019-09-14 00:00:00\",\n          \"2018-01-06 00:00:00\",\n          \"2016-05-13 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 29,\n        \"samples\": [\n          \"C22\",\n          \"C5\",\n          \"C24\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4919972321279086,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education_Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28298.214011922697,\n        \"min\": 10747.0,\n        \"max\": 188418.0,\n        \"num_unique_values\": 2339,\n        \"samples\": [\n          58943.0,\n          96076.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Joining Designation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MMM-YY\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-01-01 00:00:00\",\n        \"max\": \"2020-12-01 00:00:00\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"2020-03-01 00:00:00\",\n          \"2019-01-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total Business Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9127115,\n        \"min\": -1385530,\n        \"max\": 95331060,\n        \"num_unique_values\": 1629,\n        \"samples\": [\n          479380,\n          1628780\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9322571335084214,\n        \"min\": 1.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          3.0454545454545454,\n          2.0416666666666665\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.840686217594289,\n        \"min\": 21.0,\n        \"max\": 58.0,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          51.0,\n          41.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.9833754182849646,\n        \"min\": 21.0,\n        \"max\": 58.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          58.0,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating_increase\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -3,\n        \"max\": 3,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for null values\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "xa3-v_dbO2QG",
        "outputId": "95a795a4-842f-4f27-e8c2-066de961de9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Driver_ID                 0\n",
              "LastWorkingDate         765\n",
              "Dateofjoining             0\n",
              "City                      0\n",
              "Gender                    0\n",
              "Education_Level           0\n",
              "Income                    0\n",
              "Joining Designation       0\n",
              "MMM-YY                    0\n",
              "Total Business Value      0\n",
              "Grade                     0\n",
              "start_age                 0\n",
              "end_age                   0\n",
              "rating_increase           0\n",
              "dtype: int64"
            ],
            "text/html": [
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              "      <th>start_age</th>\n",
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              "      <td>0</td>\n",
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              "      <th>rating_increase</th>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# making the churn column\n",
        "df['LastWorkingDate'] = pd.to_datetime(df['LastWorkingDate'])\n",
        "df.loc[df['LastWorkingDate'].isnull(), 'Churn'] = 1\n",
        "df.loc[df['LastWorkingDate'].notnull(), 'Churn'] = 0\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "iZ7g9-n2h8HG",
        "outputId": "80fc70c4-8635-439c-d0bf-d8c03b7e956b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Driver_ID LastWorkingDate Dateofjoining City  Gender  Education_Level  \\\n",
              "0             1      2019-03-11    2018-12-24  C23     0.0                2   \n",
              "1             2             NaT    2020-11-06   C7     0.0                2   \n",
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              "3             5      2019-03-07    2019-01-09   C9     0.0                0   \n",
              "4             6             NaT    2020-07-31  C11     1.0                1   \n",
              "...         ...             ...           ...  ...     ...              ...   \n",
              "2376       2784             NaT    2015-10-15  C24     0.0                0   \n",
              "2377       2785      2020-10-28    2020-08-28   C9     1.0                0   \n",
              "2378       2786      2019-09-22    2018-07-31  C19     0.0                0   \n",
              "2379       2787      2019-06-20    2018-07-21  C20     1.0                2   \n",
              "2380       2788             NaT    2020-06-08  C27     0.0                2   \n",
              "\n",
              "       Income  Joining Designation     MMM-YY  Total Business Value  Grade  \\\n",
              "0     57387.0                    1 2019-03-01               1715580    1.0   \n",
              "1     67016.0                    2 2020-12-01                     0    2.0   \n",
              "2     65603.0                    2 2020-04-01                350000    2.0   \n",
              "3     46368.0                    1 2019-03-01                120360    1.0   \n",
              "4     78728.0                    3 2020-12-01               1265000    3.0   \n",
              "...       ...                  ...        ...                   ...    ...   \n",
              "2376  82815.0                    2 2020-12-01              21748820    3.0   \n",
              "2377  12105.0                    1 2020-10-01                     0    1.0   \n",
              "2378  35370.0                    2 2019-09-01               2815090    2.0   \n",
              "2379  69498.0                    1 2019-06-01                977830    1.0   \n",
              "2380  70254.0                    2 2020-12-01               2298240    2.0   \n",
              "\n",
              "      start_age  end_age  rating_increase  Churn  \n",
              "0          28.0     28.0                0    0.0  \n",
              "1          31.0     31.0                0    1.0  \n",
              "2          43.0     43.0                0    0.0  \n",
              "3          29.0     29.0                0    0.0  \n",
              "4          31.0     31.0                1    1.0  \n",
              "...         ...      ...              ...    ...  \n",
              "2376       33.0     34.0                1    1.0  \n",
              "2377       34.0     34.0                0    0.0  \n",
              "2378       44.0     45.0               -1    0.0  \n",
              "2379       28.0     28.0               -1    0.0  \n",
              "2380       29.0     30.0                1    1.0  \n",
              "\n",
              "[2381 rows x 15 columns]"
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              "      <th>Driver_ID</th>\n",
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              "      <th>1</th>\n",
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              "      <th>2</th>\n",
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              "      <td>2020-04-27</td>\n",
              "      <td>2019-12-07</td>\n",
              "      <td>C13</td>\n",
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              "      <td>2019-01-09</td>\n",
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              "      <td>2019-03-01</td>\n",
              "      <td>120360</td>\n",
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              "      <td>29.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2020-07-31</td>\n",
              "      <td>C11</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>78728.0</td>\n",
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              "      <td>2784</td>\n",
              "      <td>NaT</td>\n",
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              "      <td>C24</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>2020-10-01</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>2786</td>\n",
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              "      <td>0</td>\n",
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              "      <td>44.0</td>\n",
              "      <td>45.0</td>\n",
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              "    <tr>\n",
              "      <th>2379</th>\n",
              "      <td>2787</td>\n",
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              "      <td>28.0</td>\n",
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              "    <tr>\n",
              "      <th>2380</th>\n",
              "      <td>2788</td>\n",
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              "      <td>2020-12-01</td>\n",
              "      <td>2298240</td>\n",
              "      <td>2.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2381 rows × 15 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e0584cce-cb45-4418-813a-5c70c6187ba4')\"\n",
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              "\n",
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              "    </button>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e0584cce-cb45-4418-813a-5c70c6187ba4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e0584cce-cb45-4418-813a-5c70c6187ba4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5cb74c83-9742-4664-b092-e6264def58f8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_e055ebde-0f71-487c-b70d-cf39b1ccce91\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
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              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_e055ebde-0f71-487c-b70d-cf39b1ccce91 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2381,\n  \"fields\": [\n    {\n      \"column\": \"Driver_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 806,\n        \"min\": 1,\n        \"max\": 2788,\n        \"num_unique_values\": 2381,\n        \"samples\": [\n          1663,\n          1264,\n          1618\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LastWorkingDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-12-31 00:00:00\",\n        \"max\": \"2020-12-28 00:00:00\",\n        \"num_unique_values\": 493,\n        \"samples\": [\n          \"2020-05-03 00:00:00\",\n          \"2019-10-01 00:00:00\",\n          \"2019-07-19 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dateofjoining\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2013-04-01 00:00:00\",\n        \"max\": \"2020-12-28 00:00:00\",\n        \"num_unique_values\": 869,\n        \"samples\": [\n          \"2019-09-14 00:00:00\",\n          \"2018-01-06 00:00:00\",\n          \"2016-05-13 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 29,\n        \"samples\": [\n          \"C22\",\n          \"C5\",\n          \"C24\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4919972321279086,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education_Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28298.214011922697,\n        \"min\": 10747.0,\n        \"max\": 188418.0,\n        \"num_unique_values\": 2339,\n        \"samples\": [\n          58943.0,\n          96076.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Joining Designation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MMM-YY\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-01-01 00:00:00\",\n        \"max\": \"2020-12-01 00:00:00\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"2020-03-01 00:00:00\",\n          \"2019-01-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total Business Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9127115,\n        \"min\": -1385530,\n        \"max\": 95331060,\n        \"num_unique_values\": 1629,\n        \"samples\": [\n          479380,\n          1628780\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9322571335084214,\n        \"min\": 1.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          3.0454545454545454,\n          2.0416666666666665\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.840686217594289,\n        \"min\": 21.0,\n        \"max\": 58.0,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          51.0,\n          41.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.9833754182849646,\n        \"min\": 21.0,\n        \"max\": 58.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          58.0,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating_increase\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -3,\n        \"max\": 3,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Churn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.467071340254642,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_cols = df.select_dtypes(include=np.number).columns\n",
        "cat_cols = df.select_dtypes(exclude=np.number).columns"
      ],
      "metadata": {
        "id": "VPp4PepFR6Ji"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D4WlY7jcR-uG",
        "outputId": "b337a138-fd65-4ed7-ec18-d54af7eac30a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Driver_ID', 'Gender', 'Education_Level', 'Income',\n",
              "       'Joining Designation', 'Total Business Value', 'Grade', 'start_age',\n",
              "       'end_age', 'rating_increase', 'Churn'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract all the numerical columns first\n",
        "num_data = df.select_dtypes(include=np.number)\n",
        "num_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "5RfkEdMIRxCn",
        "outputId": "74ca02b1-da04-45a5-a90d-f3b22cc4e5fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Driver_ID               0\n",
              "Gender                  0\n",
              "Education_Level         0\n",
              "Income                  0\n",
              "Joining Designation     0\n",
              "Total Business Value    0\n",
              "Grade                   0\n",
              "start_age               0\n",
              "end_age                 0\n",
              "rating_increase         0\n",
              "Churn                   0\n",
              "dtype: int64"
            ],
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Churn</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WEVUEjeDYmkf",
        "outputId": "9eae4dac-b6ef-429c-c1bc-0959f3839336"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['LastWorkingDate', 'Dateofjoining', 'City', 'MMM-YY'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# imputing the null values in the Last working date column.\n",
        "from sklearn.impute import KNNImputer\n",
        "\n",
        "imputer = KNNImputer(n_neighbors=5, weights=\"uniform\", metric=\"nan_euclidean\")\n",
        "imputed_data = imputer.fit_transform(num_data)\n",
        "df_imputed = pd.DataFrame(imputed_data, columns=num_data.columns)"
      ],
      "metadata": {
        "id": "-4fT4TH2O7E0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract components from data\n",
        "df['Year'] = df['Dateofjoining'].dt.year\n",
        "df['Month'] = df['Dateofjoining'].dt.month\n",
        "df['Day'] = df['Dateofjoining'].dt.day\n",
        "df['Weekday'] = df['Dateofjoining'].dt.weekday  # Monday=0, Sunday=6\n",
        "df['DayOfYear'] = df['Dateofjoining'].dt.dayofyear\n",
        "df['Quarter'] = df['Dateofjoining'].dt.quarter\n",
        "df['IsWeekend'] = df['Dateofjoining'].dt.weekday >= 5  # True if Saturday or Sunday\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "NrhNmExYkz4Z",
        "outputId": "52d57322-67c0-44e9-d403-8558ed14ed96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Driver_ID LastWorkingDate Dateofjoining City  Gender  Education_Level  \\\n",
              "0             1      2019-03-11    2018-12-24  C23     0.0                2   \n",
              "1             2             NaT    2020-11-06   C7     0.0                2   \n",
              "2             4      2020-04-27    2019-12-07  C13     0.0                2   \n",
              "3             5      2019-03-07    2019-01-09   C9     0.0                0   \n",
              "4             6             NaT    2020-07-31  C11     1.0                1   \n",
              "...         ...             ...           ...  ...     ...              ...   \n",
              "2376       2784             NaT    2015-10-15  C24     0.0                0   \n",
              "2377       2785      2020-10-28    2020-08-28   C9     1.0                0   \n",
              "2378       2786      2019-09-22    2018-07-31  C19     0.0                0   \n",
              "2379       2787      2019-06-20    2018-07-21  C20     1.0                2   \n",
              "2380       2788             NaT    2020-06-08  C27     0.0                2   \n",
              "\n",
              "       Income  Joining Designation     MMM-YY  Total Business Value  ...  \\\n",
              "0     57387.0                    1 2019-03-01               1715580  ...   \n",
              "1     67016.0                    2 2020-12-01                     0  ...   \n",
              "2     65603.0                    2 2020-04-01                350000  ...   \n",
              "3     46368.0                    1 2019-03-01                120360  ...   \n",
              "4     78728.0                    3 2020-12-01               1265000  ...   \n",
              "...       ...                  ...        ...                   ...  ...   \n",
              "2376  82815.0                    2 2020-12-01              21748820  ...   \n",
              "2377  12105.0                    1 2020-10-01                     0  ...   \n",
              "2378  35370.0                    2 2019-09-01               2815090  ...   \n",
              "2379  69498.0                    1 2019-06-01                977830  ...   \n",
              "2380  70254.0                    2 2020-12-01               2298240  ...   \n",
              "\n",
              "      end_age  rating_increase  Churn  Year  Month  Day  Weekday  DayOfYear  \\\n",
              "0        28.0                0    0.0  2018     12   24        0        358   \n",
              "1        31.0                0    1.0  2020     11    6        4        311   \n",
              "2        43.0                0    0.0  2019     12    7        5        341   \n",
              "3        29.0                0    0.0  2019      1    9        2          9   \n",
              "4        31.0                1    1.0  2020      7   31        4        213   \n",
              "...       ...              ...    ...   ...    ...  ...      ...        ...   \n",
              "2376     34.0                1    1.0  2015     10   15        3        288   \n",
              "2377     34.0                0    0.0  2020      8   28        4        241   \n",
              "2378     45.0               -1    0.0  2018      7   31        1        212   \n",
              "2379     28.0               -1    0.0  2018      7   21        5        202   \n",
              "2380     30.0                1    1.0  2020      6    8        0        160   \n",
              "\n",
              "      Quarter  IsWeekend  \n",
              "0           4      False  \n",
              "1           4      False  \n",
              "2           4       True  \n",
              "3           1      False  \n",
              "4           3      False  \n",
              "...       ...        ...  \n",
              "2376        4      False  \n",
              "2377        3      False  \n",
              "2378        3      False  \n",
              "2379        3       True  \n",
              "2380        2      False  \n",
              "\n",
              "[2381 rows x 22 columns]"
            ],
            "text/html": [
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              "  <div id=\"df-98011400-be42-4a27-9cdd-a2dd59eec42b\" class=\"colab-df-container\">\n",
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              "      <th>Driver_ID</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2019-03-11</td>\n",
              "      <td>2018-12-24</td>\n",
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              "      <td>358</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2020-11-06</td>\n",
              "      <td>C7</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>67016.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-12-01</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2020</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>311</td>\n",
              "      <td>4</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>2020-04-27</td>\n",
              "      <td>2019-12-07</td>\n",
              "      <td>C13</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>65603.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>350000</td>\n",
              "      <td>...</td>\n",
              "      <td>43.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>12</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>341</td>\n",
              "      <td>4</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>2019-03-07</td>\n",
              "      <td>2019-01-09</td>\n",
              "      <td>C9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>46368.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-03-01</td>\n",
              "      <td>120360</td>\n",
              "      <td>...</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2020-07-31</td>\n",
              "      <td>C11</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>78728.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2020-12-01</td>\n",
              "      <td>1265000</td>\n",
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              "      <td>31.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>2020</td>\n",
              "      <td>7</td>\n",
              "      <td>31</td>\n",
              "      <td>4</td>\n",
              "      <td>213</td>\n",
              "      <td>3</td>\n",
              "      <td>False</td>\n",
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              "      <th>2376</th>\n",
              "      <td>2784</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>C24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>2</td>\n",
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              "      <td>34.0</td>\n",
              "      <td>1</td>\n",
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              "      <td>15</td>\n",
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              "      <td>4</td>\n",
              "      <td>False</td>\n",
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              "      <th>2377</th>\n",
              "      <td>2785</td>\n",
              "      <td>2020-10-28</td>\n",
              "      <td>2020-08-28</td>\n",
              "      <td>C9</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>2020-10-01</td>\n",
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              "      <td>34.0</td>\n",
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              "      <td>8</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "      <td>241</td>\n",
              "      <td>3</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2378</th>\n",
              "      <td>2786</td>\n",
              "      <td>2019-09-22</td>\n",
              "      <td>2018-07-31</td>\n",
              "      <td>C19</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>45.0</td>\n",
              "      <td>-1</td>\n",
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              "    <tr>\n",
              "      <th>2379</th>\n",
              "      <td>2787</td>\n",
              "      <td>2019-06-20</td>\n",
              "      <td>2018-07-21</td>\n",
              "      <td>C20</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>69498.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-06-01</td>\n",
              "      <td>977830</td>\n",
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              "      <td>28.0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2018</td>\n",
              "      <td>7</td>\n",
              "      <td>21</td>\n",
              "      <td>5</td>\n",
              "      <td>202</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2380</th>\n",
              "      <td>2788</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2020-06-08</td>\n",
              "      <td>C27</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>70254.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-12-01</td>\n",
              "      <td>2298240</td>\n",
              "      <td>...</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>160</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2381 rows × 22 columns</p>\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['IsWeekend'] = df['IsWeekend'].apply(lambda x: 1 if x else 0)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "5wu_GQXdl6z1",
        "outputId": "1dc4875c-47ca-4c48-a270-ab7d2a63480d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Driver_ID LastWorkingDate Dateofjoining City  Gender  Education_Level  \\\n",
              "0             1      2019-03-11    2018-12-24  C23     0.0                2   \n",
              "1             2             NaT    2020-11-06   C7     0.0                2   \n",
              "2             4      2020-04-27    2019-12-07  C13     0.0                2   \n",
              "3             5      2019-03-07    2019-01-09   C9     0.0                0   \n",
              "4             6             NaT    2020-07-31  C11     1.0                1   \n",
              "...         ...             ...           ...  ...     ...              ...   \n",
              "2376       2784             NaT    2015-10-15  C24     0.0                0   \n",
              "2377       2785      2020-10-28    2020-08-28   C9     1.0                0   \n",
              "2378       2786      2019-09-22    2018-07-31  C19     0.0                0   \n",
              "2379       2787      2019-06-20    2018-07-21  C20     1.0                2   \n",
              "2380       2788             NaT    2020-06-08  C27     0.0                2   \n",
              "\n",
              "       Income  Joining Designation     MMM-YY  Total Business Value  ...  \\\n",
              "0     57387.0                    1 2019-03-01               1715580  ...   \n",
              "1     67016.0                    2 2020-12-01                     0  ...   \n",
              "2     65603.0                    2 2020-04-01                350000  ...   \n",
              "3     46368.0                    1 2019-03-01                120360  ...   \n",
              "4     78728.0                    3 2020-12-01               1265000  ...   \n",
              "...       ...                  ...        ...                   ...  ...   \n",
              "2376  82815.0                    2 2020-12-01              21748820  ...   \n",
              "2377  12105.0                    1 2020-10-01                     0  ...   \n",
              "2378  35370.0                    2 2019-09-01               2815090  ...   \n",
              "2379  69498.0                    1 2019-06-01                977830  ...   \n",
              "2380  70254.0                    2 2020-12-01               2298240  ...   \n",
              "\n",
              "      end_age  rating_increase  Churn  Year  Month  Day  Weekday  DayOfYear  \\\n",
              "0        28.0                0    0.0  2018     12   24        0        358   \n",
              "1        31.0                0    1.0  2020     11    6        4        311   \n",
              "2        43.0                0    0.0  2019     12    7        5        341   \n",
              "3        29.0                0    0.0  2019      1    9        2          9   \n",
              "4        31.0                1    1.0  2020      7   31        4        213   \n",
              "...       ...              ...    ...   ...    ...  ...      ...        ...   \n",
              "2376     34.0                1    1.0  2015     10   15        3        288   \n",
              "2377     34.0                0    0.0  2020      8   28        4        241   \n",
              "2378     45.0               -1    0.0  2018      7   31        1        212   \n",
              "2379     28.0               -1    0.0  2018      7   21        5        202   \n",
              "2380     30.0                1    1.0  2020      6    8        0        160   \n",
              "\n",
              "      Quarter  IsWeekend  \n",
              "0           4          0  \n",
              "1           4          0  \n",
              "2           4          1  \n",
              "3           1          0  \n",
              "4           3          0  \n",
              "...       ...        ...  \n",
              "2376        4          0  \n",
              "2377        3          0  \n",
              "2378        3          0  \n",
              "2379        3          1  \n",
              "2380        2          0  \n",
              "\n",
              "[2381 rows x 22 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Driver_ID</th>\n",
              "      <th>LastWorkingDate</th>\n",
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              "      <th>DayOfYear</th>\n",
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              "      <th>IsWeekend</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2019-03-11</td>\n",
              "      <td>2018-12-24</td>\n",
              "      <td>C23</td>\n",
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              "      <th>1</th>\n",
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              "      <td>NaT</td>\n",
              "      <td>2020-11-06</td>\n",
              "      <td>C7</td>\n",
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              "      <td>2</td>\n",
              "      <td>67016.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-12-01</td>\n",
              "      <td>0</td>\n",
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              "      <td>31.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2020</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>311</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>2020-04-27</td>\n",
              "      <td>2019-12-07</td>\n",
              "      <td>C13</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>65603.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>350000</td>\n",
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              "      <td>43.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>12</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>341</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>2019-03-07</td>\n",
              "      <td>2019-01-09</td>\n",
              "      <td>C9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>46368.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-03-01</td>\n",
              "      <td>120360</td>\n",
              "      <td>...</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
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              "      <td>6</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2020-07-31</td>\n",
              "      <td>C11</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>78728.0</td>\n",
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              "      <td>2020-12-01</td>\n",
              "      <td>1265000</td>\n",
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              "      <td>31.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2020</td>\n",
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              "      <td>4</td>\n",
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              "      <td>2019-09-22</td>\n",
              "      <td>2018-07-31</td>\n",
              "      <td>C19</td>\n",
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              "      <td>0</td>\n",
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              "      <td>2</td>\n",
              "      <td>2019-09-01</td>\n",
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              "      <td>-1</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>31</td>\n",
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              "      <td>212</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "      <td>2787</td>\n",
              "      <td>2019-06-20</td>\n",
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              "      <td>28.0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>21</td>\n",
              "      <td>5</td>\n",
              "      <td>202</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
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              "      <th>2380</th>\n",
              "      <td>2788</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2020-06-08</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>70254.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-12-01</td>\n",
              "      <td>2298240</td>\n",
              "      <td>...</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>160</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
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          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = df.drop(columns=['Dateofjoining', 'MMM-YY'])\n",
        "train_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "51NO-6orlrrd",
        "outputId": "a8df7e74-bd00-4a79-a3d5-62b8f09c8197"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Driver_ID LastWorkingDate City  Gender  Education_Level   Income  \\\n",
              "0             1      2019-03-11  C23     0.0                2  57387.0   \n",
              "1             2             NaT   C7     0.0                2  67016.0   \n",
              "2             4      2020-04-27  C13     0.0                2  65603.0   \n",
              "3             5      2019-03-07   C9     0.0                0  46368.0   \n",
              "4             6             NaT  C11     1.0                1  78728.0   \n",
              "...         ...             ...  ...     ...              ...      ...   \n",
              "2376       2784             NaT  C24     0.0                0  82815.0   \n",
              "2377       2785      2020-10-28   C9     1.0                0  12105.0   \n",
              "2378       2786      2019-09-22  C19     0.0                0  35370.0   \n",
              "2379       2787      2019-06-20  C20     1.0                2  69498.0   \n",
              "2380       2788             NaT  C27     0.0                2  70254.0   \n",
              "\n",
              "      Joining Designation  Total Business Value  Grade  start_age  end_age  \\\n",
              "0                       1               1715580    1.0       28.0     28.0   \n",
              "1                       2                     0    2.0       31.0     31.0   \n",
              "2                       2                350000    2.0       43.0     43.0   \n",
              "3                       1                120360    1.0       29.0     29.0   \n",
              "4                       3               1265000    3.0       31.0     31.0   \n",
              "...                   ...                   ...    ...        ...      ...   \n",
              "2376                    2              21748820    3.0       33.0     34.0   \n",
              "2377                    1                     0    1.0       34.0     34.0   \n",
              "2378                    2               2815090    2.0       44.0     45.0   \n",
              "2379                    1                977830    1.0       28.0     28.0   \n",
              "2380                    2               2298240    2.0       29.0     30.0   \n",
              "\n",
              "      rating_increase  Churn  Year  Month  Day  Weekday  DayOfYear  Quarter  \\\n",
              "0                   0    0.0  2018     12   24        0        358        4   \n",
              "1                   0    1.0  2020     11    6        4        311        4   \n",
              "2                   0    0.0  2019     12    7        5        341        4   \n",
              "3                   0    0.0  2019      1    9        2          9        1   \n",
              "4                   1    1.0  2020      7   31        4        213        3   \n",
              "...               ...    ...   ...    ...  ...      ...        ...      ...   \n",
              "2376                1    1.0  2015     10   15        3        288        4   \n",
              "2377                0    0.0  2020      8   28        4        241        3   \n",
              "2378               -1    0.0  2018      7   31        1        212        3   \n",
              "2379               -1    0.0  2018      7   21        5        202        3   \n",
              "2380                1    1.0  2020      6    8        0        160        2   \n",
              "\n",
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              "0             0  \n",
              "1             0  \n",
              "2             1  \n",
              "3             0  \n",
              "4             0  \n",
              "...         ...  \n",
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              "2377          0  \n",
              "2378          0  \n",
              "2379          1  \n",
              "2380          0  \n",
              "\n",
              "[2381 rows x 20 columns]"
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              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>67016.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2020</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>311</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>2020-04-27</td>\n",
              "      <td>C13</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>65603.0</td>\n",
              "      <td>2</td>\n",
              "      <td>350000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>12</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>341</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>2019-03-07</td>\n",
              "      <td>C9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>46368.0</td>\n",
              "      <td>1</td>\n",
              "      <td>120360</td>\n",
              "      <td>1.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>NaT</td>\n",
              "      <td>C11</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>78728.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1265000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2020</td>\n",
              "      <td>7</td>\n",
              "      <td>31</td>\n",
              "      <td>4</td>\n",
              "      <td>213</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2376</th>\n",
              "      <td>2784</td>\n",
              "      <td>NaT</td>\n",
              "      <td>C24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>82815.0</td>\n",
              "      <td>2</td>\n",
              "      <td>21748820</td>\n",
              "      <td>3.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2015</td>\n",
              "      <td>10</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>288</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2377</th>\n",
              "      <td>2785</td>\n",
              "      <td>2020-10-28</td>\n",
              "      <td>C9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>12105.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020</td>\n",
              "      <td>8</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "      <td>241</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2378</th>\n",
              "      <td>2786</td>\n",
              "      <td>2019-09-22</td>\n",
              "      <td>C19</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>35370.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2815090</td>\n",
              "      <td>2.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2018</td>\n",
              "      <td>7</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "      <td>212</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2379</th>\n",
              "      <td>2787</td>\n",
              "      <td>2019-06-20</td>\n",
              "      <td>C20</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>69498.0</td>\n",
              "      <td>1</td>\n",
              "      <td>977830</td>\n",
              "      <td>1.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2018</td>\n",
              "      <td>7</td>\n",
              "      <td>21</td>\n",
              "      <td>5</td>\n",
              "      <td>202</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2380</th>\n",
              "      <td>2788</td>\n",
              "      <td>NaT</td>\n",
              "      <td>C27</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>70254.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2298240</td>\n",
              "      <td>2.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>160</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2381 rows × 20 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
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              "\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "\n",
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              "type": "dataframe",
              "variable_name": "train_data",
              "summary": "{\n  \"name\": \"train_data\",\n  \"rows\": 2381,\n  \"fields\": [\n    {\n      \"column\": \"Driver_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 806,\n        \"min\": 1,\n        \"max\": 2788,\n        \"num_unique_values\": 2381,\n        \"samples\": [\n          1663,\n          1264,\n          1618\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LastWorkingDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-12-31 00:00:00\",\n        \"max\": \"2020-12-28 00:00:00\",\n        \"num_unique_values\": 493,\n        \"samples\": [\n          \"2020-05-03 00:00:00\",\n          \"2019-10-01 00:00:00\",\n          \"2019-07-19 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 29,\n        \"samples\": [\n          \"C22\",\n          \"C5\",\n          \"C24\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4919972321279086,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education_Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28298.214011922697,\n        \"min\": 10747.0,\n        \"max\": 188418.0,\n        \"num_unique_values\": 2339,\n        \"samples\": [\n          58943.0,\n          96076.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Joining Designation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total Business Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9127115,\n        \"min\": -1385530,\n        \"max\": 95331060,\n        \"num_unique_values\": 1629,\n        \"samples\": [\n          479380,\n          1628780\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9322571335084214,\n        \"min\": 1.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          3.0454545454545454,\n          2.0416666666666665\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.840686217594289,\n        \"min\": 21.0,\n        \"max\": 58.0,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          51.0,\n          41.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.9833754182849646,\n        \"min\": 21.0,\n        \"max\": 58.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          58.0,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating_increase\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -3,\n        \"max\": 3,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Churn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.467071340254642,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2020,\n          2016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          4,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Day\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          5,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weekday\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DayOfYear\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 337,\n        \"samples\": [\n          212,\n          78\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quarter\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IsWeekend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# converting the last working date column to timestamp number and then imputing it\n",
        "train_data['LastWorkingDate'] = pd.to_datetime(train_data['LastWorkingDate'])\n",
        "train_data['LastWorkingDateTimestamp'] = train_data['LastWorkingDate'].apply(lambda x: x.timestamp() if pd.notnull(x) else np.nan)\n",
        "\n",
        "from sklearn.impute import KNNImputer\n",
        "\n",
        "imputer = KNNImputer(n_neighbors=5, weights=\"uniform\", metric=\"nan_euclidean\")\n",
        "train_data['LastWorkingDateTimestamp'] = imputer.fit_transform(train_data[['LastWorkingDateTimestamp']])\n",
        "\n",
        "train_data = train_data.drop(columns = ['LastWorkingDate'])"
      ],
      "metadata": {
        "id": "A4GHgExMmY_H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "id": "QSVJbi3jlkg-",
        "outputId": "43fb2672-660a-4031-b1bb-4d5e1e8dca39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Driver_ID                   0\n",
              "City                        0\n",
              "Gender                      0\n",
              "Education_Level             0\n",
              "Income                      0\n",
              "Joining Designation         0\n",
              "Total Business Value        0\n",
              "Grade                       0\n",
              "start_age                   0\n",
              "end_age                     0\n",
              "rating_increase             0\n",
              "Churn                       0\n",
              "Year                        0\n",
              "Month                       0\n",
              "Day                         0\n",
              "Weekday                     0\n",
              "DayOfYear                   0\n",
              "Quarter                     0\n",
              "IsWeekend                   0\n",
              "LastWorkingDateTimestamp    0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Driver_ID</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>City</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>Education_Level</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Income</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Joining Designation</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total Business Value</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Grade</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>start_age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>end_age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rating_increase</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Churn</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Day</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weekday</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DayOfYear</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Quarter</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IsWeekend</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LastWorkingDateTimestamp</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# encode the cat labels...\n",
        "num_cols = train_data.select_dtypes(include=np.number).columns\n",
        "cat_cols = train_data.select_dtypes(exclude=np.number).columns"
      ],
      "metadata": {
        "id": "aznjBNVPoN-F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BHfk4kGOoSIx",
        "outputId": "cfa7f963-a0e4-46a6-8b47-98a4834a3442"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Driver_ID', 'Gender', 'Education_Level', 'Income',\n",
              "       'Joining Designation', 'Total Business Value', 'Grade', 'start_age',\n",
              "       'end_age', 'rating_increase', 'Churn', 'Year', 'Month', 'Day',\n",
              "       'Weekday', 'DayOfYear', 'Quarter', 'IsWeekend',\n",
              "       'LastWorkingDateTimestamp'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3j_Kt3rbkai2",
        "outputId": "24eebdee-f5e5-4bbc-a108-17286083a878"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['City'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data[cat_cols].nunique() # number of unique cities"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "id": "A7oLdt6ToWMM",
        "outputId": "a78a6033-ef37-49de-94cd-113acf93f2e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "City    29\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>City</th>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# encoding the cities using frequency encoding...\n",
        "freq_dist = train_data['City'].value_counts()\n",
        "sns.kdeplot(freq_dist)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "mJtRaLPwobEg",
        "outputId": "dafdb288-1bd5-4d5a-c2aa-7d60ebb6f7e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='count', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 129
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data['City_encoded'] = train_data['City'].map(freq_dist)\n",
        "train_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "CCW98tELpkF3",
        "outputId": "425d6454-2fff-4d41-e9e9-fa9c6680d33b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Driver_ID City  Gender  Education_Level   Income  Joining Designation  \\\n",
              "0             1  C23     0.0                2  57387.0                    1   \n",
              "1             2   C7     0.0                2  67016.0                    2   \n",
              "2             4  C13     0.0                2  65603.0                    2   \n",
              "3             5   C9     0.0                0  46368.0                    1   \n",
              "4             6  C11     1.0                1  78728.0                    3   \n",
              "...         ...  ...     ...              ...      ...                  ...   \n",
              "2376       2784  C24     0.0                0  82815.0                    2   \n",
              "2377       2785   C9     1.0                0  12105.0                    1   \n",
              "2378       2786  C19     0.0                0  35370.0                    2   \n",
              "2379       2787  C20     1.0                2  69498.0                    1   \n",
              "2380       2788  C27     0.0                2  70254.0                    2   \n",
              "\n",
              "      Total Business Value  Grade  start_age  end_age  ...  Churn  Year  \\\n",
              "0                  1715580    1.0       28.0     28.0  ...    0.0  2018   \n",
              "1                        0    2.0       31.0     31.0  ...    1.0  2020   \n",
              "2                   350000    2.0       43.0     43.0  ...    0.0  2019   \n",
              "3                   120360    1.0       29.0     29.0  ...    0.0  2019   \n",
              "4                  1265000    3.0       31.0     31.0  ...    1.0  2020   \n",
              "...                    ...    ...        ...      ...  ...    ...   ...   \n",
              "2376              21748820    3.0       33.0     34.0  ...    1.0  2015   \n",
              "2377                     0    1.0       34.0     34.0  ...    0.0  2020   \n",
              "2378               2815090    2.0       44.0     45.0  ...    0.0  2018   \n",
              "2379                977830    1.0       28.0     28.0  ...    0.0  2018   \n",
              "2380               2298240    2.0       29.0     30.0  ...    1.0  2020   \n",
              "\n",
              "      Month  Day  Weekday  DayOfYear  Quarter  IsWeekend  \\\n",
              "0        12   24        0        358        4          0   \n",
              "1        11    6        4        311        4          0   \n",
              "2        12    7        5        341        4          1   \n",
              "3         1    9        2          9        1          0   \n",
              "4         7   31        4        213        3          0   \n",
              "...     ...  ...      ...        ...      ...        ...   \n",
              "2376     10   15        3        288        4          0   \n",
              "2377      8   28        4        241        3          0   \n",
              "2378      7   31        1        212        3          0   \n",
              "2379      7   21        5        202        3          1   \n",
              "2380      6    8        0        160        2          0   \n",
              "\n",
              "      LastWorkingDateTimestamp  City_encoded  \n",
              "0                 1.552262e+09            74  \n",
              "1                 1.576962e+09            76  \n",
              "2                 1.587946e+09            71  \n",
              "3                 1.551917e+09            75  \n",
              "4                 1.576962e+09            64  \n",
              "...                        ...           ...  \n",
              "2376              1.576962e+09            73  \n",
              "2377              1.603843e+09            75  \n",
              "2378              1.569110e+09            72  \n",
              "2379              1.560989e+09           152  \n",
              "2380              1.576962e+09            89  \n",
              "\n",
              "[2381 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1e0dfc9d-ea9c-47f7-a1b5-798e06ec6d7b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Driver_ID</th>\n",
              "      <th>City</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Education_Level</th>\n",
              "      <th>Income</th>\n",
              "      <th>Joining Designation</th>\n",
              "      <th>Total Business Value</th>\n",
              "      <th>Grade</th>\n",
              "      <th>start_age</th>\n",
              "      <th>end_age</th>\n",
              "      <th>...</th>\n",
              "      <th>Churn</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Weekday</th>\n",
              "      <th>DayOfYear</th>\n",
              "      <th>Quarter</th>\n",
              "      <th>IsWeekend</th>\n",
              "      <th>LastWorkingDateTimestamp</th>\n",
              "      <th>City_encoded</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>C23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>57387.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1715580</td>\n",
              "      <td>1.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2018</td>\n",
              "      <td>12</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>358</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1.552262e+09</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>C7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>67016.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2020</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>311</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1.576962e+09</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>C13</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>65603.0</td>\n",
              "      <td>2</td>\n",
              "      <td>350000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>12</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>341</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1.587946e+09</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>C9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>46368.0</td>\n",
              "      <td>1</td>\n",
              "      <td>120360</td>\n",
              "      <td>1.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.551917e+09</td>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>C11</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>78728.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1265000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2020</td>\n",
              "      <td>7</td>\n",
              "      <td>31</td>\n",
              "      <td>4</td>\n",
              "      <td>213</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1.576962e+09</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2376</th>\n",
              "      <td>2784</td>\n",
              "      <td>C24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>82815.0</td>\n",
              "      <td>2</td>\n",
              "      <td>21748820</td>\n",
              "      <td>3.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2015</td>\n",
              "      <td>10</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>288</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1.576962e+09</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2377</th>\n",
              "      <td>2785</td>\n",
              "      <td>C9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>12105.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020</td>\n",
              "      <td>8</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "      <td>241</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1.603843e+09</td>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2378</th>\n",
              "      <td>2786</td>\n",
              "      <td>C19</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>35370.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2815090</td>\n",
              "      <td>2.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2018</td>\n",
              "      <td>7</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "      <td>212</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1.569110e+09</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2379</th>\n",
              "      <td>2787</td>\n",
              "      <td>C20</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>69498.0</td>\n",
              "      <td>1</td>\n",
              "      <td>977830</td>\n",
              "      <td>1.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2018</td>\n",
              "      <td>7</td>\n",
              "      <td>21</td>\n",
              "      <td>5</td>\n",
              "      <td>202</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1.560989e+09</td>\n",
              "      <td>152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2380</th>\n",
              "      <td>2788</td>\n",
              "      <td>C27</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>70254.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2298240</td>\n",
              "      <td>2.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>160</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1.576962e+09</td>\n",
              "      <td>89</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2381 rows × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e0dfc9d-ea9c-47f7-a1b5-798e06ec6d7b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1e0dfc9d-ea9c-47f7-a1b5-798e06ec6d7b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1e0dfc9d-ea9c-47f7-a1b5-798e06ec6d7b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c6e29704-9a5c-4c0e-8680-3ced7fe39010\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c6e29704-9a5c-4c0e-8680-3ced7fe39010')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c6e29704-9a5c-4c0e-8680-3ced7fe39010 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_9044d8c7-823b-4ff0-ab76-b41b3087b0ca\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('train_data');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_data"
            }
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "id": "9APJPz-Ez_4z",
        "outputId": "44fad2e5-f64d-49e5-fb47-aa1897a1a5ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Driver_ID                   0\n",
              "City                        0\n",
              "Gender                      0\n",
              "Education_Level             0\n",
              "Income                      0\n",
              "Joining Designation         0\n",
              "Total Business Value        0\n",
              "Grade                       0\n",
              "start_age                   0\n",
              "end_age                     0\n",
              "rating_increase             0\n",
              "Churn                       0\n",
              "Year                        0\n",
              "Month                       0\n",
              "Day                         0\n",
              "Weekday                     0\n",
              "DayOfYear                   0\n",
              "Quarter                     0\n",
              "IsWeekend                   0\n",
              "LastWorkingDateTimestamp    0\n",
              "City_encoded                0\n",
              "dtype: int64"
            ],
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              "      <td>0</td>\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. impute the null values -> use KNN imputer\n",
        "  1.1 Knn imputer only takes numerical values so segregate the dataframe seperately and then impute them\n",
        "  \n",
        "2. after imputing the numerical values concatenate the categorical features data set and the numerically imputed df , :: HINT: we can make use of a set to ge the differences between the columns, you can figure that out.\n",
        "\n",
        "3."
      ],
      "metadata": {
        "id": "vOI8-MoZ3ccu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_final = train_data.drop(columns=['City', 'Driver_ID'])\n",
        "data_final"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "TyYeMfacqC--",
        "outputId": "15713716-10fe-4269-ccb7-d108125cd62a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>2298240</td>\n",
              "      <td>2.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>160</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1.576962e+09</td>\n",
              "      <td>89</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2381 rows × 19 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fd772327-79a7-46fa-a6d8-d0d395a4c454')\"\n",
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              "\n",
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              "\n",
              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fd772327-79a7-46fa-a6d8-d0d395a4c454 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fd772327-79a7-46fa-a6d8-d0d395a4c454');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
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              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ab5a4b1c-ef89-4dd4-91b0-f9098f8ce12c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_c91b4630-649d-46ba-bbf0-b59ac71edc20\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
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              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data_final')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "      buttonEl.onclick = () => {\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_final",
              "summary": "{\n  \"name\": \"data_final\",\n  \"rows\": 2381,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4919972321279086,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education_Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28298.214011922697,\n        \"min\": 10747.0,\n        \"max\": 188418.0,\n        \"num_unique_values\": 2339,\n        \"samples\": [\n          58943.0,\n          96076.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Joining Designation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total Business Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9127115,\n        \"min\": -1385530,\n        \"max\": 95331060,\n        \"num_unique_values\": 1629,\n        \"samples\": [\n          479380,\n          1628780\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9322571335084214,\n        \"min\": 1.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          3.0454545454545454,\n          2.0416666666666665\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.840686217594289,\n        \"min\": 21.0,\n        \"max\": 58.0,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          51.0,\n          41.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.9833754182849646,\n        \"min\": 21.0,\n        \"max\": 58.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          58.0,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating_increase\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -3,\n        \"max\": 3,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Churn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.467071340254642,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2020,\n          2016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          4,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Day\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          5,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weekday\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DayOfYear\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 337,\n        \"samples\": [\n          212,\n          78\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quarter\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IsWeekend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LastWorkingDateTimestamp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15360563.628461307,\n        \"min\": 1546214400.0,\n        \"max\": 1609113600.0,\n        \"num_unique_values\": 494,\n        \"samples\": [\n          1588464000.0,\n          1592784000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City_encoded\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 64,\n        \"max\": 152,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          74,\n          101\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# First we are going to try out tree based ensembles so scaling wont affect the data that much.\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = data_final.drop(columns=['Churn'])\n",
        "y = data_final['Churn']"
      ],
      "metadata": {
        "id": "SpG2kktXqZ3i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "print(\"Training set size:\", X_train.shape)\n",
        "print(\"Testing set size:\", X_test.shape)"
      ],
      "metadata": {
        "id": "nrbsbAGX-clO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7980cf9d-6b31-4f56-e177-44ad6abaf37c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set size: (1904, 18)\n",
            "Testing set size: (477, 18)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "ada_clf = AdaBoostClassifier(\n",
        " DecisionTreeClassifier(max_depth=1), n_estimators=200, learning_rate=0.5)\n",
        "\n",
        "ada_clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "eEq7gB4jq-BB",
        "outputId": "798b1622-b275-44aa-e990-5fb32709f81c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=1),\n",
              "                   learning_rate=0.5, n_estimators=200)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=1),\n",
              "                   learning_rate=0.5, n_estimators=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>AdaBoostClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.AdaBoostClassifier.html\">?<span>Documentation for AdaBoostClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=1),\n",
              "                   learning_rate=0.5, n_estimators=200)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: DecisionTreeClassifier</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=1)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=1)</pre></div> </div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ada_y_pred = ada_clf.predict(X_test)\n",
        "ada_y_train_pred = ada_clf.predict(X_train)"
      ],
      "metadata": {
        "id": "_CUHCE9EsTDi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "\n",
        "print(classification_report(y_test, ada_y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9eLQDmzQsYxl",
        "outputId": "41df3d2e-4789-4f7c-fc92-e11878e0e21e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      1.00      1.00       327\n",
            "         1.0       1.00      1.00      1.00       150\n",
            "\n",
            "    accuracy                           1.00       477\n",
            "   macro avg       1.00      1.00      1.00       477\n",
            "weighted avg       1.00      1.00      1.00       477\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_train, ada_y_train_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ugovYdLusdl0",
        "outputId": "f36938e6-c687-428a-906a-e9132d441078"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      1.00      1.00      1289\n",
            "         1.0       1.00      1.00      1.00       615\n",
            "\n",
            "    accuracy                           1.00      1904\n",
            "   macro avg       1.00      1.00      1.00      1904\n",
            "weighted avg       1.00      1.00      1.00      1904\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Image\n",
        "from six import StringIO\n",
        "from sklearn.tree import export_graphviz\n",
        "import pydot\n",
        "\n",
        "# List of individual trees in the AdaBoost ensemble\n",
        "trees = ada_clf.estimators_\n",
        "\n",
        "# Features from the training dataset\n",
        "features = list(X_train.columns)\n",
        "\n",
        "# Iterate through each tree in the ensemble\n",
        "for i, tree in enumerate(trees):\n",
        "    dot_data = StringIO()\n",
        "\n",
        "    # Export the current tree to DOT format\n",
        "    export_graphviz(tree, out_file=dot_data, feature_names=features, filled=True)\n",
        "\n",
        "    # Create a graph from the DOT data\n",
        "    graph = pydot.graph_from_dot_data(dot_data.getvalue())\n",
        "\n",
        "    # Display the image of the tree\n",
        "    (Image(graph[0].create_png()))\n",
        "\n",
        "    # Optionally, save the tree image\n",
        "    graph[0].write_png(f'tree_{i}.png')\n"
      ],
      "metadata": {
        "id": "wz8Pomeayl4o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Using random forest classifier..."
      ],
      "metadata": {
        "id": "oke_kKsgtrer"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import cross_validate, KFold, StratifiedKFold, cross_val_score\n",
        "\n",
        "kfold = KFold(n_splits=10)\n",
        "\n",
        "rf = RandomForestClassifier()\n",
        "\n",
        "cv_acc_results = cross_validate(rf, X_train, y_train, cv= kfold, scoring='f1',return_train_score=True)\n",
        "\n",
        "print(f\"Accuracy Mean: Train: {cv_acc_results['train_score'].mean()*100} Validation: {cv_acc_results['test_score'].mean()*100}\")\n",
        "print(f\"Accuracy Std: Train: {cv_acc_results['train_score'].std()*100} Validation: {cv_acc_results['test_score'].std()*100}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F57XKRIFtrAv",
        "outputId": "f931fdfa-e410-4487-fb66-8c681e865826"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Mean: Train: 100.0 Validation: 99.91735537190083\n",
            "Accuracy Std: Train: 0.0 Validation: 0.2479338842975198\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_classifier = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=42)\n",
        "\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
        "\n",
        "# Cross-validation scoring\n",
        "scores = cross_val_score(rf_classifier, X_train, y_train, cv=cv, scoring='f1', n_jobs=-1)\n",
        "\n",
        "# Print results\n",
        "print(f\"Cross-validation scores: {scores}\")\n",
        "print(f\"Mean cross-validation score: {scores.mean():.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-C49cZqqtA_u",
        "outputId": "3f02f403-dea6-4a4a-9bf7-4a3783a4f910"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-validation scores: [0.99180328 0.97925311 0.99180328 0.95319149 0.99180328]\n",
            "Mean cross-validation score: 0.9816\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking rf_classifier on the test set.\n",
        "rf_classifier.fit(X_train, y_train)\n",
        "y_pred_rf = rf_classifier.predict(X_test)\n",
        "print(classification_report(y_test, y_pred_rf))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8fK_xaxAu5gO",
        "outputId": "d72210ac-56b0-4f0c-ef7e-e8015d740c7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.97      1.00      0.99       327\n",
            "         1.0       1.00      0.94      0.97       150\n",
            "\n",
            "    accuracy                           0.98       477\n",
            "   macro avg       0.99      0.97      0.98       477\n",
            "weighted avg       0.98      0.98      0.98       477\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Let's apply some grid search and tune the hyperparameters for RF"
      ],
      "metadata": {
        "id": "puu__ry6vlrp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import make_scorer, f1_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "param_grid = {\n",
        "    'n_estimators': [100, 200],\n",
        "    'max_depth': [3, 5, 10, None],\n",
        "    'min_samples_split': [2, 5],\n",
        "    'min_samples_leaf': [1, 2],\n",
        "    'max_features': ['auto', 'sqrt', 'log2']\n",
        "}\n",
        "\n",
        "scorer = make_scorer(f1_score, pos_label=1)\n",
        "\n",
        "grid_search = GridSearchCV(estimator=rf_classifier, param_grid=param_grid, cv=5, n_jobs=-1, verbose=True, scoring=scorer)\n",
        "\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "print(\"Best Parameters:\", grid_search.best_params_)\n",
        "print(\"Best F1 Score:\", grid_search.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tPpBVM4KvlHU",
        "outputId": "c6439b1d-7d2e-44ab-e3b7-1b48d765971f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 96 candidates, totalling 480 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:528: FitFailedWarning: \n",
            "160 fits failed out of a total of 480.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "58 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1382, in wrapper\n",
            "    estimator._validate_params()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 436, in _validate_params\n",
            "    validate_parameter_constraints(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 98, in validate_parameter_constraints\n",
            "    raise InvalidParameterError(\n",
            "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "102 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1382, in wrapper\n",
            "    estimator._validate_params()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 436, in _validate_params\n",
            "    validate_parameter_constraints(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 98, in validate_parameter_constraints\n",
            "    raise InvalidParameterError(\n",
            "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:1107: UserWarning: One or more of the test scores are non-finite: [       nan        nan        nan        nan        nan        nan\n",
            "        nan        nan 0.93876834 0.9556264  0.93787363 0.9556264\n",
            " 0.93692302 0.95745461 0.93692302 0.95745461 0.93876834 0.9556264\n",
            " 0.93787363 0.9556264  0.93692302 0.95745461 0.93692302 0.95745461\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan 0.97499907 0.98181804 0.97667928 0.98265469\n",
            " 0.97668631 0.98265469 0.97668631 0.98265469 0.97499907 0.98181804\n",
            " 0.97667928 0.98265469 0.97668631 0.98265469 0.97668631 0.98265469\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan 0.99755763 0.99755763 0.99673461 0.99918367\n",
            " 0.99755763 0.99837396 0.99673461 0.99837396 0.99755763 0.99755763\n",
            " 0.99673461 0.99918367 0.99755763 0.99837396 0.99673461 0.99837396\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan 0.99919028 0.99837396 0.99837396 0.99837396\n",
            " 0.99673461 0.99755094 0.99674791 0.99918367 0.99919028 0.99837396\n",
            " 0.99837396 0.99837396 0.99673461 0.99755094 0.99674791 0.99918367]\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Parameters: {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
            "Best F1 Score: 0.9991902834008097\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid_model = grid_search.best_estimator_"
      ],
      "metadata": {
        "id": "XVAf418FvhmV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid_y_pred = grid_model.predict(X_test)\n",
        "print(classification_report(y_test, grid_y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PbknwzWxxuc7",
        "outputId": "83fff302-df1c-41e8-c4c0-99ea9bd5a13a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      1.00      1.00       327\n",
            "         1.0       0.99      1.00      1.00       150\n",
            "\n",
            "    accuracy                           1.00       477\n",
            "   macro avg       1.00      1.00      1.00       477\n",
            "weighted avg       1.00      1.00      1.00       477\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Applying Logistic regression"
      ],
      "metadata": {
        "id": "ZKYqwNgIx_-i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "1Fj0kEjK4UIa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for class imbalance\n",
        "y_train.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "Z-IAvFhz5Ztt",
        "outputId": "6cbebaf3-1952-42f9-b44c-597381a64197"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Churn\n",
              "0.0    1289\n",
              "1.0     615\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Churn</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>1289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>615</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "model_lr = LogisticRegression(class_weight='balanced') # start with a basic logistic regression model\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
        "\n",
        "# Cross-validation scoring\n",
        "scores = cross_val_score(rf_classifier, X_train, y_train, cv=cv, scoring='f1', n_jobs=-1)\n",
        "\n",
        "# Print results\n",
        "print(f\"Cross-validation scores: {scores}\")\n",
        "print(f\"Mean cross-validation score: {scores.mean():.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "edNyZqwFx_EH",
        "outputId": "6d81bbeb-4917-4f35-a11e-8317f24e4b8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-validation scores: [0.99180328 0.97925311 0.99180328 0.95319149 0.99180328]\n",
            "Mean cross-validation score: 0.9816\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_lr.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "eXhpRYAv2uJb",
        "outputId": "c53f4c50-7b46-4d84-d7e6-c2fbb7a7f230"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(class_weight='balanced')"
            ],
            "text/html": [
              "<style>#sk-container-id-8 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-8 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-8 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-8 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-8 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-8 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-8 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-8 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-8 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-8 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(class_weight=&#x27;balanced&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(class_weight=&#x27;balanced&#x27;)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_model_lr = model_lr.predict(X_test)\n",
        "print(classification_report(y_test, y_pred_model_lr))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DiXDV1TdxxZ3",
        "outputId": "2790be8f-88fd-4dda-9768-160f1352a4d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.95      0.95      0.95       327\n",
            "         1.0       0.90      0.89      0.89       150\n",
            "\n",
            "    accuracy                           0.93       477\n",
            "   macro avg       0.92      0.92      0.92       477\n",
            "weighted avg       0.93      0.93      0.93       477\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# applying grid search for logistic regression...\n",
        "param_grid = {\n",
        "    'C': [0.01, 0.1, 1, 10],             # Regularization parameter\n",
        "    'penalty': ['l2', 'elasticnet'],     # Regularization types\n",
        "    'solver': ['lbfgs', 'liblinear'],  # Optimization algorithms\n",
        "    'class_weight': ['balanced', None]  # Class weights\n",
        "}"
      ],
      "metadata": {
        "id": "mdkO8wr42r9r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scorer = make_scorer(f1_score, pos_label = 1)\n",
        "grid_search = GridSearchCV(LogisticRegression(max_iter=1000), param_grid = param_grid, cv=5, scoring=scorer, verbose=True, n_jobs=-1)"
      ],
      "metadata": {
        "id": "YeyrVNqD3Tzj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 934
        },
        "id": "V4ppqyCg3kTk",
        "outputId": "3725b1de-f59f-454b-a662-fd1967ed0449"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 32 candidates, totalling 160 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:528: FitFailedWarning: \n",
            "80 fits failed out of a total of 160.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "40 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1389, in wrapper\n",
            "    return fit_method(estimator, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 1193, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 63, in _check_solver\n",
            "    raise ValueError(\n",
            "ValueError: Solver lbfgs supports only 'l2' or None penalties, got elasticnet penalty.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "40 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1389, in wrapper\n",
            "    return fit_method(estimator, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 1193, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 71, in _check_solver\n",
            "    raise ValueError(\n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:1107: UserWarning: One or more of the test scores are non-finite: [0.78720144 0.7902908         nan        nan 0.57149881 0.64864553\n",
            "        nan        nan 0.88026825 0.87968964        nan        nan\n",
            " 0.81037196 0.81386355        nan        nan 0.89527963 0.89527963\n",
            "        nan        nan 0.88390351 0.88488922        nan        nan\n",
            " 0.89269453 0.89269453        nan        nan 0.89190848 0.89279841\n",
            "        nan        nan]\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=LogisticRegression(max_iter=1000), n_jobs=-1,\n",
              "             param_grid={'C': [0.01, 0.1, 1, 10],\n",
              "                         'class_weight': ['balanced', None],\n",
              "                         'penalty': ['l2', 'elasticnet'],\n",
              "                         'solver': ['lbfgs', 'liblinear']},\n",
              "             scoring=make_scorer(f1_score, response_method='predict', pos_label=1),\n",
              "             verbose=True)"
            ],
            "text/html": [
              "<style>#sk-container-id-9 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-9 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-9 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-9 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-9 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-9 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-9 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-9 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-9 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-9 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-9 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-9 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-9 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(max_iter=1000), n_jobs=-1,\n",
              "             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1, 10],\n",
              "                         &#x27;class_weight&#x27;: [&#x27;balanced&#x27;, None],\n",
              "                         &#x27;penalty&#x27;: [&#x27;l2&#x27;, &#x27;elasticnet&#x27;],\n",
              "                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;]},\n",
              "             scoring=make_scorer(f1_score, response_method=&#x27;predict&#x27;, pos_label=1),\n",
              "             verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(max_iter=1000), n_jobs=-1,\n",
              "             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1, 10],\n",
              "                         &#x27;class_weight&#x27;: [&#x27;balanced&#x27;, None],\n",
              "                         &#x27;penalty&#x27;: [&#x27;l2&#x27;, &#x27;elasticnet&#x27;],\n",
              "                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;]},\n",
              "             scoring=make_scorer(f1_score, response_method=&#x27;predict&#x27;, pos_label=1),\n",
              "             verbose=True)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: LogisticRegression</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=1, class_weight=&#x27;balanced&#x27;, max_iter=1000)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=1, class_weight=&#x27;balanced&#x27;, max_iter=1000)</pre></div> </div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Best Parameters:\", grid_search.best_params_)\n",
        "print(\"Best F1 Score:\", grid_search.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sd73wGq03wOQ",
        "outputId": "2bdf67d8-ad6c-4dd8-f430-fbac6050528c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Parameters: {'C': 1, 'class_weight': 'balanced', 'penalty': 'l2', 'solver': 'lbfgs'}\n",
            "Best F1 Score: 0.8952796329153803\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# applying smote over-sampling\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "smote = SMOTE(random_state=42)\n",
        "\n",
        "X_resampled, y_resampled = smote.fit_resample(X_train, y_train)\n",
        "\n",
        "model_lr.fit(X_resampled, y_resampled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "lbZdwN9S3Kvl",
        "outputId": "dd4585b2-dd3f-4ebc-d008-a2c8525ac6df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(class_weight='balanced')"
            ],
            "text/html": [
              "<style>#sk-container-id-10 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-10 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-10 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-10 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-10 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-10 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-10 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-10 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-10 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-10 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(class_weight=&#x27;balanced&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" checked><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(class_weight=&#x27;balanced&#x27;)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_smote_model_lr = model_lr.predict(X_test)\n",
        "print(classification_report(y_test, y_pred_smote_model_lr))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1XHqcAlz7MEB",
        "outputId": "a180f027-4249-48c2-e49f-b0fb85f62902"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.95      0.96      0.96       327\n",
            "         1.0       0.91      0.89      0.90       150\n",
            "\n",
            "    accuracy                           0.94       477\n",
            "   macro avg       0.93      0.93      0.93       477\n",
            "weighted avg       0.94      0.94      0.94       477\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# applying grid search over the over-sampled data\n",
        "param_grid = {\n",
        "    'max_iter': [700, 1000, 1500],\n",
        "    'penalty': ['l1', 'l2', 'elasticnet'],\n",
        "    'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
        "    'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga', 'newton-cholesky'],\n",
        "    'class_weight': ['balanced', None],\n",
        "    'fit_intercept': [True, False],\n",
        "    'tol': [1e-4, 1e-3, 1e-2],\n",
        "}"
      ],
      "metadata": {
        "id": "eOtNP4Bi7YdP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid_search_smote = GridSearchCV(LogisticRegression(), param_grid = param_grid, cv=5, scoring=scorer, verbose=True, n_jobs=-1)"
      ],
      "metadata": {
        "id": "mPimRn6r89DO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid_search_smote.fit(X_resampled, y_resampled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2562
        },
        "id": "RgGRcoZk9CO3",
        "outputId": "a455d080-2142-4f40-fa5b-f26d28e11067"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 4536 candidates, totalling 22680 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:528: FitFailedWarning: \n",
            "12600 fits failed out of a total of 22680.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "1260 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1389, in wrapper\n",
            "    return fit_method(estimator, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 1193, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 63, in _check_solver\n",
            "    raise ValueError(\n",
            "ValueError: Solver newton-cg supports only 'l2' or None penalties, got l1 penalty.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "1260 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1389, in wrapper\n",
            "    return fit_method(estimator, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 1193, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 63, in _check_solver\n",
            "    raise ValueError(\n",
            "ValueError: Solver lbfgs supports only 'l2' or None penalties, got l1 penalty.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "1260 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1389, in wrapper\n",
            "    return fit_method(estimator, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 1193, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 63, in _check_solver\n",
            "    raise ValueError(\n",
            "ValueError: Solver sag supports only 'l2' or None penalties, got l1 penalty.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "1260 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1389, in wrapper\n",
            "    return fit_method(estimator, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 1193, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 63, in _check_solver\n",
            "    raise ValueError(\n",
            "ValueError: Solver newton-cholesky supports only 'l2' or None penalties, got l1 penalty.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "1260 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1389, in wrapper\n",
            "    return fit_method(estimator, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 1193, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 63, in _check_solver\n",
            "    raise ValueError(\n",
            "ValueError: Solver newton-cg supports only 'l2' or None penalties, got elasticnet penalty.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "1260 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1389, in wrapper\n",
            "    return fit_method(estimator, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 1193, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 63, in _check_solver\n",
            "    raise ValueError(\n",
            "ValueError: Solver lbfgs supports only 'l2' or None penalties, got elasticnet penalty.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "1260 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1389, in wrapper\n",
            "    return fit_method(estimator, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 1193, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 71, in _check_solver\n",
            "    raise ValueError(\n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "1260 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1389, in wrapper\n",
            "    return fit_method(estimator, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 1193, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 63, in _check_solver\n",
            "    raise ValueError(\n",
            "ValueError: Solver sag supports only 'l2' or None penalties, got elasticnet penalty.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "1260 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1389, in wrapper\n",
            "    return fit_method(estimator, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 1203, in fit\n",
            "    raise ValueError(\"l1_ratio must be specified when penalty is elasticnet.\")\n",
            "ValueError: l1_ratio must be specified when penalty is elasticnet.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "1260 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1389, in wrapper\n",
            "    return fit_method(estimator, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 1193, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 63, in _check_solver\n",
            "    raise ValueError(\n",
            "ValueError: Solver newton-cholesky supports only 'l2' or None penalties, got elasticnet penalty.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:1107: UserWarning: One or more of the test scores are non-finite: [nan nan nan ... nan nan nan]\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=LogisticRegression(), n_jobs=-1,\n",
              "             param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
              "                         'class_weight': ['balanced', None],\n",
              "                         'fit_intercept': [True, False],\n",
              "                         'max_iter': [700, 1000, 1500],\n",
              "                         'penalty': ['l1', 'l2', 'elasticnet'],\n",
              "                         'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag',\n",
              "                                    'saga', 'newton-cholesky'],\n",
              "                         'tol': [0.0001, 0.001, 0.01]},\n",
              "             scoring=make_scorer(f1_score, response_method='predict', pos_label=1),\n",
              "             verbose=True)"
            ],
            "text/html": [
              "<style>#sk-container-id-11 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-11 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-11 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-11 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-11 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-11 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-11 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-11 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-11 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-11 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-11 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-11 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-11 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-11 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(), n_jobs=-1,\n",
              "             param_grid={&#x27;C&#x27;: [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
              "                         &#x27;class_weight&#x27;: [&#x27;balanced&#x27;, None],\n",
              "                         &#x27;fit_intercept&#x27;: [True, False],\n",
              "                         &#x27;max_iter&#x27;: [700, 1000, 1500],\n",
              "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;],\n",
              "                         &#x27;solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;sag&#x27;,\n",
              "                                    &#x27;saga&#x27;, &#x27;newton-cholesky&#x27;],\n",
              "                         &#x27;tol&#x27;: [0.0001, 0.001, 0.01]},\n",
              "             scoring=make_scorer(f1_score, response_method=&#x27;predict&#x27;, pos_label=1),\n",
              "             verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(), n_jobs=-1,\n",
              "             param_grid={&#x27;C&#x27;: [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
              "                         &#x27;class_weight&#x27;: [&#x27;balanced&#x27;, None],\n",
              "                         &#x27;fit_intercept&#x27;: [True, False],\n",
              "                         &#x27;max_iter&#x27;: [700, 1000, 1500],\n",
              "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;],\n",
              "                         &#x27;solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;sag&#x27;,\n",
              "                                    &#x27;saga&#x27;, &#x27;newton-cholesky&#x27;],\n",
              "                         &#x27;tol&#x27;: [0.0001, 0.001, 0.01]},\n",
              "             scoring=make_scorer(f1_score, response_method=&#x27;predict&#x27;, pos_label=1),\n",
              "             verbose=True)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: LogisticRegression</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=1, fit_intercept=False, max_iter=700, penalty=&#x27;l1&#x27;,\n",
              "                   solver=&#x27;liblinear&#x27;, tol=0.01)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=1, fit_intercept=False, max_iter=700, penalty=&#x27;l1&#x27;,\n",
              "                   solver=&#x27;liblinear&#x27;, tol=0.01)</pre></div> </div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Best Hyperparameters:\", grid_search.best_params_)\n",
        "print(\"Best F1 Score:\", grid_search.best_score_)\n",
        "\n",
        "# Evaluate model\n",
        "best_model = grid_search_smote.best_estimator_\n",
        "y_pred_best_model_smote = best_model.predict(X_test)\n",
        "print(classification_report(y_test, y_pred_best_model_smote))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WWomB1Tx9FZh",
        "outputId": "783953cd-3408-4ff2-8c7d-38c8639d1d35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Hyperparameters: {'C': 1, 'class_weight': 'balanced', 'penalty': 'l2', 'solver': 'lbfgs'}\n",
            "Best F1 Score: 0.8952796329153803\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.96      0.95      0.96       327\n",
            "         1.0       0.90      0.91      0.90       150\n",
            "\n",
            "    accuracy                           0.94       477\n",
            "   macro avg       0.93      0.93      0.93       477\n",
            "weighted avg       0.94      0.94      0.94       477\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_model.fit(X_resampled, y_resampled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 97
        },
        "id": "EXCW0VUC_laR",
        "outputId": "61e91cca-7a84-4999-e75e-48623ca975ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1, fit_intercept=False, max_iter=700, penalty='l1',\n",
              "                   solver='liblinear', tol=0.01)"
            ],
            "text/html": [
              "<style>#sk-container-id-12 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-12 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-12 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-12 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-12 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-12 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-12 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-12 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-12 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-12 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-12 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-12 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-12 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-12 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=1, fit_intercept=False, max_iter=700, penalty=&#x27;l1&#x27;,\n",
              "                   solver=&#x27;liblinear&#x27;, tol=0.01)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" checked><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=1, fit_intercept=False, max_iter=700, penalty=&#x27;l1&#x27;,\n",
              "                   solver=&#x27;liblinear&#x27;, tol=0.01)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 217
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_best_model_smote = best_model.predict(X_test)\n",
        "print(classification_report(y_test, y_pred_best_model_smote))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HHh8fVIhJGhe",
        "outputId": "85c458ba-2883-40c8-8d2c-ba70dd822771"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.96      0.95      0.95       327\n",
            "         1.0       0.89      0.91      0.90       150\n",
            "\n",
            "    accuracy                           0.94       477\n",
            "   macro avg       0.92      0.93      0.93       477\n",
            "weighted avg       0.94      0.94      0.94       477\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weights = best_model.coef_"
      ],
      "metadata": {
        "id": "R84VGjEkJYAO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = pd.DataFrame(X_train)"
      ],
      "metadata": {
        "id": "KdIyCaPmJj0p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.columns = data_final.drop(columns=['Churn']).columns"
      ],
      "metadata": {
        "id": "wX9zUpZcK0Qv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weights = best_model.coef_.flatten()  # Flatten to make it a 1D array if binary classification\n",
        "\n",
        "# Create a DataFrame for better readability\n",
        "feature_imps = pd.DataFrame(weights, columns=['Weight'], index=X_train.columns)\n",
        "\n",
        "# Optionally, you can sort the DataFrame by the absolute value of the weights to highlight important features\n",
        "feature_imps_sorted = feature_imps.reindex(feature_imps['Weight'].abs().sort_values(ascending=False).index)\n",
        "\n",
        "# Display the feature importance sorted by the absolute value of the weights\n",
        "print(feature_imps_sorted)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vFdH3N4OJuHC",
        "outputId": "79740452-b726-43b7-9eaa-fa3589a6f1cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            Weight\n",
            "Total Business Value      6.409520\n",
            "Year                      4.244082\n",
            "LastWorkingDateTimestamp -1.809110\n",
            "rating_increase           1.407269\n",
            "start_age                -0.646491\n",
            "end_age                   0.628567\n",
            "Grade                     0.536758\n",
            "DayOfYear                 0.493977\n",
            "Quarter                   0.253398\n",
            "Weekday                   0.180317\n",
            "Month                     0.160584\n",
            "IsWeekend                -0.131334\n",
            "Joining Designation      -0.116770\n",
            "Day                       0.106829\n",
            "Gender                   -0.092528\n",
            "City_encoded             -0.031967\n",
            "Education_Level           0.004891\n",
            "Income                    0.004748\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "# Check number of CPU cores\n",
        "num_cores = os.cpu_count()\n",
        "print(f\"Number of CPU cores available: {num_cores}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_zM5-RqRKnuj",
        "outputId": "2b25700c-ae2b-4a59-ceac-543e37679243"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of CPU cores available: 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Utilising the gaussian discriminant analysis algorithm..."
      ],
      "metadata": {
        "id": "naNwpZmMMuEI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "class GDA:\n",
        "    def __init__(self):\n",
        "        self.phi = None\n",
        "        self.mu_0 = None\n",
        "        self.mu_1 = None\n",
        "        self.sigma = None\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        n = X.shape[0]\n",
        "        self.phi = np.mean(y)\n",
        "        self.mu_0 = np.mean(X[y == 0], axis=0)\n",
        "        self.mu_1 = np.mean(X[y == 1], axis=0)\n",
        "        self.sigma = np.cov(X.T, aweights=(y == 0) * 1 + (y == 1) * 1, bias=True)\n",
        "\n",
        "    def predict(self, X):\n",
        "        sigma_inv = np.linalg.inv(self.sigma)\n",
        "        diff = self.mu_1 - self.mu_0\n",
        "        decision_boundary = (X @ sigma_inv @ diff -\n",
        "                             0.5 * (self.mu_1.T @ sigma_inv @ self.mu_1 -\n",
        "                                    self.mu_0.T @ sigma_inv @ self.mu_0) +\n",
        "                             np.log(self.phi / (1 - self.phi)))\n",
        "        return (decision_boundary > 0).astype(int)"
      ],
      "metadata": {
        "id": "I605JtW8MtlL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gda = GDA()\n",
        "gda.fit(X_resampled, y_resampled)\n",
        "y_gda_predict = gda.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o59sfTddMJ9L",
        "outputId": "547c2132-4d0c-45be-eb94-debe79236706"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.93      0.87      0.90       327\n",
            "         1.0       0.75      0.85      0.80       150\n",
            "\n",
            "    accuracy                           0.87       477\n",
            "   macro avg       0.84      0.86      0.85       477\n",
            "weighted avg       0.87      0.87      0.87       477\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# utilising an SVM..."
      ],
      "metadata": {
        "id": "e63wPJGVrnzo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "7TebU5Ysrhtk",
        "outputId": "26ddd3a5-dad4-4b53-a5b0-c9cd9d90ad8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Gender  Education_Level    Income  Joining Designation  \\\n",
              "0    -0.833037        -1.241468  1.003180             1.386445   \n",
              "1    -0.833037        -1.241468 -0.734300             0.202151   \n",
              "2    -0.833037        -1.241468 -0.223283            -0.982143   \n",
              "3     1.200427         1.214381 -0.888988            -0.982143   \n",
              "4    -0.833037        -1.241468  1.095323            -0.982143   \n",
              "...        ...              ...       ...                  ...   \n",
              "1899 -0.833037        -0.013543  0.139961             0.202151   \n",
              "1900  1.200427        -0.013543 -0.234430            -0.982143   \n",
              "1901  1.200427        -0.013543 -1.479554            -0.982143   \n",
              "1902 -0.833037        -0.013543 -0.988927            -0.982143   \n",
              "1903 -0.833037         1.214381  0.611751             0.202151   \n",
              "\n",
              "      Total Business Value     Grade  start_age   end_age  rating_increase  \\\n",
              "0                -0.507968  0.976260  -0.890669 -0.964094         0.046631   \n",
              "1                -0.457571 -0.092581   0.134210  0.034710         0.046631   \n",
              "2                -0.507968 -0.092581  -1.061482 -1.130562         0.046631   \n",
              "3                -0.130762 -1.161422  -0.719855 -0.631160         1.103597   \n",
              "4                 1.912452  0.976260   2.013155  2.198786        -1.010336   \n",
              "...                    ...       ...        ...       ...              ...   \n",
              "1899             -0.507968 -0.092581  -1.061482 -1.130562         0.046631   \n",
              "1900             -0.375267 -1.161422  -1.232295 -1.297029        -1.010336   \n",
              "1901             -0.456252 -1.161422   0.988276  0.867047         0.046631   \n",
              "1902             -0.507968 -1.161422  -0.378229 -0.464692         0.046631   \n",
              "1903             -0.447950 -0.092581  -0.549042 -0.464692         0.046631   \n",
              "\n",
              "          Year     Month       Day   Weekday  DayOfYear   Quarter  IsWeekend  \\\n",
              "0     0.910871  1.149725  0.773468 -1.612440   1.230932  1.169563  -0.671729   \n",
              "1     0.292583  1.149725 -0.079396  0.355879   1.136783  1.169563  -0.671729   \n",
              "2    -0.943994 -1.735701 -0.612436  0.355879  -1.771363 -1.810163  -0.671729   \n",
              "3     0.292583  1.149725  0.560252 -0.136201   1.199549  1.169563  -0.671729   \n",
              "4    -2.180571 -0.132687  0.773468 -0.136201  -0.066227  0.176321  -0.671729   \n",
              "...        ...       ...       ...       ...        ...       ...        ...   \n",
              "1899  0.910871  1.470328 -0.079396 -1.120361   1.461073  1.169563  -0.671729   \n",
              "1900 -0.325706 -0.773893  1.306508 -1.612440  -0.652041 -0.816921  -0.671729   \n",
              "1901 -0.325706  0.508519  1.413116  0.847959   0.645118  0.176321   1.488696   \n",
              "1902  0.910871  1.470328 -0.932260 -1.612440   1.377385  1.169563  -0.671729   \n",
              "1903  0.910871 -0.132687 -0.505828  0.847959  -0.181298  0.176321   1.488696   \n",
              "\n",
              "      LastWorkingDateTimestamp  City_encoded  \n",
              "0                     0.002362      0.049266  \n",
              "1                     0.519664      3.445319  \n",
              "2                    -1.715175     -0.516742  \n",
              "3                     0.002362     -0.156555  \n",
              "4                     0.002362     -0.156555  \n",
              "...                        ...           ...  \n",
              "1899                  0.002362     -0.413832  \n",
              "1900                 -0.749365     -0.465287  \n",
              "1901                 -1.030124     -0.465287  \n",
              "1902                  0.002362      3.445319  \n",
              "1903                  1.682005     -0.208010  \n",
              "\n",
              "[1904 rows x 18 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-34052dcb-7c47-4424-aac9-640e9624645b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Education_Level</th>\n",
              "      <th>Income</th>\n",
              "      <th>Joining Designation</th>\n",
              "      <th>Total Business Value</th>\n",
              "      <th>Grade</th>\n",
              "      <th>start_age</th>\n",
              "      <th>end_age</th>\n",
              "      <th>rating_increase</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Weekday</th>\n",
              "      <th>DayOfYear</th>\n",
              "      <th>Quarter</th>\n",
              "      <th>IsWeekend</th>\n",
              "      <th>LastWorkingDateTimestamp</th>\n",
              "      <th>City_encoded</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.833037</td>\n",
              "      <td>-1.241468</td>\n",
              "      <td>1.003180</td>\n",
              "      <td>1.386445</td>\n",
              "      <td>-0.507968</td>\n",
              "      <td>0.976260</td>\n",
              "      <td>-0.890669</td>\n",
              "      <td>-0.964094</td>\n",
              "      <td>0.046631</td>\n",
              "      <td>0.910871</td>\n",
              "      <td>1.149725</td>\n",
              "      <td>0.773468</td>\n",
              "      <td>-1.612440</td>\n",
              "      <td>1.230932</td>\n",
              "      <td>1.169563</td>\n",
              "      <td>-0.671729</td>\n",
              "      <td>0.002362</td>\n",
              "      <td>0.049266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.833037</td>\n",
              "      <td>-1.241468</td>\n",
              "      <td>-0.734300</td>\n",
              "      <td>0.202151</td>\n",
              "      <td>-0.457571</td>\n",
              "      <td>-0.092581</td>\n",
              "      <td>0.134210</td>\n",
              "      <td>0.034710</td>\n",
              "      <td>0.046631</td>\n",
              "      <td>0.292583</td>\n",
              "      <td>1.149725</td>\n",
              "      <td>-0.079396</td>\n",
              "      <td>0.355879</td>\n",
              "      <td>1.136783</td>\n",
              "      <td>1.169563</td>\n",
              "      <td>-0.671729</td>\n",
              "      <td>0.519664</td>\n",
              "      <td>3.445319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.833037</td>\n",
              "      <td>-1.241468</td>\n",
              "      <td>-0.223283</td>\n",
              "      <td>-0.982143</td>\n",
              "      <td>-0.507968</td>\n",
              "      <td>-0.092581</td>\n",
              "      <td>-1.061482</td>\n",
              "      <td>-1.130562</td>\n",
              "      <td>0.046631</td>\n",
              "      <td>-0.943994</td>\n",
              "      <td>-1.735701</td>\n",
              "      <td>-0.612436</td>\n",
              "      <td>0.355879</td>\n",
              "      <td>-1.771363</td>\n",
              "      <td>-1.810163</td>\n",
              "      <td>-0.671729</td>\n",
              "      <td>-1.715175</td>\n",
              "      <td>-0.516742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.200427</td>\n",
              "      <td>1.214381</td>\n",
              "      <td>-0.888988</td>\n",
              "      <td>-0.982143</td>\n",
              "      <td>-0.130762</td>\n",
              "      <td>-1.161422</td>\n",
              "      <td>-0.719855</td>\n",
              "      <td>-0.631160</td>\n",
              "      <td>1.103597</td>\n",
              "      <td>0.292583</td>\n",
              "      <td>1.149725</td>\n",
              "      <td>0.560252</td>\n",
              "      <td>-0.136201</td>\n",
              "      <td>1.199549</td>\n",
              "      <td>1.169563</td>\n",
              "      <td>-0.671729</td>\n",
              "      <td>0.002362</td>\n",
              "      <td>-0.156555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.833037</td>\n",
              "      <td>-1.241468</td>\n",
              "      <td>1.095323</td>\n",
              "      <td>-0.982143</td>\n",
              "      <td>1.912452</td>\n",
              "      <td>0.976260</td>\n",
              "      <td>2.013155</td>\n",
              "      <td>2.198786</td>\n",
              "      <td>-1.010336</td>\n",
              "      <td>-2.180571</td>\n",
              "      <td>-0.132687</td>\n",
              "      <td>0.773468</td>\n",
              "      <td>-0.136201</td>\n",
              "      <td>-0.066227</td>\n",
              "      <td>0.176321</td>\n",
              "      <td>-0.671729</td>\n",
              "      <td>0.002362</td>\n",
              "      <td>-0.156555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1899</th>\n",
              "      <td>-0.833037</td>\n",
              "      <td>-0.013543</td>\n",
              "      <td>0.139961</td>\n",
              "      <td>0.202151</td>\n",
              "      <td>-0.507968</td>\n",
              "      <td>-0.092581</td>\n",
              "      <td>-1.061482</td>\n",
              "      <td>-1.130562</td>\n",
              "      <td>0.046631</td>\n",
              "      <td>0.910871</td>\n",
              "      <td>1.470328</td>\n",
              "      <td>-0.079396</td>\n",
              "      <td>-1.120361</td>\n",
              "      <td>1.461073</td>\n",
              "      <td>1.169563</td>\n",
              "      <td>-0.671729</td>\n",
              "      <td>0.002362</td>\n",
              "      <td>-0.413832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1900</th>\n",
              "      <td>1.200427</td>\n",
              "      <td>-0.013543</td>\n",
              "      <td>-0.234430</td>\n",
              "      <td>-0.982143</td>\n",
              "      <td>-0.375267</td>\n",
              "      <td>-1.161422</td>\n",
              "      <td>-1.232295</td>\n",
              "      <td>-1.297029</td>\n",
              "      <td>-1.010336</td>\n",
              "      <td>-0.325706</td>\n",
              "      <td>-0.773893</td>\n",
              "      <td>1.306508</td>\n",
              "      <td>-1.612440</td>\n",
              "      <td>-0.652041</td>\n",
              "      <td>-0.816921</td>\n",
              "      <td>-0.671729</td>\n",
              "      <td>-0.749365</td>\n",
              "      <td>-0.465287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1901</th>\n",
              "      <td>1.200427</td>\n",
              "      <td>-0.013543</td>\n",
              "      <td>-1.479554</td>\n",
              "      <td>-0.982143</td>\n",
              "      <td>-0.456252</td>\n",
              "      <td>-1.161422</td>\n",
              "      <td>0.988276</td>\n",
              "      <td>0.867047</td>\n",
              "      <td>0.046631</td>\n",
              "      <td>-0.325706</td>\n",
              "      <td>0.508519</td>\n",
              "      <td>1.413116</td>\n",
              "      <td>0.847959</td>\n",
              "      <td>0.645118</td>\n",
              "      <td>0.176321</td>\n",
              "      <td>1.488696</td>\n",
              "      <td>-1.030124</td>\n",
              "      <td>-0.465287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1902</th>\n",
              "      <td>-0.833037</td>\n",
              "      <td>-0.013543</td>\n",
              "      <td>-0.988927</td>\n",
              "      <td>-0.982143</td>\n",
              "      <td>-0.507968</td>\n",
              "      <td>-1.161422</td>\n",
              "      <td>-0.378229</td>\n",
              "      <td>-0.464692</td>\n",
              "      <td>0.046631</td>\n",
              "      <td>0.910871</td>\n",
              "      <td>1.470328</td>\n",
              "      <td>-0.932260</td>\n",
              "      <td>-1.612440</td>\n",
              "      <td>1.377385</td>\n",
              "      <td>1.169563</td>\n",
              "      <td>-0.671729</td>\n",
              "      <td>0.002362</td>\n",
              "      <td>3.445319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1903</th>\n",
              "      <td>-0.833037</td>\n",
              "      <td>1.214381</td>\n",
              "      <td>0.611751</td>\n",
              "      <td>0.202151</td>\n",
              "      <td>-0.447950</td>\n",
              "      <td>-0.092581</td>\n",
              "      <td>-0.549042</td>\n",
              "      <td>-0.464692</td>\n",
              "      <td>0.046631</td>\n",
              "      <td>0.910871</td>\n",
              "      <td>-0.132687</td>\n",
              "      <td>-0.505828</td>\n",
              "      <td>0.847959</td>\n",
              "      <td>-0.181298</td>\n",
              "      <td>0.176321</td>\n",
              "      <td>1.488696</td>\n",
              "      <td>1.682005</td>\n",
              "      <td>-0.208010</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1904 rows × 18 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-34052dcb-7c47-4424-aac9-640e9624645b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-34052dcb-7c47-4424-aac9-640e9624645b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-34052dcb-7c47-4424-aac9-640e9624645b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-83839011-bcf7-4db7-b3a3-ee9435a0912b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_20019a82-cf26-4893-8deb-1b73ec562525\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X_train')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_train",
              "summary": "{\n  \"name\": \"X_train\",\n  \"rows\": 1904,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294082,\n        \"min\": -0.8330367206407789,\n        \"max\": 1.2004272743592763,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.2004272743592763,\n          -0.8330367206407789\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education_Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294267,\n        \"min\": -1.2414676640148907,\n        \"max\": 1.2143810968000204,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -1.2414676640148907,\n          1.2143810968000204\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294238,\n        \"min\": -1.717530206083195,\n        \"max\": 4.550104738451364,\n        \"num_unique_values\": 1878,\n        \"samples\": [\n          -1.3564385744029785,\n          -0.048028197398364406\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Joining Designation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000262708529436,\n        \"min\": -0.9821431461155318,\n        \"max\": 3.7550336751738227,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.20215105920680682,\n          2.570739469851484\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total Business Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294225,\n        \"min\": -0.6577579530021879,\n        \"max\": 9.798304773837874,\n        \"num_unique_values\": 1310,\n        \"samples\": [\n          -0.40848147217259345,\n          0.6352189234554552\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294156,\n        \"min\": -1.1614222233958504,\n        \"max\": 3.113942985943083,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          2.0451016836083498,\n          0.041024241730724616\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294262,\n        \"min\": -2.0863608302553436,\n        \"max\": 3.550473693592426,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          2.5255946892564682,\n          1.5007156849205099\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294222,\n        \"min\": -2.129365955565276,\n        \"max\": 3.5305246007670457,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          0.7005793226008847,\n          1.1999814305125602\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating_increase\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000262708529408,\n        \"min\": -3.124268457501651,\n        \"max\": 3.2175302025017,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.04663087250002464,\n          1.1035973158339165\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294238,\n        \"min\": -3.4171479850220763,\n        \"max\": 0.9108714338103572,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.2925829454057238,\n          -1.5622825198081762\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294258,\n        \"min\": -2.0563044486027513,\n        \"max\": 1.470328401997971,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.4532895165115139,\n          0.5085194427432286\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294271,\n        \"min\": -1.5719082370786281,\n        \"max\": 1.6263320743102454,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          -0.7190441540415953,\n          -0.9322601748008534\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weekday\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294207,\n        \"min\": -1.612440423168841,\n        \"max\": 1.3400390437779195,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          -1.612440423168841,\n          0.35587922146233264\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DayOfYear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294225,\n        \"min\": -2.1898014426619055,\n        \"max\": 1.6179870507005225,\n        \"num_unique_values\": 331,\n        \"samples\": [\n          0.44635982197362156,\n          -1.834128891084096\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quarter\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000262708529427,\n        \"min\": -1.810162587470438,\n        \"max\": 1.1695632625673547,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -1.810162587470438,\n          -0.8169206374578404\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IsWeekend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294378,\n        \"min\": -0.6717287489717273,\n        \"max\": 1.488696146369774,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.488696146369774,\n          -0.6717287489717273\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LastWorkingDateTimestamp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294267,\n        \"min\": -1.9959341082257744,\n        \"max\": 2.0919126247779776,\n        \"num_unique_values\": 463,\n        \"samples\": [\n          0.008683039689527158,\n          -0.5079129760197382\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City_encoded\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294225,\n        \"min\": -1.0827512911887172,\n        \"max\": 3.4453192027898667,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          0.049266332305928764,\n          -0.41383178639642637\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"X_resampled shape: {X_resampled.shape}\")\n",
        "print(f\"y_resampled shape: {y_resampled.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vICMCHJZtDTM",
        "outputId": "54ba7608-84f4-485d-da77-5eb9b048b93a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_resampled shape: (2578, 18)\n",
            "y_resampled shape: (2578,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "pieI0T5jtvMY",
        "outputId": "8a4b6125-132c-4994-911f-25c746d1a489"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Gender  Education_Level    Income  Joining Designation  \\\n",
              "0    -0.833037        -1.241468  1.003180             1.386445   \n",
              "1    -0.833037        -1.241468 -0.734300             0.202151   \n",
              "2    -0.833037        -1.241468 -0.223283            -0.982143   \n",
              "3     1.200427         1.214381 -0.888988            -0.982143   \n",
              "4    -0.833037        -1.241468  1.095323            -0.982143   \n",
              "...        ...              ...       ...                  ...   \n",
              "1899 -0.833037        -0.013543  0.139961             0.202151   \n",
              "1900  1.200427        -0.013543 -0.234430            -0.982143   \n",
              "1901  1.200427        -0.013543 -1.479554            -0.982143   \n",
              "1902 -0.833037        -0.013543 -0.988927            -0.982143   \n",
              "1903 -0.833037         1.214381  0.611751             0.202151   \n",
              "\n",
              "      Total Business Value     Grade  start_age   end_age  rating_increase  \\\n",
              "0                -0.507968  0.976260  -0.890669 -0.964094         0.046631   \n",
              "1                -0.457571 -0.092581   0.134210  0.034710         0.046631   \n",
              "2                -0.507968 -0.092581  -1.061482 -1.130562         0.046631   \n",
              "3                -0.130762 -1.161422  -0.719855 -0.631160         1.103597   \n",
              "4                 1.912452  0.976260   2.013155  2.198786        -1.010336   \n",
              "...                    ...       ...        ...       ...              ...   \n",
              "1899             -0.507968 -0.092581  -1.061482 -1.130562         0.046631   \n",
              "1900             -0.375267 -1.161422  -1.232295 -1.297029        -1.010336   \n",
              "1901             -0.456252 -1.161422   0.988276  0.867047         0.046631   \n",
              "1902             -0.507968 -1.161422  -0.378229 -0.464692         0.046631   \n",
              "1903             -0.447950 -0.092581  -0.549042 -0.464692         0.046631   \n",
              "\n",
              "          Year     Month       Day   Weekday  DayOfYear   Quarter  IsWeekend  \\\n",
              "0     0.910871  1.149725  0.773468 -1.612440   1.230932  1.169563  -0.671729   \n",
              "1     0.292583  1.149725 -0.079396  0.355879   1.136783  1.169563  -0.671729   \n",
              "2    -0.943994 -1.735701 -0.612436  0.355879  -1.771363 -1.810163  -0.671729   \n",
              "3     0.292583  1.149725  0.560252 -0.136201   1.199549  1.169563  -0.671729   \n",
              "4    -2.180571 -0.132687  0.773468 -0.136201  -0.066227  0.176321  -0.671729   \n",
              "...        ...       ...       ...       ...        ...       ...        ...   \n",
              "1899  0.910871  1.470328 -0.079396 -1.120361   1.461073  1.169563  -0.671729   \n",
              "1900 -0.325706 -0.773893  1.306508 -1.612440  -0.652041 -0.816921  -0.671729   \n",
              "1901 -0.325706  0.508519  1.413116  0.847959   0.645118  0.176321   1.488696   \n",
              "1902  0.910871  1.470328 -0.932260 -1.612440   1.377385  1.169563  -0.671729   \n",
              "1903  0.910871 -0.132687 -0.505828  0.847959  -0.181298  0.176321   1.488696   \n",
              "\n",
              "      LastWorkingDateTimestamp  City_encoded  \n",
              "0                     0.002362      0.049266  \n",
              "1                     0.519664      3.445319  \n",
              "2                    -1.715175     -0.516742  \n",
              "3                     0.002362     -0.156555  \n",
              "4                     0.002362     -0.156555  \n",
              "...                        ...           ...  \n",
              "1899                  0.002362     -0.413832  \n",
              "1900                 -0.749365     -0.465287  \n",
              "1901                 -1.030124     -0.465287  \n",
              "1902                  0.002362      3.445319  \n",
              "1903                  1.682005     -0.208010  \n",
              "\n",
              "[1904 rows x 18 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-76f1e270-ac6a-47b5-9849-521b3172d26b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Education_Level</th>\n",
              "      <th>Income</th>\n",
              "      <th>Joining Designation</th>\n",
              "      <th>Total Business Value</th>\n",
              "      <th>Grade</th>\n",
              "      <th>start_age</th>\n",
              "      <th>end_age</th>\n",
              "      <th>rating_increase</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Weekday</th>\n",
              "      <th>DayOfYear</th>\n",
              "      <th>Quarter</th>\n",
              "      <th>IsWeekend</th>\n",
              "      <th>LastWorkingDateTimestamp</th>\n",
              "      <th>City_encoded</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.833037</td>\n",
              "      <td>-1.241468</td>\n",
              "      <td>1.003180</td>\n",
              "      <td>1.386445</td>\n",
              "      <td>-0.507968</td>\n",
              "      <td>0.976260</td>\n",
              "      <td>-0.890669</td>\n",
              "      <td>-0.964094</td>\n",
              "      <td>0.046631</td>\n",
              "      <td>0.910871</td>\n",
              "      <td>1.149725</td>\n",
              "      <td>0.773468</td>\n",
              "      <td>-1.612440</td>\n",
              "      <td>1.230932</td>\n",
              "      <td>1.169563</td>\n",
              "      <td>-0.671729</td>\n",
              "      <td>0.002362</td>\n",
              "      <td>0.049266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.833037</td>\n",
              "      <td>-1.241468</td>\n",
              "      <td>-0.734300</td>\n",
              "      <td>0.202151</td>\n",
              "      <td>-0.457571</td>\n",
              "      <td>-0.092581</td>\n",
              "      <td>0.134210</td>\n",
              "      <td>0.034710</td>\n",
              "      <td>0.046631</td>\n",
              "      <td>0.292583</td>\n",
              "      <td>1.149725</td>\n",
              "      <td>-0.079396</td>\n",
              "      <td>0.355879</td>\n",
              "      <td>1.136783</td>\n",
              "      <td>1.169563</td>\n",
              "      <td>-0.671729</td>\n",
              "      <td>0.519664</td>\n",
              "      <td>3.445319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.833037</td>\n",
              "      <td>-1.241468</td>\n",
              "      <td>-0.223283</td>\n",
              "      <td>-0.982143</td>\n",
              "      <td>-0.507968</td>\n",
              "      <td>-0.092581</td>\n",
              "      <td>-1.061482</td>\n",
              "      <td>-1.130562</td>\n",
              "      <td>0.046631</td>\n",
              "      <td>-0.943994</td>\n",
              "      <td>-1.735701</td>\n",
              "      <td>-0.612436</td>\n",
              "      <td>0.355879</td>\n",
              "      <td>-1.771363</td>\n",
              "      <td>-1.810163</td>\n",
              "      <td>-0.671729</td>\n",
              "      <td>-1.715175</td>\n",
              "      <td>-0.516742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.200427</td>\n",
              "      <td>1.214381</td>\n",
              "      <td>-0.888988</td>\n",
              "      <td>-0.982143</td>\n",
              "      <td>-0.130762</td>\n",
              "      <td>-1.161422</td>\n",
              "      <td>-0.719855</td>\n",
              "      <td>-0.631160</td>\n",
              "      <td>1.103597</td>\n",
              "      <td>0.292583</td>\n",
              "      <td>1.149725</td>\n",
              "      <td>0.560252</td>\n",
              "      <td>-0.136201</td>\n",
              "      <td>1.199549</td>\n",
              "      <td>1.169563</td>\n",
              "      <td>-0.671729</td>\n",
              "      <td>0.002362</td>\n",
              "      <td>-0.156555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.833037</td>\n",
              "      <td>-1.241468</td>\n",
              "      <td>1.095323</td>\n",
              "      <td>-0.982143</td>\n",
              "      <td>1.912452</td>\n",
              "      <td>0.976260</td>\n",
              "      <td>2.013155</td>\n",
              "      <td>2.198786</td>\n",
              "      <td>-1.010336</td>\n",
              "      <td>-2.180571</td>\n",
              "      <td>-0.132687</td>\n",
              "      <td>0.773468</td>\n",
              "      <td>-0.136201</td>\n",
              "      <td>-0.066227</td>\n",
              "      <td>0.176321</td>\n",
              "      <td>-0.671729</td>\n",
              "      <td>0.002362</td>\n",
              "      <td>-0.156555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1899</th>\n",
              "      <td>-0.833037</td>\n",
              "      <td>-0.013543</td>\n",
              "      <td>0.139961</td>\n",
              "      <td>0.202151</td>\n",
              "      <td>-0.507968</td>\n",
              "      <td>-0.092581</td>\n",
              "      <td>-1.061482</td>\n",
              "      <td>-1.130562</td>\n",
              "      <td>0.046631</td>\n",
              "      <td>0.910871</td>\n",
              "      <td>1.470328</td>\n",
              "      <td>-0.079396</td>\n",
              "      <td>-1.120361</td>\n",
              "      <td>1.461073</td>\n",
              "      <td>1.169563</td>\n",
              "      <td>-0.671729</td>\n",
              "      <td>0.002362</td>\n",
              "      <td>-0.413832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1900</th>\n",
              "      <td>1.200427</td>\n",
              "      <td>-0.013543</td>\n",
              "      <td>-0.234430</td>\n",
              "      <td>-0.982143</td>\n",
              "      <td>-0.375267</td>\n",
              "      <td>-1.161422</td>\n",
              "      <td>-1.232295</td>\n",
              "      <td>-1.297029</td>\n",
              "      <td>-1.010336</td>\n",
              "      <td>-0.325706</td>\n",
              "      <td>-0.773893</td>\n",
              "      <td>1.306508</td>\n",
              "      <td>-1.612440</td>\n",
              "      <td>-0.652041</td>\n",
              "      <td>-0.816921</td>\n",
              "      <td>-0.671729</td>\n",
              "      <td>-0.749365</td>\n",
              "      <td>-0.465287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1901</th>\n",
              "      <td>1.200427</td>\n",
              "      <td>-0.013543</td>\n",
              "      <td>-1.479554</td>\n",
              "      <td>-0.982143</td>\n",
              "      <td>-0.456252</td>\n",
              "      <td>-1.161422</td>\n",
              "      <td>0.988276</td>\n",
              "      <td>0.867047</td>\n",
              "      <td>0.046631</td>\n",
              "      <td>-0.325706</td>\n",
              "      <td>0.508519</td>\n",
              "      <td>1.413116</td>\n",
              "      <td>0.847959</td>\n",
              "      <td>0.645118</td>\n",
              "      <td>0.176321</td>\n",
              "      <td>1.488696</td>\n",
              "      <td>-1.030124</td>\n",
              "      <td>-0.465287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1902</th>\n",
              "      <td>-0.833037</td>\n",
              "      <td>-0.013543</td>\n",
              "      <td>-0.988927</td>\n",
              "      <td>-0.982143</td>\n",
              "      <td>-0.507968</td>\n",
              "      <td>-1.161422</td>\n",
              "      <td>-0.378229</td>\n",
              "      <td>-0.464692</td>\n",
              "      <td>0.046631</td>\n",
              "      <td>0.910871</td>\n",
              "      <td>1.470328</td>\n",
              "      <td>-0.932260</td>\n",
              "      <td>-1.612440</td>\n",
              "      <td>1.377385</td>\n",
              "      <td>1.169563</td>\n",
              "      <td>-0.671729</td>\n",
              "      <td>0.002362</td>\n",
              "      <td>3.445319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1903</th>\n",
              "      <td>-0.833037</td>\n",
              "      <td>1.214381</td>\n",
              "      <td>0.611751</td>\n",
              "      <td>0.202151</td>\n",
              "      <td>-0.447950</td>\n",
              "      <td>-0.092581</td>\n",
              "      <td>-0.549042</td>\n",
              "      <td>-0.464692</td>\n",
              "      <td>0.046631</td>\n",
              "      <td>0.910871</td>\n",
              "      <td>-0.132687</td>\n",
              "      <td>-0.505828</td>\n",
              "      <td>0.847959</td>\n",
              "      <td>-0.181298</td>\n",
              "      <td>0.176321</td>\n",
              "      <td>1.488696</td>\n",
              "      <td>1.682005</td>\n",
              "      <td>-0.208010</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1904 rows × 18 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_a3af1b5f-a98d-41aa-afed-952654347d12\">\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X_train')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "        google.colab.notebook.generateWithVariable('X_train');\n",
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              "\n",
              "    </div>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
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              "summary": "{\n  \"name\": \"X_train\",\n  \"rows\": 1904,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294082,\n        \"min\": -0.8330367206407789,\n        \"max\": 1.2004272743592763,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.2004272743592763,\n          -0.8330367206407789\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education_Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294267,\n        \"min\": -1.2414676640148907,\n        \"max\": 1.2143810968000204,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -1.2414676640148907,\n          1.2143810968000204\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294238,\n        \"min\": -1.717530206083195,\n        \"max\": 4.550104738451364,\n        \"num_unique_values\": 1878,\n        \"samples\": [\n          -1.3564385744029785,\n          -0.048028197398364406\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Joining Designation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000262708529436,\n        \"min\": -0.9821431461155318,\n        \"max\": 3.7550336751738227,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.20215105920680682,\n          2.570739469851484\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total Business Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294225,\n        \"min\": 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2.5255946892564682,\n          1.5007156849205099\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294222,\n        \"min\": -2.129365955565276,\n        \"max\": 3.5305246007670457,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          0.7005793226008847,\n          1.1999814305125602\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating_increase\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000262708529408,\n        \"min\": -3.124268457501651,\n        \"max\": 3.2175302025017,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.04663087250002464,\n          1.1035973158339165\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294238,\n        \"min\": -3.4171479850220763,\n        \"max\": 0.9108714338103572,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.2925829454057238,\n          -1.5622825198081762\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294258,\n        \"min\": -2.0563044486027513,\n        \"max\": 1.470328401997971,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.4532895165115139,\n          0.5085194427432286\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294271,\n        \"min\": -1.5719082370786281,\n        \"max\": 1.6263320743102454,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          -0.7190441540415953,\n          -0.9322601748008534\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weekday\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294207,\n        \"min\": -1.612440423168841,\n        \"max\": 1.3400390437779195,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          -1.612440423168841,\n          0.35587922146233264\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DayOfYear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294225,\n        \"min\": -2.1898014426619055,\n        \"max\": 1.6179870507005225,\n        \"num_unique_values\": 331,\n        \"samples\": [\n          0.44635982197362156,\n          -1.834128891084096\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quarter\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000262708529427,\n        \"min\": -1.810162587470438,\n        \"max\": 1.1695632625673547,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -1.810162587470438,\n          -0.8169206374578404\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IsWeekend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294378,\n        \"min\": -0.6717287489717273,\n        \"max\": 1.488696146369774,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.488696146369774,\n          -0.6717287489717273\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LastWorkingDateTimestamp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294267,\n        \"min\": -1.9959341082257744,\n        \"max\": 2.0919126247779776,\n        \"num_unique_values\": 463,\n        \"samples\": [\n          0.008683039689527158,\n          -0.5079129760197382\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City_encoded\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0002627085294225,\n        \"min\": -1.0827512911887172,\n        \"max\": 3.4453192027898667,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          0.049266332305928764,\n          -0.41383178639642637\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "rbf_kernel_svm_clf = SVC(kernel=\"rbf\", gamma=5, C=0.001)\n",
        "rbf_kernel_svm_clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "s3sKTJ95i2Vm",
        "outputId": "96a76dc8-9187-4c60-da6f-855a5bb99070"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=0.001, gamma=5)"
            ],
            "text/html": [
              "<style>#sk-container-id-15 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-15 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-15 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-15 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-15 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-15 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-15 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-15 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-15 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-15 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-15 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-15 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-15 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-15 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=0.001, gamma=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" checked><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=0.001, gamma=5)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 255
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_rbf_svm = rbf_kernel_svm_clf.predict(X_test)\n",
        "print(classification_report(y_test, y_pred_rbf_svm))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zxU52q0QtVAv",
        "outputId": "319f39f8-f0dc-4fa0-ef51-122bb39fb143"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.69      1.00      0.81       327\n",
            "         1.0       0.00      0.00      0.00       150\n",
            "\n",
            "    accuracy                           0.69       477\n",
            "   macro avg       0.34      0.50      0.41       477\n",
            "weighted avg       0.47      0.69      0.56       477\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "params = {\n",
        "    'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n",
        "    'C': [0.001, 0.01, 0.1, 1, 10, 100, 150, 200],\n",
        "    'class_weight': ['balanced', None],\n",
        "    'gamma': [1, 0.1, 0.01, 0.001, 'scale', 'auto'],\n",
        "}\n",
        "\n",
        "grid_search_svm = GridSearchCV(SVC(), param_grid = params, cv=3, scoring=scorer, verbose=True, n_jobs=-1)"
      ],
      "metadata": {
        "id": "sekt7_U0tfTu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid_search_svm.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        },
        "id": "BHBYuwpXtWIM",
        "outputId": "4cb05e49-0b2c-46d2-b13a-22a4de5023bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 192 candidates, totalling 576 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3, estimator=SVC(), n_jobs=-1,\n",
              "             param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
              "                         'class_weight': ['balanced', None],\n",
              "                         'gamma': [1, 0.1, 0.01, 0.001],\n",
              "                         'kernel': ['linear', 'poly', 'rbf', 'sigmoid']},\n",
              "             scoring=make_scorer(f1_score, response_method='predict', pos_label=1),\n",
              "             verbose=True)"
            ],
            "text/html": [
              "<style>#sk-container-id-17 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-17 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-17 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-17 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-17 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-17 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-17 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-17 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-17 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-17 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-17 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-17 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-17 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-17 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=SVC(), n_jobs=-1,\n",
              "             param_grid={&#x27;C&#x27;: [0.001, 0.01, 0.1, 1, 10, 100],\n",
              "                         &#x27;class_weight&#x27;: [&#x27;balanced&#x27;, None],\n",
              "                         &#x27;gamma&#x27;: [1, 0.1, 0.01, 0.001],\n",
              "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;]},\n",
              "             scoring=make_scorer(f1_score, response_method=&#x27;predict&#x27;, pos_label=1),\n",
              "             verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=3, estimator=SVC(), n_jobs=-1,\n",
              "             param_grid={&#x27;C&#x27;: [0.001, 0.01, 0.1, 1, 10, 100],\n",
              "                         &#x27;class_weight&#x27;: [&#x27;balanced&#x27;, None],\n",
              "                         &#x27;gamma&#x27;: [1, 0.1, 0.01, 0.001],\n",
              "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;]},\n",
              "             scoring=make_scorer(f1_score, response_method=&#x27;predict&#x27;, pos_label=1),\n",
              "             verbose=True)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: SVC</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=100, gamma=0.01)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=100, gamma=0.01)</pre></div> </div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 262
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Best Hyperparameters:\", grid_search_svm.best_params_)\n",
        "print(\"Best F1 Score:\", grid_search_svm.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CktykSrauoWI",
        "outputId": "4479a462-3bc9-4643-bdf9-e8654671a0fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Hyperparameters: {'C': 100, 'class_weight': None, 'gamma': 0.01, 'kernel': 'rbf'}\n",
            "Best F1 Score: 0.9608549033743442\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_svm_rbf = grid_search_svm.best_estimator_.predict(X_test)\n",
        "print(classification_report(y_test, y_pred_svm_rbf))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CFlNM3_uu0JW",
        "outputId": "39defab6-00d2-433c-eeab-7aeb08fe1145"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.97      0.98      0.98       327\n",
            "         1.0       0.97      0.93      0.95       150\n",
            "\n",
            "    accuracy                           0.97       477\n",
            "   macro avg       0.97      0.96      0.96       477\n",
            "weighted avg       0.97      0.97      0.97       477\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline = Pipeline([\n",
        "    ('svm', SVC())\n",
        "])\n",
        "\n",
        "grid_search_svm_two = GridSearchCV(\n",
        "    estimator=pipeline,\n",
        "    param_grid={\n",
        "        'svm__C': [0.01, 0.1, 100],\n",
        "        'svm__kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n",
        "        'svm__degree': [2, 3, 4, 5],\n",
        "        'svm__gamma': ['scale', 'auto', 0.001, 0.01,],\n",
        "        'svm__coef0': [0.0, 0.1, 0.5, 1.0],\n",
        "        'svm__class_weight': [None, 'balanced'],\n",
        "        'svm__decision_function_shape': ['ovo', 'ovr'],\n",
        "        'svm__tol': [1e-3, 1e-4, 1e-5],\n",
        "    },\n",
        "    scoring=scorer,\n",
        "    cv=3,\n",
        "    verbose=2,\n",
        "    n_jobs=-1\n",
        ")"
      ],
      "metadata": {
        "id": "7Kgdnh31vJH8"
      },
      "execution_count": 273,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid_search_svm_two.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        },
        "id": "uSQTB5T5xroL",
        "outputId": "a4368f2d-39d0-4cb3-cb9a-38e825509b18"
      },
      "execution_count": 274,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 9216 candidates, totalling 27648 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3, estimator=Pipeline(steps=[('svm', SVC())]), n_jobs=-1,\n",
              "             param_grid={'svm__C': [0.01, 0.1, 100],\n",
              "                         'svm__class_weight': [None, 'balanced'],\n",
              "                         'svm__coef0': [0.0, 0.1, 0.5, 1.0],\n",
              "                         'svm__decision_function_shape': ['ovo', 'ovr'],\n",
              "                         'svm__degree': [2, 3, 4, 5],\n",
              "                         'svm__gamma': ['scale', 'auto', 0.001, 0.01],\n",
              "                         'svm__kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n",
              "                         'svm__tol': [0.001, 0.0001, 1e-05]},\n",
              "             scoring=make_scorer(f1_score, response_method='predict', pos_label=1),\n",
              "             verbose=2)"
            ],
            "text/html": [
              "<style>#sk-container-id-18 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-18 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-18 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-18 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-18 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-18 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-18 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-18 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-18 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-18 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-18 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-18 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-18 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-18 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=Pipeline(steps=[(&#x27;svm&#x27;, SVC())]), n_jobs=-1,\n",
              "             param_grid={&#x27;svm__C&#x27;: [0.01, 0.1, 100],\n",
              "                         &#x27;svm__class_weight&#x27;: [None, &#x27;balanced&#x27;],\n",
              "                         &#x27;svm__coef0&#x27;: [0.0, 0.1, 0.5, 1.0],\n",
              "                         &#x27;svm__decision_function_shape&#x27;: [&#x27;ovo&#x27;, &#x27;ovr&#x27;],\n",
              "                         &#x27;svm__degree&#x27;: [2, 3, 4, 5],\n",
              "                         &#x27;svm__gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;, 0.001, 0.01],\n",
              "                         &#x27;svm__kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;],\n",
              "                         &#x27;svm__tol&#x27;: [0.001, 0.0001, 1e-05]},\n",
              "             scoring=make_scorer(f1_score, response_method=&#x27;predict&#x27;, pos_label=1),\n",
              "             verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=3, estimator=Pipeline(steps=[(&#x27;svm&#x27;, SVC())]), n_jobs=-1,\n",
              "             param_grid={&#x27;svm__C&#x27;: [0.01, 0.1, 100],\n",
              "                         &#x27;svm__class_weight&#x27;: [None, &#x27;balanced&#x27;],\n",
              "                         &#x27;svm__coef0&#x27;: [0.0, 0.1, 0.5, 1.0],\n",
              "                         &#x27;svm__decision_function_shape&#x27;: [&#x27;ovo&#x27;, &#x27;ovr&#x27;],\n",
              "                         &#x27;svm__degree&#x27;: [2, 3, 4, 5],\n",
              "                         &#x27;svm__gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;, 0.001, 0.01],\n",
              "                         &#x27;svm__kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;],\n",
              "                         &#x27;svm__tol&#x27;: [0.001, 0.0001, 1e-05]},\n",
              "             scoring=make_scorer(f1_score, response_method=&#x27;predict&#x27;, pos_label=1),\n",
              "             verbose=2)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: Pipeline</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;svm&#x27;,\n",
              "                 SVC(C=100, class_weight=&#x27;balanced&#x27;, coef0=1.0,\n",
              "                     decision_function_shape=&#x27;ovo&#x27;, degree=2, gamma=0.01,\n",
              "                     kernel=&#x27;poly&#x27;))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=100, class_weight=&#x27;balanced&#x27;, coef0=1.0, decision_function_shape=&#x27;ovo&#x27;,\n",
              "    degree=2, gamma=0.01, kernel=&#x27;poly&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 274
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Best Parameters:\", grid_search_svm_two.best_params_)\n",
        "print(\"Best Score:\", grid_search_svm_two.best_score_)"
      ],
      "metadata": {
        "id": "B0HK5ptcxtxu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c296d214-4e5a-4c11-e07a-05048a879bb1"
      },
      "execution_count": 276,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Parameters: {'svm__C': 100, 'svm__class_weight': 'balanced', 'svm__coef0': 1.0, 'svm__decision_function_shape': 'ovo', 'svm__degree': 2, 'svm__gamma': 0.01, 'svm__kernel': 'poly', 'svm__tol': 0.001}\n",
            "Best Score: 0.9636000722161722\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_svm_two = grid_search_svm_two.best_estimator_.predict(X_test)\n",
        "print(classification_report(y_test, y_pred_svm_two))"
      ],
      "metadata": {
        "id": "LfLk43l_yqhs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "578cbf18-2761-4677-bfd7-e9976ed72afd"
      },
      "execution_count": 277,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.97      0.99      0.98       327\n",
            "         1.0       0.97      0.93      0.95       150\n",
            "\n",
            "    accuracy                           0.97       477\n",
            "   macro avg       0.97      0.96      0.97       477\n",
            "weighted avg       0.97      0.97      0.97       477\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "t93ANRnXihUH"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}