06_Chi_Square_test.ipynb

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      "source": [
        "# <center>`Fisher` Score - `Chisquare`  Test For Feature Selection</center>"
      ]
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import os\n",
        "\n",
        "# plt.style.use('ggplot')\n",
        "# sns.set_theme(style='whitegrid')\n",
        "%matplotlib inline"
      ],
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        "Compute chi-squared stats between each non-negative feature and class.\n",
        "\n",
        "- This score should be used to evaluate categorical variables in a classification task.\n",
        "\n",
        "This score can be used to select the n_features features with the highest values for the test chi-squared statistic from X, which must contain only non-negative features such as booleans or frequencies (e.g., term counts in document classification), relative to the classes.\n",
        "\n",
        "Recall that the chi-square test measures dependence between stochastic variables, so using this function “weeds out” the features that are the most likely to be independent of class and therefore irrelevant for classification.\n",
        "The Chi Square statistic is commonly used for testing relationships between categorical variables.\n",
        "\n",
        "It compares the observed distribution of the different classes of target Y among the different categories of the feature, against the expected distribution of the target classes, regardless of the feature categories."
      ]
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              "Index(['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare',\n",
              "       'embarked', 'class', 'who', 'adult_male', 'deck', 'embark_town',\n",
              "       'alive', 'alone'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "df=sns.load_dataset('titanic')\n",
        "df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "| Column      | Description                                                                                       |\n",
        "|-------------|---------------------------------------------------------------------------------------------------|\n",
        "| survived    | Indicates whether a passenger survived (1) or did not survive (0) the Titanic disaster.           |\n",
        "| pclass      | Represents the passenger class or ticket class of a passenger (1st, 2nd, or 3rd class).            |\n",
        "| sex         | Denotes the gender of a passenger (male or female).                                               |\n",
        "| age         | Represents the age of a passenger.                                                                |\n",
        "| sibsp       | Indicates the number of siblings or spouses accompanying a passenger aboard the Titanic.          |\n",
        "| parch       | Represents the number of parents or children accompanying a passenger aboard the Titanic.          |\n",
        "| fare        | Denotes the fare or price paid by a passenger for their ticket.                                   |\n",
        "| embarked    | Represents the port or location where a passenger boarded the Titanic (C = Cherbourg, Q = Queenstown, S = Southampton). |\n",
        "| class       | Indicates the passenger class or ticket class, similar to the `pclass` column.                     |\n",
        "| who         | Represents the category of a passenger (man, woman, or child) based on their age and gender.       |\n",
        "| adult_male  | Indicates whether a passenger is an adult male (True) or not (False).                              |\n",
        "| deck        | Represents the deck or level where a passenger's cabin is located on the Titanic.                  |\n",
        "| embark_town | Denotes the name of the town or city where a passenger boarded the Titanic.                         |\n",
        "| alive       | Indicates whether a passenger survived (yes) or did not survive (no) the Titanic disaster.         |\n",
        "| alone       | Denotes whether a passenger was traveling alone (True) or with others (False).                     |\n"
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              "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
              "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
              "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
              "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
              "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
              "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
              "\n",
              "     who  adult_male deck  embark_town alive  alone  \n",
              "0    man        True  NaN  Southampton    no  False  \n",
              "1  woman       False    C    Cherbourg   yes  False  \n",
              "2  woman       False  NaN  Southampton   yes   True  \n",
              "3  woman       False    C  Southampton   yes  False  \n",
              "4    man        True  NaN  Southampton    no   True  "
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 15 columns):\n",
            " #   Column       Non-Null Count  Dtype   \n",
            "---  ------       --------------  -----   \n",
            " 0   survived     891 non-null    int64   \n",
            " 1   pclass       891 non-null    int64   \n",
            " 2   sex          891 non-null    object  \n",
            " 3   age          714 non-null    float64 \n",
            " 4   sibsp        891 non-null    int64   \n",
            " 5   parch        891 non-null    int64   \n",
            " 6   fare         891 non-null    float64 \n",
            " 7   embarked     889 non-null    object  \n",
            " 8   class        891 non-null    category\n",
            " 9   who          891 non-null    object  \n",
            " 10  adult_male   891 non-null    bool    \n",
            " 11  deck         203 non-null    category\n",
            " 12  embark_town  889 non-null    object  \n",
            " 13  alive        891 non-null    object  \n",
            " 14  alone        891 non-null    bool    \n",
            "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
            "memory usage: 80.7+ KB\n"
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              "      sex embarked  alone  pclass  survived\n",
              "0    male        S  False       3         0\n",
              "1  female        C  False       1         1\n",
              "2  female        S   True       3         1\n",
              "3  female        S  False       1         1\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-28e5c8e7-c07f-4723-82f0-2b1a8acc9a31 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-28e5c8e7-c07f-4723-82f0-2b1a8acc9a31');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "##['sex','embarked','alone','pclass','Survived']\n",
        "df=df[['sex','embarked','alone','pclass','survived']]\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "Yro0PfXLoOsz",
        "outputId": "cf60c2e1-384c-4de1-e0c7-9b09d2f8d91f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sex embarked  alone  pclass  survived\n",
              "0    1        S  False       3         0\n",
              "1    0        C  False       1         1\n",
              "2    0        S   True       3         1\n",
              "3    0        S  False       1         1\n",
              "4    1        S   True       3         0"
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              "\n",
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              "      <th>sex</th>\n",
              "      <th>embarked</th>\n",
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              "      <th>pclass</th>\n",
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              "\n",
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              "          const element = document.querySelector('#df-97f6068a-2ac2-416a-bd22-3d7feec0395a');\n",
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              "            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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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df['sex'] = np.where(df['sex']==\"male\", 1, 0)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "IBb3a2wGoOs0"
      },
      "outputs": [],
      "source": [
        "### let's perform label encoding on embarked\n",
        "ordinal_label = {k: i for i, k in enumerate(df['embarked'].unique(), 0)}\n",
        "df['embarked'] = df['embarked'].map(ordinal_label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "PwCfjh3EoOs0",
        "outputId": "8288806f-e5e8-4858-a18e-4936faef61b4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sex  embarked  alone  pclass  survived\n",
              "0    1         0  False       3         0\n",
              "1    0         1  False       1         1\n",
              "2    0         0   True       3         1\n",
              "3    0         0  False       1         1\n",
              "4    1         0   True       3         0"
            ],
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              "\n",
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              "      <th>embarked</th>\n",
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              "      <th>pclass</th>\n",
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              "              style=\"display:none;\">\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
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              "          const element = document.querySelector('#df-20031652-b39c-4809-8785-8f5774c1e1e8');\n",
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              "            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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          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "VlgJVHLBoOs1"
      },
      "outputs": [],
      "source": [
        "### let's perform label encoding on alone\n",
        "df['alone']=np.where(df['alone']==True, 1, 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "ap2nZtJJoOs1",
        "outputId": "ccfd6c9a-6bbd-43e4-e51d-b78d9a1e4ba1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sex  embarked  alone  pclass  survived\n",
              "0    1         0      0       3         0\n",
              "1    0         1      0       1         1\n",
              "2    0         0      1       3         1\n",
              "3    0         0      0       1         1\n",
              "4    1         0      1       3         0"
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              "      <th>embarked</th>\n",
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              "      <th>0</th>\n",
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              "                                                     [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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "zlw1Ay1poOs1"
      },
      "outputs": [],
      "source": [
        "### train Test split is usually done to avaoid overfitting\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train,X_test,y_train,y_test=train_test_split(df[['sex','embarked','alone','pclass']],\n",
        "                                              df['survived'],test_size=0.3,random_state=100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "L76OUZG4oOs2",
        "outputId": "333ae8fd-2cd4-4f7d-9d88-0375cfb2c568"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sex  embarked  alone  pclass\n",
              "69     1         0      0       3\n",
              "85     0         0      0       3\n",
              "794    1         0      1       3\n",
              "161    0         0      1       2\n",
              "815    1         0      1       1"
            ],
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              "      <th>embarked</th>\n",
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              "\n",
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              "          const element = document.querySelector('#df-56815beb-85b6-4dbb-863b-7eea7a099caa');\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "X_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6MVEGMn5oOs2",
        "outputId": "9252aa71-44aa-4178-c9ab-27ac15a55fb8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "X_train['sex'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_tIev2bJoOs2",
        "outputId": "0ef27bd6-3a19-4a20-ee6c-7f7a786f95da"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex         0\n",
              "embarked    0\n",
              "alone       0\n",
              "pclass      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "X_train.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "AbEQFg1goOs3"
      },
      "outputs": [],
      "source": [
        "## Perform chi2 test\n",
        "### chi2 returns 2 values\n",
        "### Fscore and the pvalue"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.feature_selection import chi2\n",
        "\n",
        "f_scores, p_values = chi2(X_train, y_train)\n",
        "print(f_scores, p_values)\n",
        "\n",
        "p_values = pd.Series(p_values)\n",
        "p_values.index = X_train.columns\n",
        "print(p_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "svH19Vslscmd",
        "outputId": "795a3699-611f-4cb8-8f64-f4cd0f296145"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[65.67929505  7.55053653 10.88471585 21.97994154] [5.30603805e-16 5.99922095e-03 9.69610546e-04 2.75514881e-06]\n",
            "sex         5.306038e-16\n",
            "embarked    5.999221e-03\n",
            "alone       9.696105e-04\n",
            "pclass      2.755149e-06\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XDaNupD9oOs3",
        "outputId": "7908217a-078c-4462-d507-a8bef5e1fea2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex         5.306038e-16\n",
              "pclass      2.755149e-06\n",
              "embarked    5.999221e-03\n",
              "alone       9.696105e-04\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "p_values.sort_index(ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uNkeRZOToOs3"
      },
      "source": [
        "### Observation\n",
        "Sex Column is the most important column when compared to the output feature\n",
        "Survived"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.feature_selection import chi2\n",
        "\n",
        "# Load the Titanic dataset from Seaborn\n",
        "df = sns.load_dataset('titanic')\n",
        "\n",
        "# Select the relevant columns for feature selection\n",
        "df = df[['sex', 'embarked', 'alone', 'pclass', 'survived']]\n",
        "\n",
        "# Perform preprocessing\n",
        "df['sex'] = np.where(df['sex'] == 'male', 1, 0)\n",
        "ordinal_label = {k: i for i, k in enumerate(df['embarked'].unique(), 0)}\n",
        "df['embarked'] = df['embarked'].map(ordinal_label)\n",
        "df['alone'] = np.where(df['alone'] == True, 1, 0)\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(df[['sex', 'embarked', 'alone', 'pclass']], df['survived'], test_size=0.3, random_state=100)\n",
        "\n",
        "# Perform chi2 test\n",
        "f_scores, p_values = chi2(X_train, y_train)\n",
        "\n",
        "# Create a Series to store the p-values and set the column names as index\n",
        "p_values = pd.Series(p_values)\n",
        "p_values.index = X_train.columns\n",
        "\n",
        "# Sort the p-values in descending order\n",
        "sorted_p_values = p_values.sort_index(ascending=False)\n",
        "\n",
        "# Plot the bar graph\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.barh(sorted_p_values.index, sorted_p_values.values)\n",
        "plt.xlabel('p-value')\n",
        "plt.ylabel('Feature')\n",
        "plt.title('Chi2 Test - Sorted p-values')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "4y9U0rQBvXY1",
        "outputId": "35997c3f-95bb-424c-f984-97b96578f641"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**PreProcessing On complete dataset**"
      ],
      "metadata": {
        "id": "s-lFFrlNzYet"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_selection import chi2\n",
        "\n",
        "# Load the Titanic dataset\n",
        "df = sns.load_dataset('titanic')\n",
        "df.columns\n",
        "\n",
        "# Drop columns with missing values\n",
        "df = df.dropna()\n",
        "df = df.drop(['embark_town', 'alive'], axis=1)\n",
        "\n",
        "# Preprocessing using numpy\n",
        "# df['sex'] = np.where(df['sex'] == \"male\", 1, 0)\n",
        "\n",
        "# Columns to encode\n",
        "columns_to_encode = ['sex', 'embarked', 'class', 'who', 'adult_male', 'deck', 'alone']\n",
        "\n",
        "# Creating the label encoder object\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "# Apply LabelEncoder to each column\n",
        "for column in columns_to_encode:\n",
        "    df[column] = label_encoder.fit_transform(df[column])\n",
        "\n",
        "# Normalize age and fare using MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "df[['age', 'fare']] = scaler.fit_transform(df[['age', 'fare']])\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X = df.drop(labels='survived', axis=1)\n",
        "y = df['survived']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "\n",
        "# Perform chi2 test\n",
        "f_scores, p_values = chi2(X_train, y_train)\n",
        "\n",
        "# Create a Series to store the p-values and set the column names as index\n",
        "p_values = pd.Series(p_values)\n",
        "p_values.index = X_train.columns\n",
        "\n",
        "# Sort the p-values in ascending order\n",
        "# sorted_p_values = p_values.sort_index(ascending=True)\n",
        "sorted_p_values = p_values.sort_values(ascending=True)\n",
        "\n",
        "# Plot the bar graph\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.barh(sorted_p_values.index, sorted_p_values.values)\n",
        "plt.xlabel('p-value')\n",
        "plt.ylabel('Feature')\n",
        "plt.title('Chi2 Test - Sorted p-values')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "yvpVqKw21O2N",
        "outputId": "b1cadff1-76e7-4c18-ec2d-85b188f50f2a"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "on complete dataset"
      ],
      "metadata": {
        "id": "_Glr_fLp5NxL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_selection import chi2\n",
        "\n",
        "# Load the Titanic dataset\n",
        "df = sns.load_dataset('titanic')\n",
        "df.columns\n",
        "\n",
        "# Drop columns with missing values\n",
        "df = df.dropna()\n",
        "df = df.drop(['embark_town', 'alive'], axis=1)\n",
        "\n",
        "# Preprocessing using numpy\n",
        "# df['sex'] = np.where(df['sex'] == \"male\", 1, 0)\n",
        "\n",
        "# Columns to encode\n",
        "columns_to_encode = ['sex', 'embarked', 'class', 'who', 'adult_male', 'deck', 'alone']\n",
        "\n",
        "# Creating the label encoder object\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "# Apply LabelEncoder to each column\n",
        "for column in columns_to_encode:\n",
        "    df[column] = label_encoder.fit_transform(df[column])\n",
        "\n",
        "# Normalize age and fare using MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "df[['age', 'fare']] = scaler.fit_transform(df[['age', 'fare']])\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X = df.drop(labels='survived', axis=1)\n",
        "y = df['survived']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "\n",
        "# Perform chi2 test\n",
        "f_scores, p_values = chi2(X, y)\n",
        "\n",
        "# Create a Series to store the p-values and set the column names as index\n",
        "p_values = pd.Series(p_values)\n",
        "p_values.index = X_train.columns\n",
        "\n",
        "# Sort the p-values in ascending order\n",
        "# sorted_p_values = p_values.sort_index(ascending=True)\n",
        "sorted_p_values = p_values.sort_values(ascending=True)\n",
        "\n",
        "# Plot the bar graph\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.barh(sorted_p_values.index, sorted_p_values.values)\n",
        "plt.xlabel('p-value')\n",
        "plt.ylabel('Feature')\n",
        "plt.title('Chi2 Test - Sorted p-values')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "qMUqsdxT5Jcm",
        "outputId": "92e92861-0842-46aa-c237-944cadf0f526"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "without normaliszation"
      ],
      "metadata": {
        "id": "mXUNnAix7RMs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_selection import chi2\n",
        "\n",
        "# Load the Titanic dataset\n",
        "df = sns.load_dataset('titanic')\n",
        "df.columns\n",
        "\n",
        "# Drop columns with missing values\n",
        "df = df.dropna()\n",
        "df = df.drop(['embark_town', 'alive'], axis=1)\n",
        "\n",
        "# Preprocessing using numpy\n",
        "# df['sex'] = np.where(df['sex'] == \"male\", 1, 0)\n",
        "\n",
        "# Columns to encode\n",
        "columns_to_encode = ['sex', 'embarked', 'class', 'who', 'adult_male', 'deck', 'alone']\n",
        "\n",
        "# Creating the label encoder object\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "# Apply LabelEncoder to each column\n",
        "for column in columns_to_encode:\n",
        "    df[column] = label_encoder.fit_transform(df[column])\n",
        "\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X = df.drop(labels='survived', axis=1)\n",
        "y = df['survived']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "\n",
        "# Perform chi2 test\n",
        "f_scores, p_values = chi2(X, y)\n",
        "\n",
        "# Create a Series to store the p-values and set the column names as index\n",
        "p_values = pd.Series(p_values)\n",
        "p_values.index = X_train.columns\n",
        "\n",
        "# Sort the p-values in ascending order\n",
        "# sorted_p_values = p_values.sort_index(ascending=True)\n",
        "sorted_p_values = p_values.sort_values(ascending=True)\n",
        "\n",
        "# Plot the bar graph\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.barh(sorted_p_values.index, sorted_p_values.values)\n",
        "plt.xlabel('p-value')\n",
        "plt.ylabel('Feature')\n",
        "plt.title('Chi2 Test - Sorted p-values')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "VUf8zJlx7Lim",
        "outputId": "b04fa799-9946-430d-aa1e-09f8cdaa1f56"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_selection import SelectKBest, mutual_info_classif\n",
        "\n",
        "# Load the Titanic dataset\n",
        "df = sns.load_dataset('titanic')\n",
        "df.columns\n",
        "\n",
        "# Drop columns with missing values\n",
        "df = df.dropna()\n",
        "df = df.drop(['embark_town', 'alive'], axis=1)\n",
        "\n",
        "# Preprocessing using numpy\n",
        "# df['sex'] = np.where(df['sex'] == \"male\", 1, 0)\n",
        "\n",
        "# Columns to encode\n",
        "columns_to_encode = ['sex', 'embarked', 'class', 'who', 'adult_male', 'deck', 'alone']\n",
        "\n",
        "# Creating the label encoder object\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "# Apply LabelEncoder to each column\n",
        "for column in columns_to_encode:\n",
        "    df[column] = label_encoder.fit_transform(df[column])\n",
        "\n",
        "# Normalize age and fare using MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "df[['age', 'fare']] = scaler.fit_transform(df[['age', 'fare']])\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X = df.drop(labels='survived', axis=1)\n",
        "y = df['survived']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "\n",
        "# Perform mutual information classification\n",
        "mutual_info = mutual_info_classif(X_train, y_train)\n",
        "\n",
        "# Create a Series to store the mutual information scores and set the column names as index\n",
        "mutual_info_scores = pd.Series(mutual_info)\n",
        "mutual_info_scores.index = X_train.columns\n",
        "\n",
        "# Sort the mutual information scores in descending order\n",
        "sorted_mutual_info = mutual_info_scores.sort_values(ascending=False)\n",
        "\n",
        "# Use SelectKBest to select the top k features based on mutual information\n",
        "k = 5  # Number of top features to select\n",
        "selector = SelectKBest(mutual_info_classif, k=k)\n",
        "selector.fit(X_train, y_train)\n",
        "\n",
        "# Get the indices of the selected features\n",
        "selected_feature_indices = selector.get_support(indices=True)\n",
        "\n",
        "# Get the names of the selected features\n",
        "selected_features = X_train.columns[selected_feature_indices]\n",
        "\n",
        "# Plot the bar graph\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.barh(selected_features, sorted_mutual_info[selected_features])\n",
        "plt.xlabel('Mutual Information')\n",
        "plt.ylabel('Feature')\n",
        "plt.title(f'Top {k} Features based on Mutual Information')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "bK4y9bCP6n8Y",
        "outputId": "2031ee0b-e325-4bb6-d88c-69ea945cb288"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.7"
    },
    "colab": {
      "provenance": [],
      "toc_visible": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}