assignment_knn.ipynb

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        "## Assignment 3: $k$ Nearest Neighbor\n",
        "\n",
        "**Do two questions.**\n",
        "\n",
        "`! git clone https://github.com/DS3001/knn`"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! git clone https://github.com/DS3001/knn"
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          "text": [
            "Cloning into 'knn'...\n",
            "remote: Enumerating objects: 68, done.\u001b[K\n",
            "remote: Counting objects: 100% (45/45), done.\u001b[K\n",
            "remote: Compressing objects: 100% (33/33), done.\u001b[K\n",
            "remote: Total 68 (delta 24), reused 26 (delta 12), pack-reused 23 (from 1)\u001b[K\n",
            "Receiving objects: 100% (68/68), 13.58 MiB | 10.75 MiB/s, done.\n",
            "Resolving deltas: 100% (25/25), done.\n"
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      "cell_type": "code",
      "source": [
        "# Importing relavant packages:\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "#from sklearn.metrics import mean_squared_error"
      ],
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        "**Q1.** This question is a case study for $k$ nearest neighbor The target variable `y` is `price` and the features are `year` and `mileage`.\n",
        "\n",
        "1. Load the `./data/USA_cars_datasets.csv`. Keep the following variables and drop the rest: `price`, `year`, `mileage`. Are there any `NA`'s to handle? Look at the head and dimensions of the data.\n",
        "2. Maxmin normalize `year` and `mileage`.\n",
        "3. Split the sample into ~80% for training and ~20% for evaluation.\n",
        "4. Use the $k$NN algorithm and the training data to predict `price` using `year` and `mileage` for the test set for $k=3,10,25,50,100,300$. For each value of $k$, compute the mean squared error and print a scatterplot showing the test value plotted against the predicted value. What patterns do you notice as you increase $k$?\n",
        "5. Determine the optimal $k$ for these data.\n",
        "6. Describe what happened in the plots of predicted versus actual prices as $k$ varied, taking your answer into part 6 into account. (Hint: Use the words \"underfitting\" and \"overfitting\".)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.\n",
        "path = './knn/data/USA_cars_datasets.csv' # Pulling the cars data\n",
        "df = pd.read_csv(path, low_memory=False) # Creating an cars dataframe"
      ],
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      "source": [
        "# Removing all but the price, year, and mileage variables:\n",
        "df = df[['price', 'year', 'mileage']]\n",
        "\n",
        "# Checking for any NANs in the df (there are none):\n",
        "print(df.isna().sum(), '\\n')\n",
        "\n",
        "# Checking any null values in the df (there are none):\n",
        "print(df.isnull().sum(), '\\n')\n",
        "\n",
        "# Seeing the dimensions of the dataframe (2499 x 3):\n",
        "print(df.shape)"
      ],
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          "text": [
            "price      0\n",
            "year       0\n",
            "mileage    0\n",
            "dtype: int64 \n",
            "\n",
            "price      0\n",
            "year       0\n",
            "mileage    0\n",
            "dtype: int64 \n",
            "\n",
            "(2499, 3)\n"
          ]
        }
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      "source": [
        "# Taking a preliminary look at the data:\n",
        "df.head()"
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          "data": {
            "text/plain": [
              "   price  year  mileage\n",
              "0   6300  2008   274117\n",
              "1   2899  2011   190552\n",
              "2   5350  2018    39590\n",
              "3  25000  2014    64146\n",
              "4  27700  2018     6654"
            ],
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              "      <th></th>\n",
              "      <th>price</th>\n",
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              "      <td>6300</td>\n",
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              "      <td>2899</td>\n",
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              "      <td>190552</td>\n",
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              "      <td>5350</td>\n",
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              "      <td>39590</td>\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2499,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12116,\n        \"min\": 0,\n        \"max\": 84900,\n        \"num_unique_values\": 790,\n        \"samples\": [\n          12995,\n          16200,\n          9280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1973,\n        \"max\": 2020,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          1984,\n          2000,\n          1994\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59705,\n        \"min\": 0,\n        \"max\": 1017936,\n        \"num_unique_values\": 2439,\n        \"samples\": [\n          20103,\n          31233,\n          9660\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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      "source": [
        "# 2.\n",
        "# Max/min normalizing the year and mileage variables:\n",
        "df['year'] = (df['year'] - df['year'].min()) / (df['year'].max() - df['year'].min())\n",
        "df['mileage'] = (df['mileage'] - df['mileage'].min()) / (df['mileage'].max() - df['mileage'].min())"
      ],
      "metadata": {
        "id": "URkkuAN00VI8"
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      "id": "URkkuAN00VI8",
      "execution_count": 7,
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      "source": [
        "# 3.\n",
        "y = df['price'] # Creating atarget variable vector (what we're predicting)\n",
        "X = df.loc[:,['year','mileage']] # Creating a feature matrix (predictors)\n",
        "\n",
        "# Splitting the sample:\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, # Feature and target variables\n",
        "                                                    test_size=.2, # Split the sample 80 train/ 20 test\n",
        "                                                    random_state=100) # For replication purposes"
      ],
      "metadata": {
        "id": "i-X01Zf9DqOL"
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      "id": "i-X01Zf9DqOL",
      "execution_count": 8,
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      "cell_type": "code",
      "source": [
        "# 4.\n",
        "\n",
        "k_values = [3, 10, 25, 50, 100, 300] # List of k's to try\n",
        "\n",
        "for k in k_values:\n",
        "  model = KNeighborsRegressor(n_neighbors=k) # Create a sk model for k\n",
        "  fitted_model = model.fit(X_train,y_train)  # Train the model on our data\n",
        "\n",
        "  y_hat = fitted_model.predict(X_test)  # Predict values for test set\n",
        "  SSE = np.sum( (y_test-y_hat)**2 )     # Save the computed SSE for test set\n",
        "\n",
        "  fig, axes = plt.subplots()\n",
        "  plt.scatter(y_test,y_hat)\n",
        "\n",
        "  plt.xlabel('Actual Price')\n",
        "  plt.ylabel('Predicted Price')\n",
        "  SSE = round(SSE,2)  # Rounding SSE to the hundredths place\n",
        "  plt.title('Predicted vs. Actual Price, k: ' + str(k) + ' SSE: ' + str(SSE))\n",
        "  plt.xlim(0,50000)\n",
        "  plt.ylim(0,50000)\n",
        "  plt.show()\n",
        "\n"
      ],
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        {
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            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "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": [
        "# Over the course of the k-values the SSE decreases by about 15%.\n",
        "#  The spread of the data also seems to decrease noticeably."
      ],
      "metadata": {
        "id": "uVgbXrkQ7P7g"
      },
      "id": "uVgbXrkQ7P7g",
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 5.\n",
        "k_bar = 200 # Number of k's to try\n",
        "SSE = np.zeros(k_bar) # We'll store the SSE here\n",
        "\n",
        "for k in range(k_bar):\n",
        "    model = KNeighborsRegressor(n_neighbors=k+1) # Create a sk model for k\n",
        "    fitted_model = model.fit(X_train,y_train) # Train the model on our data\n",
        "\n",
        "    y_hat = fitted_model.predict(X_test) # Predict values for test set\n",
        "    SSE[k] = np.sum( (y_test-y_hat)**2 ) # Save the computed SSE for test set\n",
        "\n",
        "SSE_min = np.min(SSE) # Find lowest recorded SSE\n",
        "min_index = np.where(SSE==SSE_min) # Find the indices of SSE that equal the minimum\n",
        "k_star = min_index[0]+1 # Find the optimal value of k; why index+1?\n",
        "print(k_star)"
      ],
      "metadata": {
        "id": "W2S6X8Qq5EHs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79a2ef33-a931-4028-9cfc-7a16d7723705"
      },
      "id": "W2S6X8Qq5EHs",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[77]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the k-values:\n",
        "\n",
        "plt.plot(np.arange(0,k_bar),SSE,label='Test SSE') # Plot SSE by k\n",
        "plt.xlabel(\"k\")\n",
        "plt.ylabel(\"SSE\")\n",
        "plt.title('SSE')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "O6Q3ZWc_nNxo",
        "outputId": "28b005d6-9f40-4ef9-dd2f-f71779ac935e"
      },
      "id": "O6Q3ZWc_nNxo",
      "execution_count": 12,
      "outputs": [
        {
          "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": [
        "# The optimal k-value is 77"
      ],
      "metadata": {
        "id": "30C6DCXqndnH"
      },
      "id": "30C6DCXqndnH",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 6.\n",
        "# The model is definitely underfitted at smaller (k<77) values. This\n",
        "#  can be seen in a wide spread in the predicted price without\n",
        "#  much correlation to the actual price. At k=100, which is closest\n",
        "#  to the optimal k of 77, the model is at its most accurate with a\n",
        "#  its lowest SSE. Past k=100 we start to see some overfitting and\n",
        "#  the model stops behaving naturally. This is most noticeable at\n",
        "#  the horizontal(ish) line that forms when the predicted price\n",
        "#  hits ~29k. There's no reason for prices to behave like this\n",
        "#  in real life, so it's a clear indicator of overfitting."
      ],
      "metadata": {
        "id": "VNx7NEI6nuI2"
      },
      "id": "VNx7NEI6nuI2",
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "010b57f7-bf4f-4494-b54c-49c4f3ae3ab9",
      "metadata": {
        "id": "010b57f7-bf4f-4494-b54c-49c4f3ae3ab9"
      },
      "source": [
        "**Q2.** This question is a case study for $k$ nearest neighbor. The data for the question include:\n",
        "\n",
        "- age: age of the patient (years)\n",
        "- anaemia: decrease of red blood cells or hemoglobin (boolean)\n",
        "- high blood pressure: if the patient has hypertension (boolean)\n",
        "- creatinine phosphokinase (CPK): level of the CPK enzyme in the blood (mcg/L)\n",
        "- diabetes: if the patient has diabetes (boolean)\n",
        "- ejection fraction: percentage of blood leaving the heart at each contraction (percentage)\n",
        "- platelets: platelets in the blood (kiloplatelets/mL)\n",
        "- sex: woman or man (binary)\n",
        "- serum creatinine: level of serum creatinine in the blood (mg/dL)\n",
        "- serum sodium: level of serum sodium in the blood (mEq/L)\n",
        "- smoking: if the patient smokes or not (boolean)\n",
        "- time: follow-up period (days)\n",
        "- death event: if the patient deceased during the follow-up period (boolean)\n",
        "\n",
        "1. Load the `./data/heart_failure_clinical_records_dataset.csv`. Are there any `NA`'s to handle? use `.drop()` to remove `time` from the dataframe.\n",
        "2. Make a correlation matrix. What variables are strongly associated with a death event?\n",
        "3. For the dummy variables `anaemia`, `diabetes`, `high_blood_pressure`, `sex`, and `smoking`, compute a summary table of `DEATH_EVENT` grouped by the variable. For which variables does a higher proportion of the population die when the variable takes the value 1 rather than 0?\n",
        "4. On the basis of your answers from 2 and 3, build a matrix $X$ of the variables you think are most predictive of a death, and a variable $y$ equal to `DEATH_EVENT`.\n",
        "5. Maxmin normalize all of the variables in `X`.\n",
        "6. Split the sample into ~80% for training and ~20% for evaluation. (Try to use the same train/test split for the whole question, so that you're comparing apples to apples in the questions below.).\n",
        "7. Determine the optimal number of neighbors for a $k$NN regression for the variables you selected.\n",
        "8. OK, do steps 5 through 7 again, but use all of the variables (except `time`). Which model has a lower Sum of Squared Error? Which would you prefer to use in practice, if you had to predict `DEATH_EVENT`s? If you play with the selection of variables, how much does the SSE change for your fitted model on the test data? Are more variables always better? Explain your findings."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.\n",
        "path = './knn/data/heart_failure_clinical_records_dataset.csv' # Pulling the records data\n",
        "df = pd.read_csv(path, low_memory=False) # Creating a records dataframe"
      ],
      "metadata": {
        "id": "Nyb3HGyb6VRB"
      },
      "id": "Nyb3HGyb6VRB",
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing the time variable from the dataframe:\n",
        "df = df.drop(columns=['time'])\n",
        "\n",
        "# Checking for any NANs in the df (there are none):\n",
        "print(df.isna().sum(), '\\n')\n",
        "\n",
        "# Checking any null values in the df (there are none):\n",
        "print(df.isnull().sum(), '\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G6clnlE36r1I",
        "outputId": "138cac40-a458-49b2-e4c0-a945feb3d420"
      },
      "id": "G6clnlE36r1I",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age                         0\n",
            "anaemia                     0\n",
            "creatinine_phosphokinase    0\n",
            "diabetes                    0\n",
            "ejection_fraction           0\n",
            "high_blood_pressure         0\n",
            "platelets                   0\n",
            "serum_creatinine            0\n",
            "serum_sodium                0\n",
            "sex                         0\n",
            "smoking                     0\n",
            "DEATH_EVENT                 0\n",
            "dtype: int64 \n",
            "\n",
            "age                         0\n",
            "anaemia                     0\n",
            "creatinine_phosphokinase    0\n",
            "diabetes                    0\n",
            "ejection_fraction           0\n",
            "high_blood_pressure         0\n",
            "platelets                   0\n",
            "serum_creatinine            0\n",
            "serum_sodium                0\n",
            "sex                         0\n",
            "smoking                     0\n",
            "DEATH_EVENT                 0\n",
            "dtype: int64 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Taking a preliminary look at the data:\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "JMavjwVh69D7",
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      "id": "JMavjwVh69D7",
      "execution_count": 16,
      "outputs": [
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          "data": {
            "text/plain": [
              "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
              "0  75.0        0                       582         0                 20   \n",
              "1  55.0        0                      7861         0                 38   \n",
              "2  65.0        0                       146         0                 20   \n",
              "3  50.0        1                       111         0                 20   \n",
              "4  65.0        1                       160         1                 20   \n",
              "\n",
              "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
              "0                    1  265000.00               1.9           130    1   \n",
              "1                    0  263358.03               1.1           136    1   \n",
              "2                    0  162000.00               1.3           129    1   \n",
              "3                    0  210000.00               1.9           137    1   \n",
              "4                    0  327000.00               2.7           116    0   \n",
              "\n",
              "   smoking  DEATH_EVENT  \n",
              "0        0            1  \n",
              "1        0            1  \n",
              "2        1            1  \n",
              "3        0            1  \n",
              "4        0            1  "
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 299,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.894809074044478,\n        \"min\": 40.0,\n        \"max\": 95.0,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          79.0,\n          40.0,\n          67.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"anaemia\",\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\": \"creatinine_phosphokinase\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 970,\n        \"min\": 23,\n        \"max\": 7861,\n        \"num_unique_values\": 208,\n        \"samples\": [\n          86,\n          379\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diabetes\",\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\": \"ejection_fraction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 14,\n        \"max\": 80,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          20,\n          38\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high_blood_pressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"platelets\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 97804.23686859828,\n        \"min\": 25100.0,\n        \"max\": 850000.0,\n        \"num_unique_values\": 176,\n        \"samples\": [\n          297000.0,\n          255000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"serum_creatinine\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.034510064089853,\n        \"min\": 0.5,\n        \"max\": 9.4,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          4.4,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"serum_sodium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 113,\n        \"max\": 148,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          133,\n          134\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoking\",\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\": \"DEATH_EVENT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2.\n",
        "# Making a correlation matrix:\n",
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "y1ME_nys7EJD",
        "outputId": "dfefa532-c3a3-4900-c8e4-78d025acaefd"
      },
      "id": "y1ME_nys7EJD",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               age   anaemia  creatinine_phosphokinase  \\\n",
              "age                       1.000000  0.088006                 -0.081584   \n",
              "anaemia                   0.088006  1.000000                 -0.190741   \n",
              "creatinine_phosphokinase -0.081584 -0.190741                  1.000000   \n",
              "diabetes                 -0.101012 -0.012729                 -0.009639   \n",
              "ejection_fraction         0.060098  0.031557                 -0.044080   \n",
              "high_blood_pressure       0.093289  0.038182                 -0.070590   \n",
              "platelets                -0.052354 -0.043786                  0.024463   \n",
              "serum_creatinine          0.159187  0.052174                 -0.016408   \n",
              "serum_sodium             -0.045966  0.041882                  0.059550   \n",
              "sex                       0.065430 -0.094769                  0.079791   \n",
              "smoking                   0.018668 -0.107290                  0.002421   \n",
              "DEATH_EVENT               0.253729  0.066270                  0.062728   \n",
              "\n",
              "                          diabetes  ejection_fraction  high_blood_pressure  \\\n",
              "age                      -0.101012           0.060098             0.093289   \n",
              "anaemia                  -0.012729           0.031557             0.038182   \n",
              "creatinine_phosphokinase -0.009639          -0.044080            -0.070590   \n",
              "diabetes                  1.000000          -0.004850            -0.012732   \n",
              "ejection_fraction        -0.004850           1.000000             0.024445   \n",
              "high_blood_pressure      -0.012732           0.024445             1.000000   \n",
              "platelets                 0.092193           0.072177             0.049963   \n",
              "serum_creatinine         -0.046975          -0.011302            -0.004935   \n",
              "serum_sodium             -0.089551           0.175902             0.037109   \n",
              "sex                      -0.157730          -0.148386            -0.104615   \n",
              "smoking                  -0.147173          -0.067315            -0.055711   \n",
              "DEATH_EVENT              -0.001943          -0.268603             0.079351   \n",
              "\n",
              "                          platelets  serum_creatinine  serum_sodium       sex  \\\n",
              "age                       -0.052354          0.159187     -0.045966  0.065430   \n",
              "anaemia                   -0.043786          0.052174      0.041882 -0.094769   \n",
              "creatinine_phosphokinase   0.024463         -0.016408      0.059550  0.079791   \n",
              "diabetes                   0.092193         -0.046975     -0.089551 -0.157730   \n",
              "ejection_fraction          0.072177         -0.011302      0.175902 -0.148386   \n",
              "high_blood_pressure        0.049963         -0.004935      0.037109 -0.104615   \n",
              "platelets                  1.000000         -0.041198      0.062125 -0.125120   \n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2952807785489794,\n        \"min\": -0.10101238518196966,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.018667867990770408,\n          0.06542952364475427,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"anaemia\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30378304725364297,\n        \"min\": -0.19074103020066285,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.10728983823716665,\n          -0.09476896093314953,\n          0.0880064413193916\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"creatinine_phosphokinase\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3029104314744122,\n        \"min\": -0.19074103020066285,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.0024212351965237004,\n          0.07979062873290346,\n          -0.08158389977909229\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diabetes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30960658551696546,\n        \"min\": -0.15772950433810978,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.1471734133234141,\n          -0.15772950433810978,\n          -0.10101238518196966\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ejection_fraction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.31423260402253295,\n        \"min\": -0.26860331239406127,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.06731456733133784,\n          -0.14838596532635928,\n          0.06009836323291317\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high_blood_pressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.29295767628781544,\n        \"min\": -0.10461462935689739,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.055711368777562775,\n          -0.10461462935689739,\n          0.09328868460631876\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"platelets\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2953580654739879,\n        \"min\": -0.12512048280918261,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.02823444771434774,\n          -0.12512048280918261,\n          -0.05235436663012704\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"serum_creatinine\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3075471024237243,\n        \"min\": -0.18909521009117405,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.027414135447069114,\n          0.006969778483151021,\n          0.15918713328355003\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"serum_sodium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.311636758927629,\n        \"min\": -0.19520359641640217,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.004813194662557512,\n          -0.02756612272823605,\n          -0.045965840839560256\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3330068462856133,\n        \"min\": -0.15772950433810978,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.4458917119097906,\n          1.0,\n          0.06542952364475427\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.32249880229806693,\n        \"min\": -0.1471734133234141,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          1.0,\n          0.4458917119097906,\n          0.018667867990770408\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DEATH_EVENT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.32382565149580333,\n        \"min\": -0.26860331239406127,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.012623152709359344,\n          -0.004316376319703093,\n          0.25372854308800347\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Interestingly, there seems to be a reasonably high correlation between\n",
        "#  sex and smoking. Age and death event are correlelated higher\n",
        "#  than other variables. Death event is also correlated with serum\n",
        "#  creatinine, ejection fraction, and serum sodium."
      ],
      "metadata": {
        "id": "mfCv7YKb7aDv"
      },
      "id": "mfCv7YKb7aDv",
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 3.\n",
        "# Creating the tables using the .describe() command:\n",
        "print(df.loc[:,['anaemia','DEATH_EVENT']].groupby('anaemia').describe(),'\\n')\n",
        "print(df.loc[:,['diabetes','DEATH_EVENT']].groupby('diabetes').describe(),'\\n')\n",
        "print(df.loc[:,['high_blood_pressure','DEATH_EVENT']].groupby('high_blood_pressure').describe,'\\n')\n",
        "print(df.loc[:,['sex','DEATH_EVENT']].groupby('sex').describe(),'\\n')\n",
        "print(df.loc[:,['smoking','DEATH_EVENT']].groupby('smoking').describe(),'\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6OB98moX8EyY",
        "outputId": "6db69569-0631-4c49-e419-c91fa8d3d5f8"
      },
      "id": "6OB98moX8EyY",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        DEATH_EVENT                                             \n",
            "              count      mean       std  min  25%  50%  75%  max\n",
            "anaemia                                                         \n",
            "0             170.0  0.294118  0.456991  0.0  0.0  0.0  1.0  1.0\n",
            "1             129.0  0.356589  0.480859  0.0  0.0  0.0  1.0  1.0 \n",
            "\n",
            "         DEATH_EVENT                                             \n",
            "               count      mean       std  min  25%  50%  75%  max\n",
            "diabetes                                                         \n",
            "0              174.0  0.321839  0.468530  0.0  0.0  0.0  1.0  1.0\n",
            "1              125.0  0.320000  0.468353  0.0  0.0  0.0  1.0  1.0 \n",
            "\n",
            "<bound method GroupBy.describe of <pandas.core.groupby.generic.DataFrameGroupBy object at 0x7b5f32450370>> \n",
            "\n",
            "    DEATH_EVENT                                             \n",
            "          count      mean       std  min  25%  50%  75%  max\n",
            "sex                                                         \n",
            "0         105.0  0.323810  0.470173  0.0  0.0  0.0  1.0  1.0\n",
            "1         194.0  0.319588  0.467523  0.0  0.0  0.0  1.0  1.0 \n",
            "\n",
            "        DEATH_EVENT                                             \n",
            "              count      mean       std  min  25%  50%  75%  max\n",
            "smoking                                                         \n",
            "0             203.0  0.325123  0.469579  0.0  0.0  0.0  1.0  1.0\n",
            "1              96.0  0.312500  0.465946  0.0  0.0  0.0  1.0  1.0 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# There is a higher proportion of death events for anaemia as well\n",
        "#  as for high blood pressure. This can be seen in the larger mean\n",
        "#  values corresponding to 1 for those variables. Interestingly, the\n",
        "#  mean for people who do and don't smoke seems to be about the same."
      ],
      "metadata": {
        "id": "0ADrhGv89Tlb"
      },
      "id": "0ADrhGv89Tlb",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.\n",
        "# Creating a matrix of Death Event, anemia, and high blood pressure:\n",
        "y = df['DEATH_EVENT']\n",
        "X = df.loc[:,['anaemia','high_blood_pressure']]"
      ],
      "metadata": {
        "id": "YYoCmVn8-Dmd"
      },
      "id": "YYoCmVn8-Dmd",
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 5.\n",
        "# Max/min normalizing the variable X:\n",
        "X = (X - X.min()) / (X.max() - X.min())"
      ],
      "metadata": {
        "id": "9K6lhGnT-ebf"
      },
      "id": "9K6lhGnT-ebf",
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 6.\n",
        "# Splitting the data between trained and untrained:\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, # Feature and target variables\n",
        "                                                    test_size=.2, # Split the sample 80 train/ 20 test\n",
        "                                                    random_state=100) # For replication purposes"
      ],
      "metadata": {
        "id": "SWpT6FD2-qcw"
      },
      "id": "SWpT6FD2-qcw",
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 7.\n",
        "k_bar = 200 # Number of k's to try\n",
        "SSE = np.zeros(k_bar) # We'll store the SSE here\n",
        "\n",
        "for k in range(k_bar):\n",
        "    model = KNeighborsRegressor(n_neighbors=k+1) # Create a sk model for k\n",
        "    fitted_model = model.fit(X_train,y_train) # Train the model on our data\n",
        "\n",
        "    y_hat = fitted_model.predict(X_test) # Predict values for test set\n",
        "    SSE[k] = np.sum( (y_test-y_hat)**2 ) # Save the computed SSE for test set\n",
        "\n",
        "SSE_min = np.min(SSE) # Find lowest recorded SSE\n",
        "min_index = np.where(SSE==SSE_min) # Find the indices of SSE that equal the minimum\n",
        "k_star = min_index[0]+1 # Find the optimal value of k; why index+1?\n",
        "print(k_star)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qlHV7U8g--Ut",
        "outputId": "56ecc8db-cc78-4238-aeb4-f740cc3b5912"
      },
      "id": "qlHV7U8g--Ut",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[40]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the k-values:\n",
        "plt.plot(np.arange(0,k_bar),SSE,label='Test SSE') # Plot SSE by k\n",
        "plt.xlabel(\"k\")\n",
        "plt.ylabel(\"SSE\")\n",
        "plt.title('k_star= ' + str(k_star))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "yH4wXjlH_GH4",
        "outputId": "d3b8e843-413b-4756-91c6-af70e28281b6"
      },
      "id": "yH4wXjlH_GH4",
      "execution_count": 24,
      "outputs": [
        {
          "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": [
        "# The optimal k-value seems to be 40."
      ],
      "metadata": {
        "id": "6WsBCd8-_zxR"
      },
      "id": "6WsBCd8-_zxR",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 8.\n",
        "# Re-creating the matricies with every value except time:\n",
        "y = df['DEATH_EVENT']\n",
        "X = df.drop(columns=['DEATH_EVENT'])\n",
        "\n",
        "# Normalizing X:\n",
        "X = (X - X.min()) / (X.max() - X.min())\n",
        "\n",
        "# Splitting the data:\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, # Feature and target variables\n",
        "                                                    test_size=.2, # Split the sample 80 train/ 20 test\n",
        "                                                    random_state=100) # For replication purposes"
      ],
      "metadata": {
        "id": "GS6qbp-z_xpo"
      },
      "id": "GS6qbp-z_xpo",
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting another model using every variable:\n",
        "k_bar = 200 # Number of k's to try\n",
        "SSE = np.zeros(k_bar) # We'll store the SSE here\n",
        "\n",
        "for k in range(k_bar):\n",
        "    model = KNeighborsRegressor(n_neighbors=k+1) # Create a sk model for k\n",
        "    fitted_model = model.fit(X_train,y_train) # Train the model on our data\n",
        "\n",
        "    y_hat = fitted_model.predict(X_test) # Predict values for test set\n",
        "    SSE[k] = np.sum( (y_test-y_hat)**2 ) # Save the computed SSE for test set\n",
        "\n",
        "SSE_min = np.min(SSE) # Find lowest recorded SSE\n",
        "min_index = np.where(SSE==SSE_min) # Find the indices of SSE that equal the minimum\n",
        "k_star = min_index[0]+1 # Find the optimal value of k; why index+1?\n",
        "print(k_star)"
      ],
      "metadata": {
        "id": "W3BxaMVaL-u6",
        "outputId": "b12af4ce-59c8-42ff-9a4d-94e261bc55a1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "W3BxaMVaL-u6",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[19]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the k-values:\n",
        "plt.plot(np.arange(0,k_bar),SSE,label='Test SSE') # Plot SSE by k\n",
        "plt.xlabel(\"k\")\n",
        "plt.ylabel(\"SSE\")\n",
        "plt.title('k_star= ' + str(k_star))\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "13bVEoQpMDlX",
        "outputId": "5c05073e-d75d-4aef-d0f1-0c7f3b0625a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "id": "13bVEoQpMDlX",
      "execution_count": 27,
      "outputs": [
        {
          "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": [
        "# The best k has decreased to 19. Additionally, the SSE has gone\n",
        "#  down a bit for the optimal k. This model seems to fit the\n",
        "#  data somewhat better than the previous one."
      ],
      "metadata": {
        "id": "z9FkgMlFMQ9l"
      },
      "id": "z9FkgMlFMQ9l",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Re-creating the matricies with every value except time and anemia:\n",
        "y = df['DEATH_EVENT']\n",
        "X = df.drop(columns=['DEATH_EVENT'])\n",
        "X = X.drop(columns=['anaemia'])\n",
        "\n",
        "# Normalizing X:\n",
        "X = (X - X.min()) / (X.max() - X.min())\n",
        "\n",
        "# Splitting the data:\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, # Feature and target variables\n",
        "                                                    test_size=.2, # Split the sample 80 train/ 20 test\n",
        "                                                    random_state=100) # For replication purposes"
      ],
      "metadata": {
        "id": "q6tgqEw1myVe"
      },
      "id": "q6tgqEw1myVe",
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting another model using every variable:\n",
        "k_bar = 200 # Number of k's to try\n",
        "SSE = np.zeros(k_bar) # We'll store the SSE here\n",
        "\n",
        "for k in range(k_bar):\n",
        "    model = KNeighborsRegressor(n_neighbors=k+1) # Create a sk model for k\n",
        "    fitted_model = model.fit(X_train,y_train) # Train the model on our data\n",
        "\n",
        "    y_hat = fitted_model.predict(X_test) # Predict values for test set\n",
        "    SSE[k] = np.sum( (y_test-y_hat)**2 ) # Save the computed SSE for test set\n",
        "\n",
        "SSE_min = np.min(SSE) # Find lowest recorded SSE\n",
        "min_index = np.where(SSE==SSE_min) # Find the indices of SSE that equal the minimum\n",
        "k_star = min_index[0]+1 # Find the optimal value of k; why index+1?\n",
        "print(k_star)"
      ],
      "metadata": {
        "id": "yLxQdkJEnCf6",
        "outputId": "95c4624f-aaf3-452e-f5bd-d2c3332c8ba5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "yLxQdkJEnCf6",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[13]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the k-values:\n",
        "plt.plot(np.arange(0,k_bar),SSE,label='Test SSE') # Plot SSE by k\n",
        "plt.xlabel(\"k\")\n",
        "plt.ylabel(\"SSE\")\n",
        "plt.title('k_star= ' + str(k_star))\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "yIJZksM8nE2q",
        "outputId": "8bab2264-01b6-48af-e7cd-61419ab03e84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "id": "yIJZksM8nE2q",
      "execution_count": 33,
      "outputs": [
        {
          "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": [
        "# After playing around with the date for awhile (removing various\n",
        "#  explanatory variables) I actually wasn't able to reduce the SSE\n",
        "#  much. Although having more variables isn't always for the best,\n",
        "#  I think in this case that there weren't many to begin with\n",
        "#  meant they didn't cause any negative effects in large numbers."
      ],
      "metadata": {
        "id": "9tPTtt5LnNvd"
      },
      "id": "9tPTtt5LnNvd",
      "execution_count": 34,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.10.4"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}