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  "cells": [
    {
      "cell_type": "markdown",
      "id": "200c5c7e",
      "metadata": {
        "id": "200c5c7e"
      },
      "source": [
        "# Diagnosing diabetes with KNN\n",
        "\n",
        "In this small notebook we use data about diabetes in Pima Indian women to see how well we can predict diabetes given variables like age, glucose level, blood pressure, and skin thickness.\n",
        "\n",
        "We focus on two things:\n",
        "- data exploration and cleaning\n",
        "- using hyperparameter tuning to find the best hyperparameter values for KNN classification\n",
        "\n",
        "I downloaded the dataset from Kaggle on October 21, 2021.\n",
        "\n",
        "https://www.kaggle.com/uciml/pima-indians-diabetes-database\n",
        "\n",
        "Information about the data set can be found on the Kaggle page.\n",
        "\n",
        "v1.7"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "057ab65a",
      "metadata": {
        "id": "057ab65a"
      },
      "source": [
        "### Instructions\n",
        "\n",
        "- Please read the entire notebook carefully!\n",
        "- Note that plots are preceded by a question and followed by interpretation of the plot.\n",
        "- Each problem cell begins with #@.\n",
        "- Do not make changes outside the problem cells.\n",
        "- Be sure to include plot titles, labels, etc. as shown in model output.\n",
        "- Use plotting methods covered in class.\n",
        "- Run your code from top to bottom before submitting, otherwise points will be deducted.\n",
        "- Do not modify the file name."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 271,
      "id": "7911839e",
      "metadata": {
        "id": "7911839e"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from scipy.stats import zscore\n",
        "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 272,
      "id": "82b58eaa",
      "metadata": {
        "id": "82b58eaa"
      },
      "outputs": [],
      "source": [
        "# plotting\n",
        "sns.set_theme(context='notebook', style='whitegrid')\n",
        "plt.rcParams['figure.figsize'] = (4,3)   # default plot size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 273,
      "id": "80e47552",
      "metadata": {
        "id": "80e47552"
      },
      "outputs": [],
      "source": [
        "# suppress \"future warnings\"\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0cf74dc7",
      "metadata": {
        "id": "0cf74dc7"
      },
      "source": [
        "### Set the random seed for repeatability"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 274,
      "id": "fb8176bd",
      "metadata": {
        "id": "fb8176bd"
      },
      "outputs": [],
      "source": [
        "np.random.seed(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f7a2edda",
      "metadata": {
        "id": "f7a2edda"
      },
      "source": [
        "### Read the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 275,
      "id": "c208e1fa",
      "metadata": {
        "id": "c208e1fa"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/grbruns/cst383/master/diabetes.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "29f8c8e2",
      "metadata": {
        "id": "29f8c8e2"
      },
      "source": [
        "It is useful to identify the predictor and target variables right away."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 276,
      "id": "32f7071b",
      "metadata": {
        "id": "32f7071b"
      },
      "outputs": [],
      "source": [
        "target = 'Outcome'\n",
        "predictors = list(df.columns)\n",
        "predictors.remove(target)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e4f09eef",
      "metadata": {
        "id": "e4f09eef"
      },
      "source": [
        "### Data exploration"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8b7a8607",
      "metadata": {
        "id": "8b7a8607"
      },
      "source": [
        "Looking at an overview of the data, we see no NA values.  All variables are numeric."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 277,
      "id": "e554766a",
      "metadata": {
        "id": "e554766a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bed9d93c-ff98-4af0-c76f-d89a4d2c1774"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Pregnancies               768 non-null    int64  \n",
            " 1   Glucose                   768 non-null    int64  \n",
            " 2   BloodPressure             768 non-null    int64  \n",
            " 3   SkinThickness             768 non-null    int64  \n",
            " 4   Insulin                   768 non-null    int64  \n",
            " 5   BMI                       768 non-null    float64\n",
            " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
            " 7   Age                       768 non-null    int64  \n",
            " 8   Outcome                   768 non-null    int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "072b6ef6",
      "metadata": {
        "id": "072b6ef6"
      },
      "source": [
        "It is helpful to look at a little of the raw data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 278,
      "id": "fe81ba4b",
      "metadata": {
        "id": "fe81ba4b",
        "colab": {
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          "height": 206
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        "outputId": "823fe9d3-218c-4388-ab59-e2690fa2e9f0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
              "1            1       85             66             29        0  26.6   \n",
              "2            8      183             64              0        0  23.3   \n",
              "3            1       89             66             23       94  28.1   \n",
              "4            0      137             40             35      168  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
              "3                     0.167   21        0  \n",
              "4                     2.288   33        1  "
            ],
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.8841603203754405,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33132859501277484,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\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": 278
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4b1cd406",
      "metadata": {
        "id": "4b1cd406"
      },
      "source": [
        "'Outcome' is the target value.  A value of 1 indicates the presence of diabetes.  \n",
        "\n",
        "How many people represented in the data have diabetes?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 279,
      "id": "4d706607",
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          "height": 342
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        "outputId": "db8a0816-ea73-475d-99a4-1a8d8b869149"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#@ 1  Create a bar plot that shows the fraction of patients with diabetes, and without diabetes.\n",
        "# For this and all later problems, use visualization methods covered in class.\n",
        "\n",
        "df['Outcome'].value_counts(normalize=True).plot.bar(rot=0)\n",
        "plt.title('Diabetes status of Pima Indians in data set')\n",
        "plt.xlabel('Status (1 = diabetes)')\n",
        "plt.ylabel('Fraction of people');"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2d29a18f",
      "metadata": {
        "id": "2d29a18f"
      },
      "source": [
        "About 1/3 of the patients have diabetes, according to the data."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "66511443",
      "metadata": {
        "id": "66511443"
      },
      "source": [
        "What are the basic statistics of the numeric variables?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 280,
      "id": "20db56f7",
      "metadata": {
        "id": "20db56f7",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
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            "text/plain": [
              "       Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin     BMI  \\\n",
              "count       768.00   768.00         768.00         768.00   768.00  768.00   \n",
              "mean          3.85   120.89          69.11          20.54    79.80   31.99   \n",
              "std           3.37    31.97          19.36          15.95   115.24    7.88   \n",
              "min           0.00     0.00           0.00           0.00     0.00    0.00   \n",
              "25%           1.00    99.00          62.00           0.00     0.00   27.30   \n",
              "50%           3.00   117.00          72.00          23.00    30.50   32.00   \n",
              "75%           6.00   140.25          80.00          32.00   127.25   36.60   \n",
              "max          17.00   199.00         122.00          99.00   846.00   67.10   \n",
              "\n",
              "       DiabetesPedigreeFunction     Age  Outcome  \n",
              "count                    768.00  768.00   768.00  \n",
              "mean                       0.47   33.24     0.35  \n",
              "std                        0.33   11.76     0.48  \n",
              "min                        0.08   21.00     0.00  \n",
              "25%                        0.24   24.00     0.00  \n",
              "50%                        0.37   29.00     0.00  \n",
              "75%                        0.63   41.00     1.00  \n",
              "max                        2.42   81.00     1.00  "
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              "      <th>count</th>\n",
              "      <td>768.00</td>\n",
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              "      <th>mean</th>\n",
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              "      <td>33.24</td>\n",
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              "      <th>std</th>\n",
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              "      <td>115.24</td>\n",
              "      <td>7.88</td>\n",
              "      <td>0.33</td>\n",
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              "      <td>0.48</td>\n",
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              "      <th>min</th>\n",
              "      <td>0.00</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>21.00</td>\n",
              "      <td>0.00</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.00</td>\n",
              "      <td>99.00</td>\n",
              "      <td>62.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>27.30</td>\n",
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              "      <td>0.37</td>\n",
              "      <td>29.00</td>\n",
              "      <td>0.00</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.00</td>\n",
              "      <td>140.25</td>\n",
              "      <td>80.00</td>\n",
              "      <td>32.00</td>\n",
              "      <td>127.25</td>\n",
              "      <td>36.60</td>\n",
              "      <td>0.63</td>\n",
              "      <td>41.00</td>\n",
              "      <td>1.00</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.00</td>\n",
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              "      <td>122.00</td>\n",
              "      <td>99.00</td>\n",
              "      <td>846.00</td>\n",
              "      <td>67.10</td>\n",
              "      <td>2.42</td>\n",
              "      <td>81.00</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 269.85196030151684,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.85,\n          3.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 243.7384265347646,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          120.89,\n          117.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 252.85199343475335,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          69.11,\n          72.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 263.7684073705297,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          768.0,\n          20.54,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 350.2607696085671,\n        \"min\": 0.0,\n        \"max\": 846.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          768.0,\n          79.8,\n          127.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 262.05156114439444,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          31.99,\n          32.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.3007102170684,\n        \"min\": 0.08,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.47,\n          0.37,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 260.1941775454851,\n        \"min\": 11.76,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          33.24,\n          29.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.38638553795795,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.35,\n          1.0,\n          0.48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 280
        }
      ],
      "source": [
        "df.describe().round(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "62d411a4",
      "metadata": {
        "id": "62d411a4"
      },
      "source": [
        "We see that there are no negative values in the dataset.  \n",
        "\n",
        "Something that looks odd is that many variables have zero as their minimum value.  Can blood pressure really be zero?  What about skin thickness, and body mass index?  Perhaps some of the zeroes indicate bad data.\n",
        "\n",
        "Does the data contain outliers?  This will be easier to see if the data is scaled."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 281,
      "id": "16092dae",
      "metadata": {
        "id": "16092dae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "b46b736c-7d49-4d13-aaa0-d7861aec9416"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin      BMI  \\\n",
              "count      768.000  768.000        768.000        768.000  768.000  768.000   \n",
              "mean        -0.000   -0.000          0.000          0.000   -0.000    0.000   \n",
              "std          1.001    1.001          1.001          1.001    1.001    1.001   \n",
              "min         -1.142   -3.784         -3.573         -1.288   -0.693   -4.060   \n",
              "25%         -0.845   -0.685         -0.367         -1.288   -0.693   -0.596   \n",
              "50%         -0.251   -0.122          0.150          0.155   -0.428    0.001   \n",
              "75%          0.640    0.606          0.563          0.719    0.412    0.585   \n",
              "max          3.907    2.444          2.735          4.922    6.653    4.456   \n",
              "\n",
              "       DiabetesPedigreeFunction      Age  Outcome  \n",
              "count                   768.000  768.000  768.000  \n",
              "mean                      0.000    0.000    0.000  \n",
              "std                       1.001    1.001    1.001  \n",
              "min                      -1.190   -1.042   -0.732  \n",
              "25%                      -0.689   -0.786   -0.732  \n",
              "50%                      -0.300   -0.361   -0.732  \n",
              "75%                       0.466    0.660    1.366  \n",
              "max                       5.884    4.064    1.366  "
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              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>768.000</td>\n",
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              "      <td>768.000</td>\n",
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              "      <th>mean</th>\n",
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              "      <td>0.000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>0.155</td>\n",
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              "      <td>0.719</td>\n",
              "      <td>0.412</td>\n",
              "      <td>0.585</td>\n",
              "      <td>0.466</td>\n",
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              "      <td>6.653</td>\n",
              "      <td>4.456</td>\n",
              "      <td>5.884</td>\n",
              "      <td>4.064</td>\n",
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              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-9485abee-646c-4c7d-80e3-65e6066f18c9 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-9485abee-646c-4c7d-80e3-65e6066f18c9');\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",
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              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.36634908959195,\n        \"min\": -1.142,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -0.0,\n          -0.251,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.5620920021265,\n        \"min\": -3.784,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -0.0,\n          -0.122,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.50900072633084,\n        \"min\": -3.573,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.0,\n          0.15,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.3227987508277,\n        \"min\": -1.288,\n        \"max\": 768.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          768.0,\n          0.0,\n          0.719\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.22402651103226,\n        \"min\": -0.693,\n        \"max\": 768.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          768.0,\n          -0.0,\n          0.412\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.46888653815904,\n        \"min\": -4.06,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.0,\n          0.001,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.2767310689005,\n        \"min\": -1.19,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.0,\n          -0.3,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.3551475375819,\n        \"min\": -1.042,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.0,\n          -0.361,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.4529535899295,\n        \"min\": -0.732,\n        \"max\": 768.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0,\n          1.366,\n          1.001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 281
        }
      ],
      "source": [
        "#@ 2 Produce describe() output after using zscore normalization on each column.\n",
        "# Do not modify df, as it will be used in later cells.\n",
        "# Hint: in class we covered how to zscore normalize all columns of a dataframe.\n",
        "\n",
        "df.apply(zscore).describe().round(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "72829079",
      "metadata": {
        "id": "72829079"
      },
      "source": [
        "We see that the maximum insulin value is more than 6 standard deviations above the mean, and the max skin thickness and diabetes pedigree function values are also large.\n",
        "\n",
        "Box plots can help identify outliers.  When showing multiple boxplots at once, scaling is useful."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 282,
      "id": "81e8cb44",
      "metadata": {
        "id": "81e8cb44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "2a66a9f5-af44-4906-ee94-c9c653efb1ac"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#@ 3 Produce box plots for all predictor values.  Scale each predictor using Z-score normalization.\n",
        "# Hint: recall from class how a boxplot can be created for a dataframe.\n",
        "\n",
        "df[predictors].apply(zscore).plot.box(figsize=(10, 5), fontsize=7)\n",
        "plt.title('Distributions of predictor variables');"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ed9974ac",
      "metadata": {
        "id": "ed9974ac"
      },
      "source": [
        "There seem to be many outliers in the Insulin and DiabetesPedegreeFunction variables.  \n",
        "\n",
        "We can get a deeper feeling for zero values and outliers by plotting the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 283,
      "id": "f1be116f",
      "metadata": {
        "id": "f1be116f",
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ab6162fe-ed4f-4cc5-fa60-7e8bb5996c48"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1040x1040 with 72 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#@ 4 Produce a plot containing scatterplots for all pairs of predictor variables.\n",
        "# Hint: it takes a while for the output to be produced, so don't worry about that.\n",
        "graph = sns.pairplot(df[predictors], height=1.3, aspect=1, plot_kws={'s': 10})\n",
        "plt.show();"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b76725c9",
      "metadata": {
        "id": "b76725c9"
      },
      "source": [
        "We find that some of the 0 values do indeed look strange.  For example, the zero BMI values look strange.  Also, some of the max values look like outliers.  For example, the largest skin thickness value."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8d57f209",
      "metadata": {
        "id": "8d57f209"
      },
      "source": [
        "### Investigating zero values"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fe018374",
      "metadata": {
        "id": "fe018374"
      },
      "source": [
        "A concern is that zero values might represent missing data.\n",
        "\n",
        "Let's focus first on insulin.  What is the distribution of insulin values?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 284,
      "id": "6d17ad6d",
      "metadata": {
        "id": "6d17ad6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "12a56ef1-757c-44a9-a759-c779c7488a61"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.hist(df['Insulin']);\n",
        "plt.title(\"Histogram of insulin values\");"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2332b53a",
      "metadata": {
        "id": "2332b53a"
      },
      "source": [
        "The distribution is highly skewed.  Plotting the log may make the distribution clearer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 285,
      "id": "c02d0e76",
      "metadata": {
        "id": "c02d0e76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "c9ebf969-8fee-42a9-b652-1c26c2a59c25"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.hist(np.log10(df['Insulin']+1));\n",
        "plt.title(\"Histogram of insultin values (log10 scale)\");"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cd3f08f7",
      "metadata": {
        "id": "cd3f08f7"
      },
      "source": [
        "This picture makes the zero insulin values look very suspicious.\n",
        "\n",
        "What about the other predictors?  For each predictor, what fraction of the values are 0?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 286,
      "id": "e6f31b2f",
      "metadata": {
        "id": "e6f31b2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "outputId": "adb37e7d-149c-46bb-b90f-1dc6bc25d320"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Insulin                     0.487\n",
              "SkinThickness               0.296\n",
              "Pregnancies                 0.145\n",
              "BloodPressure               0.046\n",
              "BMI                         0.014\n",
              "Glucose                     0.007\n",
              "DiabetesPedigreeFunction    0.000\n",
              "Age                         0.000\n",
              "dtype: float64"
            ],
            "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>Insulin</th>\n",
              "      <td>0.487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SkinThickness</th>\n",
              "      <td>0.296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pregnancies</th>\n",
              "      <td>0.145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BloodPressure</th>\n",
              "      <td>0.046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMI</th>\n",
              "      <td>0.014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Glucose</th>\n",
              "      <td>0.007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 286
        }
      ],
      "source": [
        "#@ 5  Write an expression to compute the fraction of values of each predictor that are zero.\n",
        "# Put output in decreasing order of 0 fraction.\n",
        "# Hint: don't forget the 'predictors' variable defined above.\n",
        "# Hint: you can use round().\n",
        "\n",
        "(df[predictors] == 0).mean().sort_values(ascending=False).round(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "031c700a",
      "metadata": {
        "id": "031c700a"
      },
      "source": [
        "About 46% of the insulin values are zero.  This could suggest they are valid.  Also, a little background research suggests zero insulin values are possible.\n",
        "\n",
        "Another way to understand zero values is to see how many standard deviations they are away from the mean value."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 287,
      "id": "e1ad8cf5",
      "metadata": {
        "id": "e1ad8cf5",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "outputId": "4d646e6f-cfcb-4a40-ca3b-2c54f9fbdbda"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BMI                         4.058\n",
              "Glucose                     3.781\n",
              "BloodPressure               3.570\n",
              "Age                         2.827\n",
              "DiabetesPedigreeFunction    1.424\n",
              "SkinThickness               1.287\n",
              "Pregnancies                 1.141\n",
              "Insulin                     0.692\n",
              "dtype: float64"
            ],
            "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>BMI</th>\n",
              "      <td>4.058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Glucose</th>\n",
              "      <td>3.781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BloodPressure</th>\n",
              "      <td>3.570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>2.827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <td>1.424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SkinThickness</th>\n",
              "      <td>1.287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pregnancies</th>\n",
              "      <td>1.141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Insulin</th>\n",
              "      <td>0.692</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 287
        }
      ],
      "source": [
        "df[predictors].apply(lambda x: x.mean()/x.std()).sort_values(ascending=False).round(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1999653a",
      "metadata": {
        "id": "1999653a"
      },
      "source": [
        "This backs up the idea that blood pressure values of 0 represent missing data.  \n",
        "\n",
        "Are the 0 values in the data set clustered in some rows?\n",
        "In other words, are the 0's spread evenly across people, or clustered in some people?  To look into this, we can count the number of rows with no zero values, with 1 zero value, etc."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 288,
      "id": "209041f1",
      "metadata": {
        "id": "209041f1",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "outputId": "234861dd-4621-4b29-f06b-97a1efadd8a9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#@ 6  Compute the number of *rows* that contain *no* predictor values of 0,\n",
        "# the number of rows that contain *one* predictor value of 0, etc.\n",
        "# Show the results as a bar plot.\n",
        "# Hint: it's possible to take the sum of every *row* of a boolean dataframe.\n",
        "# We did in class when counting NAs by row.\n",
        "\n",
        "(df[predictors] == 0).sum(axis=1).value_counts().sort_index().plot.bar(rot=0)\n",
        "plt.title('Row counts by number of 0 predictor values')\n",
        "plt.xlabel('Number f 0 predictor values')\n",
        "plt.ylabel('Number of rows');"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5dacd510",
      "metadata": {
        "id": "5dacd510"
      },
      "source": [
        "We see that about 170 rows contain two zero values, but very few rows contain more than two zero values."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "41b62da0",
      "metadata": {
        "id": "41b62da0"
      },
      "source": [
        "In the rows with more than one zero value, which predictors are 0?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 289,
      "id": "bd91d4be",
      "metadata": {
        "id": "bd91d4be",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "cc3cfc6b-f409-4547-b1aa-44cc4b629029"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "2             8      183             64              0        0  23.3   \n",
              "5             5      116             74              0        0  25.6   \n",
              "7            10      115              0              0        0  35.3   \n",
              "9             8      125             96              0        0   0.0   \n",
              "10            4      110             92              0        0  37.6   \n",
              "11           10      168             74              0        0  38.0   \n",
              "12           10      139             80              0        0  27.1   \n",
              "15            7      100              0              0        0  30.0   \n",
              "17            7      107             74              0        0  29.6   \n",
              "21            8       99             84              0        0  35.4   \n",
              "\n",
              "    DiabetesPedigreeFunction  Age  Outcome  \n",
              "2                      0.672   32        1  \n",
              "5                      0.201   30        0  \n",
              "7                      0.134   29        0  \n",
              "9                      0.232   54        1  \n",
              "10                     0.191   30        0  \n",
              "11                     0.537   34        1  \n",
              "12                     1.441   57        0  \n",
              "15                     0.484   32        1  \n",
              "17                     0.254   31        1  \n",
              "21                     0.388   50        0  "
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>116</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.6</td>\n",
              "      <td>0.201</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10</td>\n",
              "      <td>115</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.3</td>\n",
              "      <td>0.134</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>125</td>\n",
              "      <td>96</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.232</td>\n",
              "      <td>54</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>4</td>\n",
              "      <td>110</td>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37.6</td>\n",
              "      <td>0.191</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>10</td>\n",
              "      <td>168</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.537</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>10</td>\n",
              "      <td>139</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>27.1</td>\n",
              "      <td>1.441</td>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>7</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.484</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>7</td>\n",
              "      <td>107</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>29.6</td>\n",
              "      <td>0.254</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>8</td>\n",
              "      <td>99</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.4</td>\n",
              "      <td>0.388</td>\n",
              "      <td>50</td>\n",
              "      <td>0</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-31dd3261-44b0-4a65-834e-173deca2abd1')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-31dd3261-44b0-4a65-834e-173deca2abd1 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-31dd3261-44b0-4a65-834e-173deca2abd1');\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",
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              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[(df[predictors] == 0)\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 4,\n        \"max\": 10,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5,\n          7,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28,\n        \"min\": 99,\n        \"max\": 183,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          107,\n          116,\n          168\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34,\n        \"min\": 0,\n        \"max\": 96,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          64,\n          74,\n          80\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\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\": \"Insulin\",\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\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.150829565552511,\n        \"min\": 0.0,\n        \"max\": 38.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          29.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3885437770273341,\n        \"min\": 0.134,\n        \"max\": 1.441,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.254\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 29,\n        \"max\": 57,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          30\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 289
        }
      ],
      "source": [
        "df[(df[predictors] == 0).sum(axis=1) > 1].head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "74e869ad",
      "metadata": {
        "id": "74e869ad"
      },
      "source": [
        "Zero values for skin thickness and insulin seem to go together.  Does a scatter plot confirm this idea?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 290,
      "id": "f308f33f",
      "metadata": {
        "id": "f308f33f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "outputId": "8fabeae5-3f8f-4af1-d261-d077719f2681"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.scatterplot(data=df, x='SkinThickness', y='Insulin')\n",
        "plt.xlabel('skin thickness')\n",
        "plt.title('Skin thickness by insulin value');"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "40ce30cb",
      "metadata": {
        "id": "40ce30cb"
      },
      "source": [
        "It seems that if skin thickness is 0, then insulin is 0, because there are no skin thickness values of 0 except where insulin is 0.\n",
        "\n",
        "Perhaps if a person's skin is very thin, it is hard to test for insulin.  Talking to a diabetes specialist would help in understanding this."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5af73fa7",
      "metadata": {
        "id": "5af73fa7"
      },
      "source": [
        "### Data preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8fef1a97",
      "metadata": {
        "id": "8fef1a97"
      },
      "source": [
        "Our strategy on zero values will be to remove rows in which BMI, Glucose, BloodPressure, or SkinThickness are 0.  An alternative approach would be to impute values for these zero values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 291,
      "id": "a9530ac6",
      "metadata": {
        "id": "a9530ac6"
      },
      "outputs": [],
      "source": [
        "#@ 7 Modify df to to remove the rows in which BMI, Glucose, BloodPressure, or SkinThickness have value 0.\n",
        "# Hint: use a boolean mask that involves these variables.\n",
        "# Hint: write an assignment statement.\n",
        "# Don't forget to stick to techniques covered in class.\n",
        "\n",
        "df = df[(df['BMI'] != 0) & (df['Glucose'] != 0) & (df['BloodPressure'] != 0) & (df['SkinThickness'] != 0)]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "24f1ca76",
      "metadata": {
        "id": "24f1ca76"
      },
      "source": [
        "Use describe again to see the result of removing these rows."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 292,
      "id": "217f74eb",
      "metadata": {
        "id": "217f74eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "a1a3747e-b58a-42df-a7a7-993cca70569f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
              "count   532.000000  532.000000     532.000000     532.000000  532.000000   \n",
              "mean      3.516917  121.030075      71.505639      29.182331  114.988722   \n",
              "std       3.312036   30.999226      12.310253      10.523878  123.007555   \n",
              "min       0.000000   56.000000      24.000000       7.000000    0.000000   \n",
              "25%       1.000000   98.750000      64.000000      22.000000    0.000000   \n",
              "50%       2.000000  115.000000      72.000000      29.000000   91.500000   \n",
              "75%       5.000000  141.250000      80.000000      36.000000  165.250000   \n",
              "max      17.000000  199.000000     110.000000      99.000000  846.000000   \n",
              "\n",
              "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
              "count  532.000000                532.000000  532.000000  532.000000  \n",
              "mean    32.890226                  0.502966   31.614662    0.332707  \n",
              "std      6.881109                  0.344546   10.761584    0.471626  \n",
              "min     18.200000                  0.085000   21.000000    0.000000  \n",
              "25%     27.875000                  0.258750   23.000000    0.000000  \n",
              "50%     32.800000                  0.416000   28.000000    0.000000  \n",
              "75%     36.900000                  0.658500   38.000000    1.000000  \n",
              "max     67.100000                  2.420000   81.000000    1.000000  "
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              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
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              "      <td>532.000000</td>\n",
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              "      <td>532.000000</td>\n",
              "      <td>532.000000</td>\n",
              "      <td>532.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.516917</td>\n",
              "      <td>121.030075</td>\n",
              "      <td>71.505639</td>\n",
              "      <td>29.182331</td>\n",
              "      <td>114.988722</td>\n",
              "      <td>32.890226</td>\n",
              "      <td>0.502966</td>\n",
              "      <td>31.614662</td>\n",
              "      <td>0.332707</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.312036</td>\n",
              "      <td>30.999226</td>\n",
              "      <td>12.310253</td>\n",
              "      <td>10.523878</td>\n",
              "      <td>123.007555</td>\n",
              "      <td>6.881109</td>\n",
              "      <td>0.344546</td>\n",
              "      <td>10.761584</td>\n",
              "      <td>0.471626</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.200000</td>\n",
              "      <td>0.085000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>98.750000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.875000</td>\n",
              "      <td>0.258750</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>115.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>91.500000</td>\n",
              "      <td>32.800000</td>\n",
              "      <td>0.416000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>141.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>165.250000</td>\n",
              "      <td>36.900000</td>\n",
              "      <td>0.658500</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</td>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 186.55847503015858,\n        \"min\": 0.0,\n        \"max\": 532.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.5169172932330826,\n          2.0,\n          532.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 158.10682513059126,\n        \"min\": 30.999226003246658,\n        \"max\": 532.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          121.03007518796993,\n          115.0,\n          532.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 169.0496784408075,\n        \"min\": 12.310253491380529,\n        \"max\": 532.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          71.50563909774436,\n          72.0,\n          532.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 178.6298696227974,\n        \"min\": 7.0,\n        \"max\": 532.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          29.18233082706767,\n          29.0,\n          532.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 298.657890769031,\n        \"min\": 0.0,\n        \"max\": 846.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          532.0,\n          114.98872180451127,\n          165.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 177.6894747105342,\n        \"min\": 6.881108882976854,\n        \"max\": 532.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.89022556390977,\n          32.8,\n          532.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 187.85517141605038,\n        \"min\": 0.085,\n        \"max\": 532.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.5029661654135338,\n          0.416,\n          532.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 177.55208446004573,\n        \"min\": 10.761583838015309,\n        \"max\": 532.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          31.61466165413534,\n          28.0,\n          532.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 187.9492233224036,\n        \"min\": 0.0,\n        \"max\": 532.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.33270676691729323,\n          1.0,\n          0.471625993446327\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 292
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "88d879f2",
      "metadata": {
        "id": "88d879f2"
      },
      "source": [
        "Put the predictor and target values into NumPy arrays."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 293,
      "id": "98497b7a",
      "metadata": {
        "id": "98497b7a"
      },
      "outputs": [],
      "source": [
        "X = df[predictors].values\n",
        "y = df[target].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 294,
      "id": "88da57d1",
      "metadata": {
        "id": "88da57d1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61587fc1-c88b-40a3-ed12-c2362dcd73e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X shape: (532, 8)\n",
            "y shape: (532,)\n"
          ]
        }
      ],
      "source": [
        "print('X shape: {}'.format(X.shape))\n",
        "print('y shape: {}'.format(y.shape))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "472bf030",
      "metadata": {
        "id": "472bf030"
      },
      "source": [
        "Perform an 75/25 train/test split."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 295,
      "id": "ba2ae86c",
      "metadata": {
        "id": "ba2ae86c"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "71819b53",
      "metadata": {
        "id": "71819b53"
      },
      "source": [
        "Scale the data using z-score normalization.  Note that the scaler is trained on the training data, and the trained scaler is used on both the training and test data.  The target values are not scaled."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 296,
      "id": "a0f382ad",
      "metadata": {
        "id": "a0f382ad"
      },
      "outputs": [],
      "source": [
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dee08454",
      "metadata": {
        "id": "dee08454"
      },
      "source": [
        "### Basic KNN classification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 297,
      "id": "da34a9be",
      "metadata": {
        "id": "da34a9be"
      },
      "outputs": [],
      "source": [
        "#@ 8  Train a KNN classifier on the training data.\n",
        "# Create a KNeighborsClassifier object and store it as variable 'knn'.\n",
        "# Use the default hyperparameters (in other words, don't specify any hyperparamters).\n",
        "# Then train the classifier on the *training* data.\n",
        "#\n",
        "# Hint: I expect two lines of code here.  Use a semicolon at the\n",
        "# end of the line on which you train the classifier to suppress\n",
        "# the unneeded output.\n",
        "\n",
        "knn = KNeighborsClassifier()\n",
        "knn.fit(X_train, y_train);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "caf9551d",
      "metadata": {
        "id": "caf9551d"
      },
      "source": [
        "Three important hyperparameters of a KNN classifier:\n",
        "- the number of nearest neighbors ('n_neighbors' in KNeighborsClassifier)\n",
        "- the distance metric  ('metric' and 'p' in KNeighborsClassifier)\n",
        "- whether closer neighbors should be more important when making predictions ('weights' in KNeighborsClassifier)\n",
        "\n",
        "If 'metric' is 'minkowski', then p = 1 means manhattan distance, and p = 2 means Euclidian distance.\n",
        "\n",
        "Let's look at the default hyperparameters for the KNN classifier."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 298,
      "id": "faa7a22c",
      "metadata": {
        "id": "faa7a22c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50bf8a8b-4a09-404a-ff25-b10efe0e0259"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'algorithm': 'auto',\n",
              " 'leaf_size': 30,\n",
              " 'metric': 'minkowski',\n",
              " 'metric_params': None,\n",
              " 'n_jobs': None,\n",
              " 'n_neighbors': 5,\n",
              " 'p': 2,\n",
              " 'weights': 'uniform'}"
            ]
          },
          "metadata": {},
          "execution_count": 298
        }
      ],
      "source": [
        "# examining default parameters\n",
        "knn.get_params()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "28e3eb02",
      "metadata": {
        "id": "28e3eb02"
      },
      "source": [
        "What cross-validation accuracy is achieved with the default KNN classifier?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 299,
      "id": "fe9913c9",
      "metadata": {
        "id": "fe9913c9"
      },
      "outputs": [],
      "source": [
        "#@ 9  Compute the cross-validation accuracy of the classifier.\n",
        "# Use 10 folds in cross validation.\n",
        "# Use the method covered in class, of course.\n",
        "# Store your result as variable 'cv_accuracy'.\n",
        "\n",
        "cv_accuracy = cross_val_score(knn, X_train, y_train, cv=10).mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 300,
      "id": "18dcc114",
      "metadata": {
        "id": "18dcc114",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4bff7786-f2a5-4248-b9a6-ab43d215253e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CV accuracy using default hyperparameters: 0.754\n"
          ]
        }
      ],
      "source": [
        "print('CV accuracy using default hyperparameters: {:.3f}'.format(cv_accuracy))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e33f07be",
      "metadata": {
        "id": "e33f07be"
      },
      "source": [
        "### Compute the accuracy we'd get by always predicting \"no diabetes\"."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5df2b226",
      "metadata": {
        "id": "5df2b226"
      },
      "source": [
        "When we compute the accuracy of a classifier, we want a baseline for comparison.  The usual baseline is the accuracy that you would get if you always predicted the most common value of the predictor variable.  In this case, the most common value of y_test is 0."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 301,
      "id": "6c6867a1",
      "metadata": {
        "id": "6c6867a1"
      },
      "outputs": [],
      "source": [
        "#@ 10  Compute the baseline accuracy by computing the fraction of the y_train values that are 0.\n",
        "# Store the result as variable 'baseline_accuracy'.\n",
        "# Hint: here it is okay to assume the majority target value is 0, but in future we will always calculate it.\n",
        "\n",
        "baseline_accuracy = (y_train == 0).mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 302,
      "id": "2aa597c3",
      "metadata": {
        "id": "2aa597c3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0bdf992-3f12-4d13-c816-802dafb3d32d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Baseline accuracy: 0.662\n"
          ]
        }
      ],
      "source": [
        "print('Baseline accuracy: {:.3f}'.format(baseline_accuracy))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "44895304",
      "metadata": {
        "id": "44895304"
      },
      "source": [
        "So the test accuracy is significantly better than the baseline."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "16229d7e",
      "metadata": {
        "id": "16229d7e"
      },
      "source": [
        "### Determine best k by using 10-fold cross validation"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7075cbdf",
      "metadata": {
        "id": "7075cbdf"
      },
      "source": [
        "The default value of k (called n_neighbors in Scikit-Learn) is 5 with KNeighborsClassifier.  Is this a good value for k?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 303,
      "id": "fd3f5992",
      "metadata": {
        "id": "fd3f5992"
      },
      "outputs": [],
      "source": [
        "#@ 11  Compute 10-fold cross-validation accuracy for k=1, 3, 5, ..., 23.\n",
        "# Store the accuracy values in list cv_accuracy.\n",
        "# Store the k values in list ks.\n",
        "# Hint: we covered this in class.  The cv-accuracy value should be the mean\n",
        "# of the accuracy values for the 10 folds.\n",
        "\n",
        "ks = np.arange(1, 25, 2)\n",
        "cv_accuracy = []\n",
        "for k in ks:\n",
        "    knn = KNeighborsClassifier(n_neighbors=k)\n",
        "    accs = cross_val_score(knn, X_train, y_train, scoring=\"accuracy\", cv=10)\n",
        "    cv_accuracy.append(accs.mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 304,
      "id": "62468a58",
      "metadata": {
        "id": "62468a58",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "outputId": "3dff110a-3d74-479a-cf57-abe6a85a1f9b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(ks, cv_accuracy)\n",
        "plt.title('KNN accuracy by k (diabetes data)')\n",
        "plt.xlabel('k')\n",
        "plt.ylabel('cross-validation accuracy');"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0ebe55dd",
      "metadata": {
        "id": "0ebe55dd"
      },
      "source": [
        "The plot shows that a value of 11 is best, but 19 is almost as good.  Perhaps any value between about 11 and 21 is good."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "65281d1b",
      "metadata": {
        "id": "65281d1b"
      },
      "source": [
        "### Find best combination of hyperparameters k and p using grid search  (**bonus point**)  You can get bonus to finish the rest of the part"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0df51997",
      "metadata": {
        "id": "0df51997"
      },
      "source": [
        "The problem with finding the best k value on its own, is that the best value of k might depend on the other hyperparameter values, such as distance function.\n",
        "\n",
        "We really would like to search for the best combination of parameter values.  Grid search is a good way to do this."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 305,
      "id": "d810a360",
      "metadata": {
        "id": "d810a360"
      },
      "outputs": [],
      "source": [
        "#@ 12  Use GridSearchCV to find the best settings for hyperparameters 'n_neighbors',\n",
        "# 'weights', and 'p' of KNeighborsClassifier.\n",
        "# For 'n_neighbors', consider values 5, 7, 9, ..., 15.\n",
        "# For 'weights', consider values 'uniform' and 'distance'.\n",
        "# For 'p', consider values 1 and 2.\n",
        "# Assign your GridSearchCV object to variable 'knn_cv', and fit it using the training data.\n",
        "# You do not need to do more than to do the fit here.\n",
        "\n",
        "param = {'n_neighbors': range(5, 16, 2), 'weights': ['uniform', 'distance'], 'p': [1, 2]}\n",
        "knn_cv = GridSearchCV(KNeighborsClassifier(), param, cv=10)\n",
        "knn_cv.fit(X_train, y_train);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 306,
      "id": "7b2f16f5",
      "metadata": {
        "id": "7b2f16f5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad927678-4491-4661-cbfe-14ca427d3a43"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'n_neighbors': 11, 'p': 2, 'weights': 'distance'}\n"
          ]
        }
      ],
      "source": [
        "print(knn_cv.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 307,
      "id": "e03fd797",
      "metadata": {
        "id": "e03fd797",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f7f9ee8-c3e4-41d3-a4d5-5b360267c851"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "best CV accuracy: 0.789\n"
          ]
        }
      ],
      "source": [
        "print('best CV accuracy: {:.3f}'.format(knn_cv.best_score_))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3bc50b7e",
      "metadata": {
        "id": "3bc50b7e"
      },
      "source": [
        "The CV accuracy we get with the best hyperparameter values is better than with the default hyperparameters."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cde15c77",
      "metadata": {
        "id": "cde15c77"
      },
      "source": [
        "### Train model with best hyperparameters on all training data. (**bonus point**)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9691c776",
      "metadata": {
        "id": "9691c776"
      },
      "source": [
        "After using cross validation, we want to train the best model (in other words, the model with the best hyperparameter values) on the complete set of training data.\n",
        "\n",
        "Fortunately, GridSearchCV() does this for us automatically in the .fit() method, as long as the 'refit' parameter of GridSearchCV is True, which it is by default."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b1f6f552",
      "metadata": {
        "id": "b1f6f552"
      },
      "source": [
        "### Compute test accuracy using the score() function (**bonus point**)  "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2ae6c4e3",
      "metadata": {
        "id": "2ae6c4e3"
      },
      "source": [
        "We have not used the test data yet.  We now compute test accuracy to see how our classifier does on data never seen before."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 308,
      "id": "bfdb770e",
      "metadata": {
        "id": "bfdb770e"
      },
      "outputs": [],
      "source": [
        "#@ 13  Compute the test accuracy of the classifier with the best\n",
        "# hyperparameter values using the test data.\n",
        "# Use the score() function to do this, and store the\n",
        "# result in variable 'test_accuracy'.\n",
        "\n",
        "test_accuracy = knn_cv.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 309,
      "id": "207707d0",
      "metadata": {
        "id": "207707d0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1f95279-577d-4d9c-92fe-f15a88884151"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test accuracy: 0.759\n"
          ]
        }
      ],
      "source": [
        "print('Test accuracy: {:.3f}'.format(test_accuracy))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "16096a8c",
      "metadata": {
        "id": "16096a8c"
      },
      "source": [
        "Our test accuracy is significantly better than our baseline accuracy, but only by about 10%.  \n",
        "\n",
        "Some interesting questions:\n",
        "- Could we do better with a different type of classification algorithm?\n",
        "- Would we have done better if we had imputed the zero values?\n",
        "- Which of the predictor values are most important?\n",
        "- Could we do better if we drop some of the predictor variables?"
      ]
    }
  ],
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