Logistic_Regression.ipynb

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        "# Logistic Regression\n",
        "\n",
        "**Logistic Regression**: Logistic Regression is basically a classification algorithm and a statistical method used to model the relationship between input variables and one or more categorical target variables. It is especially used in cases where the dependent variable is categorical and is generally preferred for binary classification problems.\n",
        "Logistic regression is a type of linear regression, but it constrains the output (transforms it to be ***between 0 and 1*** - with a **sigmoid** function) and makes it suitable for classification problems."
      ],
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      "cell_type": "markdown",
      "source": [
        "# Import Necessary Libraries"
      ],
      "metadata": {
        "id": "SQq2B7R2oTfi"
      }
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      "cell_type": "code",
      "execution_count": 164,
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        "outputId": "2ad17475-df3a-45ea-a531-b401caebb66d"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (1.4.0)\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "pd.set_option(\"display.max_columns\",None)\n",
        "pd.set_option(\"display.max_rows\",None)\n",
        "pd.set_option(\"display.width\",500)\n",
        "pd.set_option(\"display.float_format\", lambda x: \"%.4f\" % x)\n",
        "\n",
        "from sklearn import model_selection\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, cross_validate\n",
        "from sklearn.metrics import accuracy_score, r2_score, classification_report, confusion_matrix, roc_auc_score, roc_curve\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import statsmodels.api as sm #statsmodels for linear models\n",
        "\n",
        "!pip install joblib\n",
        "import joblib\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Dataset"
      ],
      "metadata": {
        "id": "EYVB5oTcoz5z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/datasets/diabetes.csv\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
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        "id": "UspS87IBoysh",
        "outputId": "db128b40-9d9b-4827-a05d-182df71a6ede"
      },
      "execution_count": null,
      "outputs": [
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          "data": {
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              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin     BMI  DiabetesPedigreeFunction  Age  Outcome\n",
              "0            6      148             72             35        0 33.6000                    0.6270   50        1\n",
              "1            1       85             66             29        0 26.6000                    0.3510   31        0\n",
              "2            8      183             64              0        0 23.3000                    0.6720   32        1\n",
              "3            1       89             66             23       94 28.1000                    0.1670   21        0\n",
              "4            0      137             40             35      168 43.1000                    2.2880   33        1"
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              "type": "dataframe",
              "variable_name": "df",
              "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\": 7.884160320375446,\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\": 0.3313285950127749,\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": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# General Information About to the Dataset"
      ],
      "metadata": {
        "id": "XdPIBXIypK7n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def check_df(dataframe,head=5):\n",
        "  print(\"######################### Head #########################\")\n",
        "  print(dataframe.head(head))\n",
        "  print(\"######################### Tail #########################\")\n",
        "  print(dataframe.tail(head))\n",
        "  print(\"######################### Shape #########################\")\n",
        "  print(dataframe.shape)\n",
        "  print(\"######################### Types #########################\")\n",
        "  print(dataframe.dtypes)\n",
        "  print(\"######################### NA #########################\")\n",
        "  print(dataframe.isnull().sum())\n",
        "  print(\"######################### Qurtiles #########################\")\n",
        "  print(dataframe.describe([0, 0.05, 0.50, 0.95, 0.99, 1]).T)"
      ],
      "metadata": {
        "id": "9L9m3s2Go_uM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "check_df(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1veSft1SpNki",
        "outputId": "546a0ca6-e989-46cb-eb33-7cf9b0da5b2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "######################### Head #########################\n",
            "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin     BMI  DiabetesPedigreeFunction  Age  Outcome\n",
            "0            6      148             72             35        0 33.6000                    0.6270   50        1\n",
            "1            1       85             66             29        0 26.6000                    0.3510   31        0\n",
            "2            8      183             64              0        0 23.3000                    0.6720   32        1\n",
            "3            1       89             66             23       94 28.1000                    0.1670   21        0\n",
            "4            0      137             40             35      168 43.1000                    2.2880   33        1\n",
            "######################### Tail #########################\n",
            "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin     BMI  DiabetesPedigreeFunction  Age  Outcome\n",
            "763           10      101             76             48      180 32.9000                    0.1710   63        0\n",
            "764            2      122             70             27        0 36.8000                    0.3400   27        0\n",
            "765            5      121             72             23      112 26.2000                    0.2450   30        0\n",
            "766            1      126             60              0        0 30.1000                    0.3490   47        1\n",
            "767            1       93             70             31        0 30.4000                    0.3150   23        0\n",
            "######################### Shape #########################\n",
            "(768, 9)\n",
            "######################### Types #########################\n",
            "Pregnancies                   int64\n",
            "Glucose                       int64\n",
            "BloodPressure                 int64\n",
            "SkinThickness                 int64\n",
            "Insulin                       int64\n",
            "BMI                         float64\n",
            "DiabetesPedigreeFunction    float64\n",
            "Age                           int64\n",
            "Outcome                       int64\n",
            "dtype: object\n",
            "######################### NA #########################\n",
            "Pregnancies                 0\n",
            "Glucose                     0\n",
            "BloodPressure               0\n",
            "SkinThickness               0\n",
            "Insulin                     0\n",
            "BMI                         0\n",
            "DiabetesPedigreeFunction    0\n",
            "Age                         0\n",
            "Outcome                     0\n",
            "dtype: int64\n",
            "######################### Qurtiles #########################\n",
            "                            count     mean      std     min      0%      5%      50%      95%      99%     100%      max\n",
            "Pregnancies              768.0000   3.8451   3.3696  0.0000  0.0000  0.0000   3.0000  10.0000  13.0000  17.0000  17.0000\n",
            "Glucose                  768.0000 120.8945  31.9726  0.0000  0.0000 79.0000 117.0000 181.0000 196.0000 199.0000 199.0000\n",
            "BloodPressure            768.0000  69.1055  19.3558  0.0000  0.0000 38.7000  72.0000  90.0000 106.0000 122.0000 122.0000\n",
            "SkinThickness            768.0000  20.5365  15.9522  0.0000  0.0000  0.0000  23.0000  44.0000  51.3300  99.0000  99.0000\n",
            "Insulin                  768.0000  79.7995 115.2440  0.0000  0.0000  0.0000  30.5000 293.0000 519.9000 846.0000 846.0000\n",
            "BMI                      768.0000  31.9926   7.8842  0.0000  0.0000 21.8000  32.0000  44.3950  50.7590  67.1000  67.1000\n",
            "DiabetesPedigreeFunction 768.0000   0.4719   0.3313  0.0780  0.0780  0.1404   0.3725   1.1328   1.6983   2.4200   2.4200\n",
            "Age                      768.0000  33.2409  11.7602 21.0000 21.0000 21.0000  29.0000  58.0000  67.0000  81.0000  81.0000\n",
            "Outcome                  768.0000   0.3490   0.4770  0.0000  0.0000  0.0000   0.0000   1.0000   1.0000   1.0000   1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analysis of Categorical and Numerical Variables"
      ],
      "metadata": {
        "id": "yTTtCkG0pW6P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def grab_col_names(dataframe, cat_th=10, car_th=20):\n",
        "  #Catgeorical Variable Selection\n",
        "  cat_cols = [col for col in dataframe.columns if str(dataframe[col].dtypes) in [\"category\",\"object\",\"bool\"]]\n",
        "  num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and dataframe[col].dtypes in [\"uint8\",\"int64\",\"float64\"]]\n",
        "  cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and str(dataframe[col].dtypes) in [\"category\",\"object\"]]\n",
        "  cat_cols = cat_cols + num_but_cat\n",
        "  cat_cols = [col for col in cat_cols if col not in cat_but_car]\n",
        "\n",
        "  #Numerical Variable Selection\n",
        "  num_cols = [col for col in dataframe.columns if dataframe[col].dtypes in [\"uint8\",\"int64\",\"float64\"]]\n",
        "  num_cols = [col for col in num_cols if col not in cat_cols]\n",
        "\n",
        "  return cat_cols, num_cols, cat_but_car, num_but_cat"
      ],
      "metadata": {
        "id": "ieefq7yDpPy7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols, num_cols, cat_but_car, num_but_cat = grab_col_names(df)\n",
        "\n",
        "#Print Categorical and Numerical Variables\n",
        "print(f\"Observations: {df.shape[0]}\")\n",
        "print(f\"Variables: {df.shape[1]}\")\n",
        "print(f\"Cat_cols: {len(cat_cols)}\")\n",
        "print(f\"Num_cols: {len(num_cols)}\")\n",
        "print(f\"Cat_but_car: {len(cat_but_car)}\")\n",
        "print(f\"Num_But_Cat: {len(num_but_cat)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E16U0MSBpeUF",
        "outputId": "64f3cea9-4b8b-4575-ccd8-b8fb15609510"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Observations: 768\n",
            "Variables: 9\n",
            "Cat_cols: 1\n",
            "Num_cols: 8\n",
            "Cat_but_car: 0\n",
            "Num_But_Cat: 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cat_summary(dataframe,col_name,plot=False):\n",
        "  print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),\n",
        "                      'Ration': 100 * dataframe[col_name].value_counts() / len(dataframe)}))\n",
        "  print(\"##########################################\")\n",
        "  if plot:\n",
        "    sns.countplot(x=dataframe[col_name],data=dataframe)\n",
        "    plt.show(block=True)"
      ],
      "metadata": {
        "id": "gNkbQDLKpiYZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def cat_summary_df(dataframe):\n",
        "  cat_cols, num_cols, cat_but_car, num_but_cat = grab_col_names(df)\n",
        "  for col in cat_cols:\n",
        "    cat_summary(dataframe, col, plot=True)"
      ],
      "metadata": {
        "id": "DuUhQZfspygZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_summary_df(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "9RLevWaYp0Sn",
        "outputId": "c753a3ab-1d2f-40b7-a15a-90da0fce5d7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         Outcome  Ration\n",
            "Outcome                 \n",
            "0            500 65.1042\n",
            "1            268 34.8958\n",
            "##########################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def num_summary(dataframe, num_col, plot=False):\n",
        "  quantiles = [0.01, 0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99]\n",
        "  print(dataframe[num_col].describe(quantiles).T)\n",
        "\n",
        "  if plot:\n",
        "    dataframe[num_col].hist(bins=20)\n",
        "    plt.xlabel(num_col)\n",
        "    plt.title(num_col)\n",
        "    plt.show(block=True)"
      ],
      "metadata": {
        "id": "6j_bxl17p2P-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def num_summary_df(dataframe):\n",
        "  cat_cols, num_cols, cat_but_car, num_but_cat = grab_col_names(df)\n",
        "  for col in num_cols:\n",
        "    num_summary(dataframe, col, plot=True)"
      ],
      "metadata": {
        "id": "Z5JG_TvhqAcn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_summary_df(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jY151P7YqDW6",
        "outputId": "6b23779c-bf02-4659-b3f4-641cf3c170a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count   768.0000\n",
            "mean      3.8451\n",
            "std       3.3696\n",
            "min       0.0000\n",
            "1%        0.0000\n",
            "5%        0.0000\n",
            "10%       0.0000\n",
            "20%       1.0000\n",
            "30%       1.0000\n",
            "40%       2.0000\n",
            "50%       3.0000\n",
            "60%       4.0000\n",
            "70%       5.0000\n",
            "80%       7.0000\n",
            "90%       9.0000\n",
            "95%      10.0000\n",
            "99%      13.0000\n",
            "max      17.0000\n",
            "Name: Pregnancies, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count   768.0000\n",
            "mean    120.8945\n",
            "std      31.9726\n",
            "min       0.0000\n",
            "1%       57.0000\n",
            "5%       79.0000\n",
            "10%      85.0000\n",
            "20%      95.0000\n",
            "30%     102.0000\n",
            "40%     109.0000\n",
            "50%     117.0000\n",
            "60%     125.0000\n",
            "70%     134.0000\n",
            "80%     147.0000\n",
            "90%     167.0000\n",
            "95%     181.0000\n",
            "99%     196.0000\n",
            "max     199.0000\n",
            "Name: Glucose, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count   768.0000\n",
            "mean     69.1055\n",
            "std      19.3558\n",
            "min       0.0000\n",
            "1%        0.0000\n",
            "5%       38.7000\n",
            "10%      54.0000\n",
            "20%      60.0000\n",
            "30%      64.0000\n",
            "40%      68.0000\n",
            "50%      72.0000\n",
            "60%      74.0000\n",
            "70%      78.0000\n",
            "80%      82.0000\n",
            "90%      88.0000\n",
            "95%      90.0000\n",
            "99%     106.0000\n",
            "max     122.0000\n",
            "Name: BloodPressure, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count   768.0000\n",
            "mean     20.5365\n",
            "std      15.9522\n",
            "min       0.0000\n",
            "1%        0.0000\n",
            "5%        0.0000\n",
            "10%       0.0000\n",
            "20%       0.0000\n",
            "30%       8.2000\n",
            "40%      18.0000\n",
            "50%      23.0000\n",
            "60%      27.0000\n",
            "70%      31.0000\n",
            "80%      35.0000\n",
            "90%      40.0000\n",
            "95%      44.0000\n",
            "99%      51.3300\n",
            "max      99.0000\n",
            "Name: SkinThickness, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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whVQqlVx33XU5+OCDc/bZZydJRo0aleeffz7Tp0/PjBkzkiRPPvlkHn300cycOTNjx45NkgwcODATJkzIj3/840yYMGELHR4AAADA9qVVZ/b8behZb88998wbb7yRlStXZsmSJXnxxRdTV1fXbJ8JEyZk/vz5Wb16dZJk7ty5qa2tzZgxY5r2GTRoUPbcc8/MnTt3c44DAAAAgGyBCzQ//vjj6dOnT7p165ZFixYleessnb+16667Zs2aNVmyZEmSZNGiRRk4cGBqamqa7Tdo0KCm5wAAAACg9Vr1Nq6/98tf/jKzZ8/O+eefnyRZvnx5kqS2trbZfutvr7+/vr4+3bt33+D5evTokV//+tfvZqQkSceO7fdDxiqVmnfeqR3p0KH9/izY9qx/PXldQXPWBrTM2oCWWRuwcaWsj82OPa+88krOOeecjBw5MhMnTtySM70rlUpN3ve+Hao9Bv9PbW3Xao9AgbyuoGXWBrTM2oCWWRuwce19fWxW7Kmvr8+kSZPSs2fPXH/99alU3ipePXr0SPLWx6r37t272f5/e39tbW1eeeWVDZ53+fLlTftsroaGxtTXr3xXz1FNnTp1SLdu76n2GFtMff2qrFvXUO0xKESHDpXU1nb1uoK/Y21Ay6wNaJm1ARu3La+P2tqum3zGUatjz5tvvplTTz01K1asyPe+971mb8caNGhQkreuybP+39ff7tSpU/r379+03/z589PY2Njsuj2LFy/O7rvv3tqRNrB27bb1A2mN9n6q2N9bt66hXf882DZ5XUHLrA1ombUBLbM2YOPa+/poVVlYu3Ztzj777CxatCg333xz+vTp0+z+/v37Z8CAAZkzZ06z7bNnz87o0aPTuXPnJMm4ceOyfPnyzJ8/v2mfxYsX55lnnsm4ceM291gAAAAAtnutOrPnkksuyUMPPZQpU6bkjTfeyK9+9aum+/baa6907tw5Z555Zs4999zsvPPOGTlyZGbPnp0FCxbkzjvvbNp3+PDhGTt2bC644IKcf/756dKlS6655poMHjw4Bx544BY7OAAAAIDtTatiz7x585IkV1555Qb3PfDAA+nXr18OOeSQrFq1KjNmzMhNN92UgQMHZtq0aRk+fHiz/a+99tpcccUVmTp1atauXZuxY8fmwgsvTMeO7+oDwgAAAAC2a60qKw8++OAm7XfUUUflqKOOett9unfvnssvvzyXX355a0YAAAAA4G2UdTVgAAAAgO2c2AMAAABQELEHAAAAoCBiDwAAAEBBxB4AAACAgog9AAAAAAURewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBxB4AAACAgog9AAAAAAURewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBxB4AAACAgog9AAAAAAURewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBxB4AAACAgog9AAAAAAURewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBxB4AAACAgog9AAAAAAURewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBxB4AAACAgog9AAAAAAURewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBxB4AAACAgog9AAAAAAURewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBxB4AAACAgog9AAAAAAURewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBxB4AAACAgog9AAAAAAURewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBxB4AAACAgog9AAAAAAURewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBxB4AAACAgog9AAAAAAURewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBxB4AAACAgog9AAAAAAURewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBOrb2AS+99FJmzpyZp556Ki+88EIGDRqU//zP/2y2zwknnJD//u//3uCxs2fPzq677tp0e8WKFbniiivy05/+NGvWrMmHP/zhXHjhhdlxxx0341AAAAAAaHXseeGFF/LII4/kH//xH9PQ0JDGxsYW99tnn31y/vnnN9vWr1+/ZrfPPvvs/O53v8vFF1+cLl265Nprr82kSZPywx/+MB07tno0AAAAgO1eq4vK+PHj87GPfSxJMmXKlPz6179ucb/a2toMGzZso8/z5JNP5tFHH83MmTMzduzYJMnAgQMzYcKE/PjHP86ECRNaOxoAAADAdq/V1+ypVLbMZX7mzp2b2trajBkzpmnboEGDsueee2bu3Llb5GsAAAAAbG/a7ALN//3f/51hw4Zl6NChOf744/OLX/yi2f2LFi3KwIEDU1NT02z7oEGDsmjRorYaCwAAAKBobXJhnP333z+HHXZYBgwYkFdffTUzZ87M5z//+dxxxx0ZPnx4kqS+vj7du3ff4LE9evTY6FvDNlXHju33Q8YqlZp33qkd6dCh/f4s2Pasfz15XUFz1ga0zNqAllkbsHGlrI82iT2TJ09udvv//J//k0MOOSQ33HBDZsyY0RZfskmlUpP3vW+HNv0abLra2q7VHoECeV1By6wNaJm1AS2zNmDj2vv62CofefXe9743BxxwQO6///6mbbW1tXnllVc22Hf58uXp0aPHZn+thobG1Nev3OzHV1unTh3Srdt7qj3GFlNfvyrr1jVUewwK0aFDJbW1Xb2u4O9YG9AyawNaZm3Axm3L66O2tusmn3FUtc83HzRoUObPn5/GxsZm1+1ZvHhxdt9993f13GvXbls/kNZo76eK/b116xra9c+DbZPXFbTM2oCWWRvQMmsDNq69r4+tUhZWrlyZhx9+OEOHDm3aNm7cuCxfvjzz589v2rZ48eI888wzGTdu3NYYCwAAAKA4rT6zZ9WqVXnkkUeSJC+//HLeeOONzJkzJ0kyYsSILFq0KDfffHM+/vGPZ6eddsqrr76a73znO/njH/+Yb37zm03PM3z48IwdOzYXXHBBzj///HTp0iXXXHNNBg8enAMPPHALHR4AAADA9qXVsee1117LWWed1Wzb+tu33357/uEf/iFr1qzJNddck9dffz1du3bN8OHDc8kll2Tvvfdu9rhrr702V1xxRaZOnZq1a9dm7NixufDCC9OxY9XeXQYAAADQrrW6qvTr1y/PPffc2+4zc+bMTXqu7t275/LLL8/ll1/e2jEAAAAAaEFZVwMGAAAA2M6JPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUJCO1R4AgPajUqlJpVJT7THetYaGxjQ0NFZ7DAAAaBNiDwCbpFKpSc+e702HDu3/pNB16xry+usrBR8AAIok9gCwSSqVmnToUMnVdz2epctWVHuczdavT/ec+9l9U6nUiD0AABRJ7AGgVZYuW5GFLy+v9hgAAMBGtP9z8QEAAABoIvYAAAAAFKTVseell17K1KlTc9hhh2WvvfbKIYcc0uJ+P/jBD3LQQQdl6NChOfTQQ/PQQw9tsM+KFStywQUXZMSIERk+fHgmT56cV199tfVHAQAAAECSzYg9L7zwQh555JHssssu2XXXXVvc5957781FF12Uurq6zJgxI8OGDcsZZ5yRX/3qV832O/vsszNv3rxcfPHFufrqq7N48eJMmjQpa9eu3ayDAQAAANjetfoCzePHj8/HPvaxJMmUKVPy61//eoN9rrvuuhx88ME5++yzkySjRo3K888/n+nTp2fGjBlJkieffDKPPvpoZs6cmbFjxyZJBg4cmAkTJuTHP/5xJkyYsLnHBAAAALDdavWZPZXK2z9kyZIlefHFF1NXV9ds+4QJEzJ//vysXr06STJ37tzU1tZmzJgxTfsMGjQoe+65Z+bOndvasQAAAABIG1ygedGiRUneOkvnb+26665Zs2ZNlixZ0rTfwIEDU1NT02y/QYMGNT0HAAAAAK3T6rdxvZPly5cnSWpra5ttX397/f319fXp3r37Bo/v0aNHi28Na42OHdvvh4xVKjXvvFM70qFD+/1ZsO1Z/3ryuqqO0r7vJR2PtQEtszagZdYGbFwp62OLx55qq1Rq8r737VDtMfh/amu7VnsECuR1xZZQ4uuoxGOCLcHagJZZG7Bx7X19bPHY06NHjyRvfax67969m7bX19c3u7+2tjavvPLKBo9fvnx50z6bo6GhMfX1Kzf78dXWqVOHdOv2nmqPscXU16/KunUN1R6DQnToUEltbVevqypZ//0vRUmvI2sDWmZtQMusDdi4bXl91NZ23eQzjrZ47Bk0aFCSt67Js/7f19/u1KlT+vfv37Tf/Pnz09jY2Oy6PYsXL87uu+/+rmZYu3bb+oG0Rns/VezvrVvX0K5/HmybvK7YEkp8HZV4TLAlWBvQMmsDNq69r48tXhb69++fAQMGZM6cOc22z549O6NHj07nzp2TJOPGjcvy5cszf/78pn0WL16cZ555JuPGjdvSYwEAAABsF1p9Zs+qVavyyCOPJElefvnlvPHGG01hZ8SIEenVq1fOPPPMnHvuudl5550zcuTIzJ49OwsWLMidd97Z9DzDhw/P2LFjc8EFF+T8889Ply5dcs0112Tw4ME58MADt9DhAQAAAGxfWh17XnvttZx11lnNtq2/ffvtt2fkyJE55JBDsmrVqsyYMSM33XRTBg4cmGnTpmX48OHNHnfttdfmiiuuyNSpU7N27dqMHTs2F154YTp2LO660QAAAABbRaurSr9+/fLcc8+9435HHXVUjjrqqLfdp3v37rn88stz+eWXt3YMAAAAAFpQ1tWAAQAAALZzYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQTpWewCA7UWlUpNKpabaY2y2Dh389wEAAGgPxB6AraBSqUnPnu8VTAAAgDYn9gBsBZVKTTp0qOTqux7P0mUrqj3OZtlnjx0zccJe1R4DAAB4B2IPwFa0dNmKLHx5ebXH2Cz9duxW7REAAIBN4P0EAAAAAAURewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBxB4AAACAgog9AAAAAAURewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBxB4AAACAgog9AAAAAAURewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBxB4AAACAgog9AAAAAAURewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBxB4AAACAgog9AAAAAAURewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBOlZ7AIBNUanUpEOHt/r0+v9tT9rjzAAAQPsk9gDbvEqlJj17vrcpmNTWdq3yRAAAANsusQcKV6nUpFKpqfYY70qHDpV06FDJ1Xc9nqXLVlR7nM2yzx47ZuKEvao9BgAAsB0Qe6Bgf39GTHu3dNmKLHx5ebXH2Cz9duxW7REAAIDthNgDBVt/nZv2fEZM4qwYAACA1hB7YDvQns+ISZwVAwAA0BplvLcDAAAAgCTO7AFgO9Xer2XV0NCYhobGao8BAMA2SOwBYLvSs3uXNDQ0pra2a7VHeVfWrWvI66+vFHwAANiA2APAdqVb106pVGra9YXL+/XpnnM/u28qlRqxBwCADYg9AGyX2vuFywEAYGPa9wULAAAAAGhG7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoSMdqDwAAbJ4OHSot/m970dDQmIaGxmqPAQBQHLEHANqZnt27pKGhMbW1XZtt//vb27p16xry+usrBR8AgC2sTWLPPffcky9/+csbbJ80aVLOPffcpts/+MEPcvPNN+cPf/hDBg4cmHPOOScf+chH2mIkAChGt66dUqnU5Oq7Hs/SZSuqPc5m6dene8797L6pVGrEHgCALaxNz+y5+eab071796bbffr0afr3e++9NxdddFFOO+20jBo1KrNnz84ZZ5yRu+66K8OGDWvLsQCgCEuXrcjCl5dXewwAALYxbRp7hgwZkl69erV433XXXZeDDz44Z599dpJk1KhRef755zN9+vTMmDGjLccCAAAAKFZVruS4ZMmSvPjii6mrq2u2fcKECZk/f35Wr15djbEAAAAA2r02jT2HHHJI9txzz3z0ox/Nt7/97axbty5JsmjRoiTJwIEDm+2/6667Zs2aNVmyZElbjgUAAABQrDZ5G1fv3r1z5pln5h//8R9TU1OTBx98MNdee22WLVuWqVOnZvnyt64vUFtb2+xx62+vv39zdezYvj569m9VKjXVHmGLam8fA1wa339gW+f/p2hL619fXmfQnLUBG1fK+miT2PPhD384H/7wh5tujx07Nl26dMltt92W0047rS2+ZJNKpSbve98Obfo12HTt7WOAAdi6/J5ga/A6g5ZZG7Bx7X19tOkFmv9WXV1dbrnllvz2t79Njx49kiQrVqxI7969m/apr69Pkqb7N0dDQ2Pq61e+u2GrqFOnDunW7T3VHmOLqa9flXXrGqo9xnarQ4dKu/8/KaBsfk/Qltb/HvQ6g+asDdi4bXl91NZ23eQzjrZa7PlbgwYNSvLWtXvW//v62506dUr//v3f1fOvXbtt/UBao72fKvb31q1raNc/DwDalt8TbA1eZ9AyawM2rr2vj61WFmbPnp0OHTpkr732Sv/+/TNgwIDMmTNng31Gjx6dzp07b62xAAAAAIrSJmf2nHTSSRk5cmQGDx6cJHnggQfy/e9/PxMnTmx629aZZ56Zc889NzvvvHNGjhyZ2bNnZ8GCBbnzzjvbYiQAAACA7UKbxJ6BAwfmhz/8YV555ZU0NDRkwIABueCCC3LCCSc07XPIIYdk1apVmTFjRm666aYMHDgw06ZNy/Dhw9tiJAAAAIDtQpvEngsvvHCT9jvqqKNy1FFHtcUIAAAAANulsq4GDAAAALCdE3sAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACtKx2gPAtqxSqUmlUlPtMTZbhw56LgAAwPZG7IGNqFRq0rPnewUTAAAA2hWxBzaiUqlJhw6VXH3X41m6bEW1x9ks++yxYyZO2KvaYwAAALAViT3wDpYuW5GFLy+v9hibpd+O3ao9AgAAAFuZ96cAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBxB4AAACAgog9AAAAAAURewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBxB4AAACAgog9AAAAAAURewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQkI7VHgAA2H516ND+/7tTQ0NjGhoaqz0GAEATsQcA2Op6du+ShobG1NZ2rfYo79q6dQ15/fWVgg8AsM0QewCAra5b106pVGpy9V2PZ+myFdUeZ7P169M9535231QqNWIPALDNEHsAgKpZumxFFr68vNpjAAAUpf2/UR4AAACAJmIPAAAAQEHEHgAAAICCiD0AAAAABRF7AAAAAAoi9gAAAAAUROwBAAAAKIjYAwAAAFAQsQcAAACgIGIPAAAAQEHEHgAAAICCiD0AAAAABRF7AAAAAAoi9gAAAAAUROwBAAAAKIjYAwAAAFAQsQcAAACgIGIPAAAAQEHEHgAAAICCiD0AAAAABRF7AAAAAAoi9gAAAAAUROwBAAAAKIjYAwAAAFAQsQcAAACgIGIPAAAAQEHEHgAAAICCiD0AAAAABRF7AAAAAAoi9gAAAAAUROwBAAAAKIjYAwAAAFAQsQcAAACgIGIPAAAAQEE6VnsAAID2rkOH9v3fzxoaGtPQ0FjtMQCALUTsAQDYTD27d0lDQ2Nqa7tWe5R3Zd26hrz++krBBwAKIfYAAGymbl07pVKpydV3PZ6ly1ZUe5zN0q9P95z72X1TqdSIPQBQCLEHAOBdWrpsRRa+vLzaYwAAJHGBZgAAAICiiD0AAAAABRF7AAAAAAoi9gAAAAAUROwBAAAAKIhP46JNdejQfntie54dAACA7ZfYQ5vo2b1LGhoaU1vbtdqjAAAAwHZF7KFNdOvaKZVKTa6+6/EsXbai2uNsln322DETJ+xV7TEAAACgVcQe2tTSZSuy8OXl1R5js/TbsVu1RwAAAIBWq+pFSRYuXJjPf/7zGTZsWMaMGZOvfe1rWb16dTVHAgAAAGjXqnZmz/Lly/O5z30uAwYMyPXXX59ly5blyiuvzJtvvpmpU6dWaywAANqhSqUmlUpNs23rP2yhPX3oQkNDYxoaGqs9BgDtXNViz3e/+9385S9/ybRp09KzZ88kybp163LJJZfk1FNPTZ8+fao1GgAA7UilUpOePd+70ajTnj4wYt26hrz++krBB4B3pWqxZ+7cuRk9enRT6EmSurq6fOUrX8m8efNy5JFHVms0AIDtTns6++XvdehQSYcOlXb9wRBJ0q9P95z72X1TqdSIPcA2paWzJ9uj7ensyarFnkWLFuVTn/pUs221tbXp3bt3Fi1aVKWpAAC2Lz27d0lDQ2O7OvtlY9rzB0MAbKve6ezJ9mR7OnuyprGxsSpHOWTIkJx11lk55ZRTmm0/5JBDMnz48Fx22WWb9byNje271NXUJJVKJa+v+GvWrmuo9jibrUvnDun+3s7t+jgcw7ajhONwDNuOEo7DMWw7SjiO9cfwxsrVWddO/wzVqWMl731Pp3b9c0iSjh0q/y++td9joP2oVCpea2yySqXSrn9PJEmHSk26vbdzGhoa8nYVZP3fyd9pv2qoVGpSU7NpZ1gV99HrNTU16dCh/Z9e1rN7l2qPsEWUcByOYdtRwnE4hm1HCcfhGLYdJRxHt/d2rvYI71oJP4fkrb9kwNbgtUZrlPB7Itn01317Xx9Vm762tjYrVmz4nurly5enR48eVZgIAAAAoP2rWuwZNGjQBtfmWbFiRf74xz9m0KBBVZoKAAAAoH2rWuwZN25c/uu//iv19fVN2+bMmZNKpZIxY8ZUaywAAACAdq1qF2hevnx5Dj744AwcODCnnnpqli1bliuvvDKf/OQnM3Xq1GqMBAAAANDuVS32JMnChQtz2WWX5cknn8wOO+yQww47LOecc046dy7jwk8AAAAAW1tVYw8AAAAAW1b7/iwxAAAAAJoRewAAAAAKIvYAAAAAFETsAQAAACiI2AMAAABQELEHAAAAoCBiDwAAAEBBxJ5tyMKFC/P5z38+w4YNy5gxY/K1r30tq1evrvZYsNXcd999Of300zNu3LgMGzYshx12WO6+++40NjY22+8HP/hBDjrooAwdOjSHHnpoHnrooSpNDNXxl7/8JePGjcvgwYPz9NNPN7vP+mB79aMf/SiHH354hg4dmpEjR+bkk0/Om2++2XT/gw8+mEMPPTRDhw7NQQcdlB/+8IdVnBa2jgceeCBHHXVUhg8fnrFjx+ass87KkiVLNtjP7w5K9tJLL2Xq1Kk57LDDstdee+WQQw5pcb9NWQcrVqzIBRdckBEjRmT48OGZPHlyXn311bY+hM0i9mwjli9fns997nNZs2ZNrr/++pxzzjn5/ve/nyuvvLLao8FWc+utt6Zr166ZMmVKbrzxxowbNy4XXXRRpk+f3rTPvffem4suuih1dXWZMWNGhg0bljPOOCO/+tWvqjc4bGU33HBD1q1bt8F264Pt1Y033pjLLrssEyZMyMyZM3PppZemX79+Tevkl7/8Zc4444wMGzYsM2bMSF1dXf7v//2/mTNnTpUnh7bz2GOP5Ywzzshuu+2W6dOn54ILLsizzz6bE088sVkI9buD0r3wwgt55JFHsssuu2TXXXdtcZ9NXQdnn3125s2bl4svvjhXX311Fi9enEmTJmXt2rVb4UhaqZFtwre+9a3GYcOGNf75z39u2vbd7363cc8992x85ZVXqjcYbEWvvfbaBtsuvPDCxn322adx3bp1jY2NjY0HHnhg45e+9KVm+xx99NGNJ5988laZEartd7/7XeOwYcMa/+3f/q1x9913b1ywYEHTfdYH26OFCxc27rXXXo0PP/zwRvc58cQTG48++uhm2770pS811tXVtfV4UDUXXXRR4/jx4xsbGhqats2fP79x9913b/zFL37RtM3vDkq3/u8RjY2Njeeff37jwQcfvME+m7IOnnjiicbdd9+98Wc/+1nTtoULFzYOHjy48d57722Dyd8dZ/ZsI+bOnZvRo0enZ8+eTdvq6urS0NCQefPmVW8w2Ip69eq1wbY999wzb7zxRlauXJklS5bkxRdfTF1dXbN9JkyYkPnz53vbI9uFr371qznmmGMycODAZtutD7ZX99xzT/r165cDDjigxftXr16dxx57LJ/4xCeabZ8wYUIWLlyYpUuXbo0xYatbu3Ztdthhh9TU1DRt6969e5I0vUXe7w62B5XK22ePTV0Hc+fOTW1tbcaMGdO0z6BBg7Lnnntm7ty5W37wd0ns2UYsWrQogwYNarattrY2vXv3zqJFi6o0FVTf448/nj59+qRbt25Na+Hv/5K76667Zs2aNS2+Bx1KMmfOnDz//PP54he/uMF91gfbq6eeeiq77757brjhhowePTof/OAHc8wxx+Spp55Kkvz+97/PmjVrNvhz1vpT+f05i1IdeeSRWbhwYe66666sWLEiS5YsyTe+8Y3stdde2WeffZL43QHJpq+DRYsWZeDAgc0CavJW8NkWf5eIPduI+vr61NbWbrC9R48eWb58eRUmgur75S9/mdmzZ+fEE09Mkqa18PdrZf1ta4WSrVq1KldeeWXOOeecdOvWbYP7rQ+2V3/84x/z6KOP5t///d/zla98JdOnT09NTU1OPPHEvPbaa9YG26399tsv06ZNy9e//vXst99++djHPpbXXnstM2bMSIcOHZL43QHJpq+D+vr6prPj/ta2+nd2sQfYJr3yyis555xzMnLkyEycOLHa40DV3XjjjXn/+9+fT33qU9UeBbYpjY2NWblyZb75zW/mE5/4RA444IDceOONaWxszJ133lnt8aBqnnjiiZx33nn5zGc+k9tuuy3f/OY309DQkFNOOaXZBZqBMok924ja2tqsWLFig+3Lly9Pjx49qjARVE99fX0mTZqUnj175vrrr296n+36tfD3a6W+vr7Z/VCal19+ObfccksmT56cFStWpL6+PitXrkySrFy5Mn/5y1+sD7ZbtbW16dmzZ/bYY4+mbT179sxee+2V3/3ud9YG262vfvWrGTVqVKZMmZJRo0blE5/4RG666aY888wz+fd///ck/mwFyaavg9ra2rzxxhsbPH5b/Tu72LONaOl9fitWrMgf//jHDd5jDiV78803c+qpp2bFihW5+eabm50quX4t/P1aWbRoUTp16pT+/ftv1Vlha1m6dGnWrFmTU045Jfvvv3/233//nHbaaUmSiRMn5vOf/7z1wXZrt9122+h9f/3rX7PzzjunU6dOLa6NJP6cRbEWLlzYLIImyT/8wz/kfe97X37/+98n8WcrSDZ9HQwaNCiLFy9uusD5eosXL94mf5eIPduIcePG5b/+67+a6mHy1oU4K5VKs6t9Q8nWrl2bs88+O4sWLcrNN9+cPn36NLu/f//+GTBgQObMmdNs++zZszN69Oh07tx5a44LW82ee+6Z22+/vdk/X/7yl5Mkl1xySb7yla9YH2y3PvKRj+T111/Pb3/726Ztf/7zn/Ob3/wmQ4YMSefOnTNy5Mjcf//9zR43e/bs7LrrrunXr9/WHhm2ir59++aZZ55ptu3ll1/On//85+y0005J/NkKkk1fB+PGjcvy5cszf/78pn0WL16cZ555JuPGjduqM2+KjtUegLccc8wxueOOO/LFL34xp556apYtW5avfe1rOeaYYzb4Cy+U6pJLLslDDz2UKVOm5I033sivfvWrpvv22muvdO7cOWeeeWbOPffc7Lzzzhk5cmRmz56dBQsWuC4DRautrc3IkSNbvG/IkCEZMmRIklgfbJc+9rGPZejQoZk8eXLOOeecdOnSJTfddFM6d+6c4447Lkly+umnZ+LEibn44otTV1eXxx57LP/5n/+Za665psrTQ9s55phjcvnll+erX/1qxo8fn9dff73p+m9/+xHTfndQulWrVuWRRx5J8lbwfOONN5rCzogRI9KrV69NWgfDhw/P2LFjc8EFF+T8889Ply5dcs0112Tw4ME58MADq3Jsb6em8e/PQaJqFi5cmMsuuyxPPvlkdthhhxx22GE555xzFHW2G+PHj8/LL7/c4n0PPPBA0399/cEPfpAZM2bkD3/4QwYOHJgvfelL+chHPrI1R4Wqe+yxxzJx4sTcfffdGTp0aNN264Pt0Z/+9KdcccUVeeihh7JmzZrst99++fKXv9zsLV4PPPBArr322ixevDh9+/bNKaeckk9/+tNVnBraVmNjY7773e/m3/7t37JkyZLssMMOGTZsWM4555zsuuuuzfb1u4OSLV26NB/96EdbvO/2229v+g9qm7IOVqxYkSuuuCI/+clPsnbt2owdOzYXXnjhNnmChtgDAAAAUBDX7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AABVNXjw4Fx66aVvu8/SpUszePDg3HPPPW06y/jx43Pqqae+436PPfZYBg8enMcee6xVzz9lypQMHz58c8cDANgkHas9AABQrueeey7Tp0/P008/nf/93/9Nz549s9tuu2X8+PE54YQT2vzrT5kyJT/60Y/ecb8jjjgiV155ZZvPAwCwNYg9AECbeOKJJzJx4sT07ds3Rx11VHr37p3/+Z//yVNPPZXbb7+9VbFnp512yoIFC9KxY+v+6HL00Udn9OjRTbeXLl2a6667LkcffXT23Xffpu0777xzq553//33z4IFC9KpU6dWPQ4AYGsQewCANvGtb30r3bt3z913353a2tpm97322muteq6ampp06dKl1TMMHz682dumnn766Vx33XUZNmxYDjvssFY/33qVSmWz5gEA2BpcswcAaBO///3vs9tuu20QepLk/e9//9s+9oYbbsgee+yRO+64I0nL1+xZf/2bZcuW5Qtf+EKGDx+eUaNG5aqrrsq6deve1ey//OUv8+lPfzpDhw7NRz/60cyaNavZ/Ru7Zs9TTz2VSZMmZf/998+wYcPyyU9+Mrfddtvbfq3f/va3GTVqVE444YT85S9/SfL/XzvoneZIkvr6+vzzP/9zDjjggHzwgx/Mxz/+8dx0001paGhott+9996bI488MsOHD88+++yzwWxr1qzJtGnTcuCBB2bo0KEZOXJkjj322MybN68V3zkAYFsg9gAAbWKnnXbKb37zmzz//POtetw111yT6667Lpdeeuk7vtVr3bp1Oemkk9KzZ8+cd955GTFiRG655ZZ873vf2+y5X3rppZx11lkZM2ZMpkyZkh49emTKlCl54YUX3vZx8+bNy2c/+9ksXLgwEydOzPnnn5+RI0fm4Ycf3uhjFixYkM997nPZa6+9MmPGjOywww6tmmPVqlU5/vjj8x//8R85/PDDc+GFF2afffbJN77xjVxxxRXNZvvSl76U2tranHvuufmnf/qnjBgxIk888UTTPtOmTcu0adMycuTITJ06Naeddlr69u2b3/zmN5vxXQQAqsnbuACANnHiiSdm0qRJOfzww7P33ntn3333zejRozNy5MiNXuvmqquuyq233porrrgiRxxxxDt+jb/+9a+pq6vLF7/4xSTJsccemyOOOCJ33313jjvuuM2ae/Hixbnrrruy3377JUnq6upywAEH5J577sn555/f4mPWrVuXqVOnZscdd8ysWbOanc3U2NjY4mMef/zxnHLKKdlvv/1y/fXXp3Pnzq2e4zvf+U6WLFmSH/3oRxkwYECS5JhjjsmOO+6YmTNn5sQTT8wHPvCBPPzww+nWrVtmzpyZDh06tDjPww8/nAMOOCCXXXbZpn+zAIBtkjN7AIA2MWbMmHz3u9/N+PHj8+yzz+bmm2/OSSedlHHjxuWBBx5otm9jY2MuvfTS3H777fmXf/mXTQo96x177LHNbu+7775ZunTpZs+92267NQWWJOnVq1cGDhyYJUuWbPQxzzzzTJYuXZqJEydu8La1mpqaDfb/+c9/npNPPjmjR49uMfRs6hxz5szJvvvum9ra2vzpT39q+udDH/pQ1q1bl1/84hdJktra2qxatept35JVW1ubF154IS+++OJG9wEA2gdn9gAAbWbvvffOtGnTsnr16jz77LP56U9/mltvvTVnnXVWZs2ald122y1JMmvWrKxcuTIXX3xxDjnkkE1+/i5duqRXr17NtvXo0SPLly/f7Jk/8IEPbLDtnZ5zfYDZfffd3/H5//rXv+bUU0/NkCFDcu211270E8Y2ZY6XXnopzz33XLNPHPtbf/rTn5Ikxx13XO67775MmjQpffr0yZgxY1JXV5dx48Y17Tt58uR84QtfyEEHHZTdd989Y8eOzWGHHZY99tjjHY8JANi2iD0AQJvr3Llz9t577+y9994ZMGBAvvzlL2fOnDk544wzkiT77LNPnn322dx1112pq6tLz549N+l5N/aWpHejLZ7zb3Xu3Dnjxo3Lgw8+mJ/97Gf5yEc+stlzNDQ0ZMyYMTn55JNbvH/9W7ve//73Z9asWXn00Uczd+7czJ07N/fcc08OP/zwXHXVVUne+jj5n/zkJ3nggQcyb9683H333bnttttyySWX5Kijjtq8gwUAqsLbuACAreqDH/xgkuTVV19t2rbLLrtk5syZefXVV3PyySfnjTfeqNZ4m6V///5JskkXo66pqcnVV1+dUaNG5ayzztrgE71aY+edd87KlSvzoQ99qMV/+vbt27Rv586dM378+Fx88cX56U9/mqOPPjqzZs3KSy+91LRPz54986lPfSrf+MY38vDDD2fw4MG5/vrrN3s+AKA6xB4AoE38/Oc/b/HixI888kiSZNCgQc2277HHHrnpppuycOHCnH766XnzzTe3ypxbwpAhQ9KvX7/cfvvtqa+vb3ZfS9+Dzp07Z9q0aRk6dGhOO+20LFiwYLO+bl1dXZ588sn87Gc/2+C++vr6rF27Nkny5z//udl9lUolgwcPTpKsXr26xX122GGH7Lzzzk33AwDth7dxAQBt4qtf/WpWrVqVj3/84xk0aFDWrFmTJ554Ivfdd1922mmnHHnkkRs8ZtiwYbnhhhtyyimnZPLkyZk+ffpGP7lrW1KpVHLxxRfn9NNPz+GHH54jjzwyvXv3zqJFi/K73/0uM2fO3OAx73nPe/Ltb387EydOzKRJk3LHHXds0jV//tZJJ52UBx98MKeddlqOOOKIDBkyJKtWrcrzzz+f+++/Pw888EB69eqVCy+8MMuXL8+oUaPSp0+f/OEPf8idd96ZPffcM7vuumuS5OCDD86IESMyZMiQ9OzZM08//XTuv//+HH/88VvkewQAbD1iDwDQJs4777zMmTMnjzzySL73ve9lzZo16du3b4477ricfvrpG3xq1XqjR4/Otddem8mTJ+e8887L17/+9a08+eb58Ic/nNtuuy3Tp0/PLbfcksbGxvTv3z+f+cxnNvqY9R+Hfvzxx+fEE0/MXXfdlV122WWTv2bXrl1zxx135Nvf/nbmzJmTWbNmpVu3bhkwYEDOPPPMdO/ePUly6KGH5vvf/37+9V//NfX19endu3fq6upy5plnplJ560TvE044IQ8++GDmzZuX1atXp2/fvjn77LNz0kknvbtvDACw1dU0tnRuMQAAAADtkmv2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQcQeAAAAgIKIPQAAAAAFEXsAAAAACiL2AAAAABRE7AEAAAAoiNgDAAAAUBCxBwAAAKAgYg8AAABAQf4/s2enxMXRAQkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count   768.0000\n",
            "mean     79.7995\n",
            "std     115.2440\n",
            "min       0.0000\n",
            "1%        0.0000\n",
            "5%        0.0000\n",
            "10%       0.0000\n",
            "20%       0.0000\n",
            "30%       0.0000\n",
            "40%       0.0000\n",
            "50%      30.5000\n",
            "60%      72.2000\n",
            "70%     106.0000\n",
            "80%     150.0000\n",
            "90%     210.0000\n",
            "95%     293.0000\n",
            "99%     519.9000\n",
            "max     846.0000\n",
            "Name: Insulin, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count   768.0000\n",
            "mean     31.9926\n",
            "std       7.8842\n",
            "min       0.0000\n",
            "1%        0.0000\n",
            "5%       21.8000\n",
            "10%      23.6000\n",
            "20%      25.9000\n",
            "30%      28.2000\n",
            "40%      30.1000\n",
            "50%      32.0000\n",
            "60%      33.7000\n",
            "70%      35.4900\n",
            "80%      37.8000\n",
            "90%      41.5000\n",
            "95%      44.3950\n",
            "99%      50.7590\n",
            "max      67.1000\n",
            "Name: BMI, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count   768.0000\n",
            "mean      0.4719\n",
            "std       0.3313\n",
            "min       0.0780\n",
            "1%        0.0947\n",
            "5%        0.1404\n",
            "10%       0.1650\n",
            "20%       0.2194\n",
            "30%       0.2590\n",
            "40%       0.3028\n",
            "50%       0.3725\n",
            "60%       0.4542\n",
            "70%       0.5637\n",
            "80%       0.6870\n",
            "90%       0.8786\n",
            "95%       1.1328\n",
            "99%       1.6983\n",
            "max       2.4200\n",
            "Name: DiabetesPedigreeFunction, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count   768.0000\n",
            "mean     33.2409\n",
            "std      11.7602\n",
            "min      21.0000\n",
            "1%       21.0000\n",
            "5%       21.0000\n",
            "10%      22.0000\n",
            "20%      23.0000\n",
            "30%      25.0000\n",
            "40%      27.0000\n",
            "50%      29.0000\n",
            "60%      33.0000\n",
            "70%      38.0000\n",
            "80%      42.6000\n",
            "90%      51.0000\n",
            "95%      58.0000\n",
            "99%      67.0000\n",
            "max      81.0000\n",
            "Name: Age, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_num_summary(dataframe):\n",
        "  cat_cols, num_cols, cat_but_car, num_but_cat = grab_col_names(dataframe)\n",
        "  plt.figure(figsize=(12,8))\n",
        "  for index, col in enumerate(num_cols):\n",
        "    plt.subplot(3,3, index+1)\n",
        "    plt.tight_layout()\n",
        "    dataframe[col].hist(bins=20)\n",
        "    plt.title(col)"
      ],
      "metadata": {
        "id": "2DkqVgV5qu9I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_num_summary(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "kOXGFh9iqwS6",
        "outputId": "8fd8ea61-b74b-4bd2-8ba5-4e57d7b9c1d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Target Analysis"
      ],
      "metadata": {
        "id": "FuS0-W83qigg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def target_summary_with_num(dataframe, target, numerical_col):\n",
        "  print(dataframe.groupby(target).agg({numerical_col: \"mean\"}))\n",
        "  print(\"#############################################\")"
      ],
      "metadata": {
        "id": "hNH45szQqFNs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def target_summary_with_num_df(dataframe, target):\n",
        "  cat_cols, num_cols, cat_but_car, num_but_cat = grab_col_names(df)\n",
        "  for col in num_cols:\n",
        "    target_summary_with_num(dataframe, target, col)"
      ],
      "metadata": {
        "id": "u7D__gD3qKsi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target_summary_with_num_df(df, \"Outcome\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TBQztuEgqN0N",
        "outputId": "8c026299-53a1-477b-c2db-e20f66721c75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         Pregnancies\n",
            "Outcome             \n",
            "0             3.2980\n",
            "1             4.8657\n",
            "#############################################\n",
            "         Glucose\n",
            "Outcome         \n",
            "0       109.9800\n",
            "1       141.2575\n",
            "#############################################\n",
            "         BloodPressure\n",
            "Outcome               \n",
            "0              68.1840\n",
            "1              70.8246\n",
            "#############################################\n",
            "         SkinThickness\n",
            "Outcome               \n",
            "0              19.6640\n",
            "1              22.1642\n",
            "#############################################\n",
            "         Insulin\n",
            "Outcome         \n",
            "0        68.7920\n",
            "1       100.3358\n",
            "#############################################\n",
            "            BMI\n",
            "Outcome        \n",
            "0       30.3042\n",
            "1       35.1425\n",
            "#############################################\n",
            "         DiabetesPedigreeFunction\n",
            "Outcome                          \n",
            "0                          0.4297\n",
            "1                          0.5505\n",
            "#############################################\n",
            "            Age\n",
            "Outcome        \n",
            "0       31.1900\n",
            "1       37.0672\n",
            "#############################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Correlation Analysis"
      ],
      "metadata": {
        "id": "YClfoGBSqf91"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def correlated_cols(dataframe, plot=False):\n",
        "  num_cols = [col for col in dataframe.columns if dataframe[col].dtypes in [\"uint8\", \"int64\", \"float64\"]]\n",
        "  corr = dataframe[num_cols].corr()\n",
        "  print(corr)\n",
        "\n",
        "  if plot:\n",
        "    sns.set(rc={'figure.figsize': (14,7)})\n",
        "    sns.heatmap(corr, cmap='magma', annot=True, fmt=\".2f\")\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "BOxAaEJQqQdq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correlated_cols(df, plot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "id": "LzcGyZNwrE_T",
        "outputId": "7f5d54ab-d075-42e1-da21-ed81ed31d150"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                          Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin    BMI  DiabetesPedigreeFunction     Age  Outcome\n",
            "Pregnancies                    1.0000   0.1295         0.1413        -0.0817  -0.0735 0.0177                   -0.0335  0.5443   0.2219\n",
            "Glucose                        0.1295   1.0000         0.1526         0.0573   0.3314 0.2211                    0.1373  0.2635   0.4666\n",
            "BloodPressure                  0.1413   0.1526         1.0000         0.2074   0.0889 0.2818                    0.0413  0.2395   0.0651\n",
            "SkinThickness                 -0.0817   0.0573         0.2074         1.0000   0.4368 0.3926                    0.1839 -0.1140   0.0748\n",
            "Insulin                       -0.0735   0.3314         0.0889         0.4368   1.0000 0.1979                    0.1851 -0.0422   0.1305\n",
            "BMI                            0.0177   0.2211         0.2818         0.3926   0.1979 1.0000                    0.1406  0.0362   0.2927\n",
            "DiabetesPedigreeFunction      -0.0335   0.1373         0.0413         0.1839   0.1851 0.1406                    1.0000  0.0336   0.1738\n",
            "Age                            0.5443   0.2635         0.2395        -0.1140  -0.0422 0.0362                    0.0336  1.0000   0.2384\n",
            "Outcome                        0.2219   0.4666         0.0651         0.0748   0.1305 0.2927                    0.1738  0.2384   1.0000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def high_correlated_cols(dataframe, corr_th = 0.90, plot=False):\n",
        "  num_cols = [col for col in dataframe.columns if dataframe[col].dtypes in [\"uint8\", \"int64\", \"float64\"]]\n",
        "  corr = dataframe[num_cols].corr()\n",
        "  corr_matrix = corr.abs()\n",
        "  upper_triangular_matrix = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n",
        "  drop_list = [col for col in upper_triangular_matrix.columns if any(upper_triangular_matrix[col] > corr_th)]\n",
        "  if drop_list == []:\n",
        "    print(\"Aftre corelation analysis, we dont need to remove variables\")\n",
        "\n",
        "  if plot:\n",
        "    sns.set(rc={'figure.figsize': (14,7)})\n",
        "    sns.heatmap(corr, cmap='RdBu', annot=True, fmt=\".2f\")\n",
        "    plt.show()\n",
        "\n",
        "  return drop_list"
      ],
      "metadata": {
        "id": "8fIETRtyrGrn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drop_list = high_correlated_cols(df, plot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "P1K5H9oIrSqy",
        "outputId": "94d221c8-63b0-49cd-8f88-22a28813bdbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Aftre corelation analysis, we dont need to remove variables\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Missing Value Analysis\n",
        "\n",
        "**Not:** It is known that a human cannot have variable values 0 other than Pregnancies and Outcome. Therefore, an action decision should be taken regarding these values. Values that are 0 can be assigned NaN.\n"
      ],
      "metadata": {
        "id": "pA6E9uGqrl0m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hh9lS_lorUUt",
        "outputId": "00c21f13-bf51-442c-aeb9-78a9805fe40b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                 0\n",
              "Glucose                     0\n",
              "BloodPressure               0\n",
              "SkinThickness               0\n",
              "Insulin                     0\n",
              "BMI                         0\n",
              "DiabetesPedigreeFunction    0\n",
              "Age                         0\n",
              "Outcome                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "zero_columns = [col for col in df.columns if (df[col].min() == 0 and col not in [\"Pregnancies\", \"Outcome\"])]\n",
        "for col in zero_columns:\n",
        "  df[col] = np.where(df[col]==0, np.nan, df[col])"
      ],
      "metadata": {
        "id": "Bu35uMI9rrSF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oItjnUd-sCfn",
        "outputId": "8cae119e-f097-4c72-8190-7ce9979659c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                   0\n",
              "Glucose                       5\n",
              "BloodPressure                35\n",
              "SkinThickness               227\n",
              "Insulin                     374\n",
              "BMI                          11\n",
              "DiabetesPedigreeFunction      0\n",
              "Age                           0\n",
              "Outcome                       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def missing_value_table(dataframe, na_names=False):\n",
        "  na_columns = [col for col in dataframe.columns if dataframe[col].isnull().sum() > 0]\n",
        "  n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False)\n",
        "  ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False)\n",
        "  missing_df = pd.concat([n_miss, np.round(ratio,2)], axis=1, keys=[\"n_miss\", \"ratio\"])\n",
        "  print(missing_df)\n",
        "\n",
        "  if na_names:\n",
        "    print(25*\"#\", \"NA Columns\", 25*\"#\")\n",
        "    return na_columns"
      ],
      "metadata": {
        "id": "HIxBIUV3sEBI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "na_columns = missing_value_table(df, na_names=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8azXUXk9sU15",
        "outputId": "f6d0f2dc-606d-4761-8f4f-4b28a085bce8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               n_miss   ratio\n",
            "Insulin           374 48.7000\n",
            "SkinThickness     227 29.5600\n",
            "BloodPressure      35  4.5600\n",
            "BMI                11  1.4300\n",
            "Glucose             5  0.6500\n",
            "######################### NA Columns #########################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def fill_na_with_median(dataframe):\n",
        "  dataframe = dataframe.apply(lambda x: x.fillna(x.median()) if x.dtype not in [\"category\", \"object\", \"bool\"] else x, axis=0)\n",
        "  return dataframe"
      ],
      "metadata": {
        "id": "iAMakROgsXg5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = fill_na_with_median(df)"
      ],
      "metadata": {
        "id": "oYpyNeSGsicL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dzU8EdZ3sl3E",
        "outputId": "ead9425a-f268-4db3-c685-facbfdcb9d8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                 0\n",
              "Glucose                     0\n",
              "BloodPressure               0\n",
              "SkinThickness               0\n",
              "Insulin                     0\n",
              "BMI                         0\n",
              "DiabetesPedigreeFunction    0\n",
              "Age                         0\n",
              "Outcome                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Outlier Analysis"
      ],
      "metadata": {
        "id": "uT4aUGN7s6bY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def outlier_thresholds(dataframe,col_name,q1=0.05,q3=0.95):\n",
        "  quartile1 = dataframe[col_name].quantile(q1)\n",
        "  quartile3 = dataframe[col_name].quantile(q3)\n",
        "  interquartile_range = quartile3 - quartile1\n",
        "  low_limit = quartile1 - 1.5 * interquartile_range\n",
        "  up_limit = quartile3 + 1.5 * interquartile_range\n",
        "  return low_limit,up_limit"
      ],
      "metadata": {
        "id": "_5JXBBADsrqD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def check_outlier(dataframe, col_name):\n",
        "  low_limit,up_limit = outlier_thresholds(dataframe,col_name)\n",
        "  if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):\n",
        "    return True\n",
        "  else:\n",
        "    return False"
      ],
      "metadata": {
        "id": "7TFxv9mYs8fZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def replace_with_thresholds(dataframe, col_name):\n",
        "  low_limit, up_limit = outlier_thresholds(dataframe, col_name)\n",
        "  dataframe.loc[(dataframe[col_name] < low_limit), col_name] = low_limit\n",
        "  dataframe.loc[(dataframe[col_name] > up_limit), col_name] = up_limit"
      ],
      "metadata": {
        "id": "UkBRXw1Ns-1U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in df.columns:\n",
        "  print(col, check_outlier(df, col))\n",
        "  if check_outlier(df, col):\n",
        "    replace_with_thresholds(df, col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70JS9U6rtAs5",
        "outputId": "fbfa2d29-4862-49c9-8e6a-57600c3d0679"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pregnancies False\n",
            "Glucose False\n",
            "BloodPressure False\n",
            "SkinThickness True\n",
            "Insulin True\n",
            "BMI False\n",
            "DiabetesPedigreeFunction False\n",
            "Age False\n",
            "Outcome False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in df.columns:\n",
        "  print(col, check_outlier(df, col))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NTnwgQmXtE0O",
        "outputId": "6897748d-5a2a-4d6d-c8cc-c13986b1a2a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pregnancies False\n",
            "Glucose False\n",
            "BloodPressure False\n",
            "SkinThickness False\n",
            "Insulin False\n",
            "BMI False\n",
            "DiabetesPedigreeFunction False\n",
            "Age False\n",
            "Outcome False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Encoding & Scaling"
      ],
      "metadata": {
        "id": "Yskqk8VKtYb-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols, num_cols, cat_but_car, num_but_cat = grab_col_names(df)"
      ],
      "metadata": {
        "id": "clPEBAH2tLF2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def label_encoder(dataframe, binary_col):\n",
        "  labelencoder = LabelEncoder()\n",
        "  dataframe[binary_col] = labelencoder.fit_transform(dataframe[binary_col])\n",
        "  return dataframe"
      ],
      "metadata": {
        "id": "ZDnAQyBXte9v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "binary_cols = [col for col in df.columns if df[col].dtypes == \"O\" and df[col].nunique() == 2]\n",
        "for col in binary_cols:\n",
        "    df = label_encoder(df, col)"
      ],
      "metadata": {
        "id": "FVDV8EYRtjPZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols = [col for col in cat_cols if col not in binary_cols and col not in [\"Outcome\"]]\n",
        "def one_hot_encoding(dataframe, cat_cols, drop_first=False):\n",
        "    dataframe = pd.get_dummies(dataframe, columns=cat_cols, drop_first=drop_first)\n",
        "    return dataframe"
      ],
      "metadata": {
        "id": "XJ3d3XI6tkoj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = one_hot_encoding(df, cat_cols, drop_first=True)"
      ],
      "metadata": {
        "id": "kfxrKLDxt3Nc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "HWQoQD1Zt5B0",
        "outputId": "f75484b2-1c2b-48c0-d597-70e9831d3894"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin     BMI  DiabetesPedigreeFunction  Age  Outcome\n",
              "0            6 148.0000        72.0000        35.0000 125.0000 33.6000                    0.6270   50        1\n",
              "1            1  85.0000        66.0000        29.0000 125.0000 26.6000                    0.3510   31        0\n",
              "2            8 183.0000        64.0000        29.0000 125.0000 23.3000                    0.6720   32        1\n",
              "3            1  89.0000        66.0000        23.0000  94.0000 28.1000                    0.1670   21        0\n",
              "4            0 137.0000        40.0000        35.0000 168.0000 43.1000                    2.2880   33        1"
            ],
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              "      <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",
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              "      <th>Outcome</th>\n",
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              "      <td>21</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137.0000</td>\n",
              "      <td>40.0000</td>\n",
              "      <td>35.0000</td>\n",
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              "      <td>43.1000</td>\n",
              "      <td>2.2880</td>\n",
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              "        document.querySelector('#df-1041bafe-d7a1-45f7-bd91-404609cf9152 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-1041bafe-d7a1-45f7-bd91-404609cf9152');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-baa79566-4c02-42e8-93f5-0d59ec1c58cf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "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\": 30.43828582241517,\n        \"min\": 44.0,\n        \"max\": 199.0,\n        \"num_unique_values\": 135,\n        \"samples\": [\n          135.0,\n          134.0,\n          86.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.096641733978139,\n        \"min\": 24.0,\n        \"max\": 122.0,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          102.0,\n          85.0,\n          86.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.689744008717986,\n        \"min\": 7.0,\n        \"max\": 88.475,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          36.0,\n          12.0,\n          28.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 83.69117023931526,\n        \"min\": 14.0,\n        \"max\": 657.5,\n        \"num_unique_values\": 183,\n        \"samples\": [\n          70.0,\n          18.0,\n          77.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.875176818080995,\n        \"min\": 18.2,\n        \"max\": 67.1,\n        \"num_unique_values\": 247,\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\": 0.3313285950127749,\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": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Logistic Regression Model Using StatsModel"
      ],
      "metadata": {
        "id": "ILhVlgSHuCnX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def Logistic_Regression_Stast(dataframe, target):\n",
        "  X = dataframe.drop(target, axis=1)\n",
        "  y = dataframe[target]\n",
        "  loj_model = sm.Logit(y, X).fit()\n",
        "  print(loj_model.summary())\n",
        "  return loj_model"
      ],
      "metadata": {
        "id": "F6msOfIdyhgc"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loj_model = Logistic_Regression_Stast(df, \"Outcome\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RidZC7w2zH59",
        "outputId": "fbf21e0b-d170-42fb-8d46-b22d2fc2f82d"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimization terminated successfully.\n",
            "         Current function value: 0.577201\n",
            "         Iterations 5\n",
            "                           Logit Regression Results                           \n",
            "==============================================================================\n",
            "Dep. Variable:                Outcome   No. Observations:                  768\n",
            "Model:                          Logit   Df Residuals:                      760\n",
            "Method:                           MLE   Df Model:                            7\n",
            "Date:                Sat, 04 May 2024   Pseudo R-squ.:                  0.1076\n",
            "Time:                        08:51:19   Log-Likelihood:                -443.29\n",
            "converged:                       True   LL-Null:                       -496.74\n",
            "Covariance Type:            nonrobust   LLR p-value:                 4.028e-20\n",
            "============================================================================================\n",
            "                               coef    std err          z      P>|z|      [0.025      0.975]\n",
            "--------------------------------------------------------------------------------------------\n",
            "Pregnancies                  0.1339      0.029      4.562      0.000       0.076       0.191\n",
            "Glucose                      0.0226      0.003      7.047      0.000       0.016       0.029\n",
            "BloodPressure               -0.0597      0.007     -8.496      0.000      -0.073      -0.046\n",
            "SkinThickness               -0.0082      0.011     -0.722      0.471      -0.030       0.014\n",
            "Insulin                     -0.0004      0.001     -0.421      0.674      -0.003       0.002\n",
            "BMI                          0.0203      0.015      1.381      0.167      -0.009       0.049\n",
            "DiabetesPedigreeFunction     0.3772      0.250      1.511      0.131      -0.112       0.866\n",
            "Age                         -0.0024      0.009     -0.270      0.787      -0.020       0.015\n",
            "============================================================================================\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Logistic Regression Model Using Sklearn"
      ],
      "metadata": {
        "id": "qQzcATEyzWCF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def Logistic_Regression_model(dataframe, target, test_size=0.30, cv=10, results=False, conf_matrix=False, c_report=False, roc=False, save_model=False):\n",
        "  X = dataframe.drop(target, axis=1)\n",
        "  y = dataframe[target]\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=1)\n",
        "  logistic_model = LogisticRegression(solver=\"liblinear\").fit(X_train, y_train)\n",
        "\n",
        "  if results:\n",
        "    acc_train = accuracy_score(y_train, logistic_model.predict(X_train))\n",
        "    acc_test = accuracy_score(y_test, logistic_model.predict(X_test))\n",
        "    r2 = logistic_model.score(X, y)\n",
        "    r2_train = logistic_model.score(X_train, y_train)\n",
        "    r2_test = logistic_model.score(X_test, y_test)\n",
        "    cv_train = cross_val_score(logistic_model, X_train, y_train, cv=cv).mean()\n",
        "    cv_test = cross_val_score(logistic_model, X_test, y_test, cv=cv).mean()\n",
        "    cv_results = cross_validate(logistic_model, X, y, cv=cv, scoring=[\"accuracy\", \"precision\", \"recall\", \"f1\", \"roc_auc\"])\n",
        "    print(\"##################### Accuracy & R2 Metrics ######################\")\n",
        "    print(\"Accuracy_train: \", \"%.3f\" % acc_train)\n",
        "    print(\"Accuracy_test: \", \"%.3f\" % acc_test)\n",
        "    print(\"R2: \", \"%.3f\" % r2)\n",
        "    print(\"R2_train: \", \"%.3f\" % r2_train)\n",
        "    print(\"R2_test: \", \"%.3f\" % r2_test)\n",
        "    print(\"Cross Validation Score(Train): \", \"%.3f\" % cv_train)\n",
        "    print(\"Cross Validation Score(Test): \", \"%.3f\" % cv_test)\n",
        "    print(\"Cross Validate (Accuracy): \", \"%.3f\" % cv_results['test_accuracy'].mean())\n",
        "    print(\"Cross Validate (Precision): \", \"%.3f\" % cv_results['test_precision'].mean())\n",
        "    print(\"Cross Validate (Recall): \", \"%.3f\" % cv_results['test_recall'].mean())\n",
        "    print(\"Cross Validate (F1): \", \"%.3f\" % cv_results['test_f1'].mean())\n",
        "    print(\"Cross Validate (ROC AUC Score): \", \"%.3f\" % cv_results['test_roc_auc'].mean())\n",
        "\n",
        "  if conf_matrix:\n",
        "    print(\"##################### Confusion Matrix(Train) ######################\")\n",
        "    print(confusion_matrix(y_train, logistic_model.predict(X_train)))\n",
        "    print(\"##################### Confusion Matrix(Test) ######################\")\n",
        "    print(confusion_matrix(y_test, logistic_model.predict(X_test)))\n",
        "    print(\"##################### Confusion Matrix(All) ######################\")\n",
        "    print(confusion_matrix(y, logistic_model.predict(X)))\n",
        "    plt.figure(figsize=(5,5))\n",
        "    acc = accuracy_score(y, logistic_model.predict(X))\n",
        "    cm = confusion_matrix(y, logistic_model.predict(X))\n",
        "    sns.heatmap(cm, annot=True, fmt=\".0f\")\n",
        "    plt.xlabel('y_pred')\n",
        "    plt.ylabel('y')\n",
        "    plt.title('Accuracy Score: {}'.format(acc), size=10)\n",
        "    plt.show()\n",
        "\n",
        "  if c_report:\n",
        "    print(\"###################### Classification Report(Train) ######################\")\n",
        "    print(classification_report(y_train, logistic_model.predict(X_train)))\n",
        "    print(\"###################### Classification Report(Test) ######################\")\n",
        "    print(classification_report(y_test, logistic_model.predict(X_test)))\n",
        "    print(\"###################### Classification Report(All) ######################\")\n",
        "    print(classification_report(y, logistic_model.predict(X)))\n",
        "\n",
        "  if roc:\n",
        "    print(\"###################### Roc Auc Score(Train) ######################\")\n",
        "    logistic_roc_auc_train = roc_auc_score(y_train, logistic_model.predict(X_train))\n",
        "    print(\"Roc Auc Train: \", \"%.3f\" % logistic_roc_auc_train)\n",
        "    fpr, tpr, thresholds = roc_curve(y_train, logistic_model.predict_proba(X_train)[:,1])\n",
        "    plt.figure(figsize=(5,5))\n",
        "    plt.plot(fpr, tpr, label='AUC (area = %.3f)' % logistic_roc_auc_train )\n",
        "    plt.plot([0, 1], [0, 1], 'r--')\n",
        "    plt.xlim([0.0, 1.0])\n",
        "    plt.ylim([0.0, 1.05])\n",
        "    plt.xlabel('False Positive Ratio')\n",
        "    plt.ylabel('True Positive Ratio')\n",
        "    plt.title('ROC')\n",
        "    plt.show()\n",
        "\n",
        "    print(\"###################### Roc Auc Score(Test) ######################\")\n",
        "    logistic_roc_auc_test = roc_auc_score(y_test, logistic_model.predict(X_test))\n",
        "    print(\"Roc Auc Test: \", \"%.3f\" % logistic_roc_auc_test)\n",
        "    fpr, tpr, thresholds = roc_curve(y_test, logistic_model.predict_proba(X_test)[:,1])\n",
        "    plt.figure(figsize=(5,5))\n",
        "    plt.plot(fpr, tpr, label='AUC (area = %.3f)' % logistic_roc_auc_test )\n",
        "    plt.plot([0, 1], [0, 1], 'r--')\n",
        "    plt.xlim([0.0, 1.0])\n",
        "    plt.ylim([0.0, 1.05])\n",
        "    plt.xlabel('False Positive Ratio')\n",
        "    plt.ylabel('True Positive Ratio')\n",
        "    plt.title('ROC')\n",
        "    plt.show()\n",
        "\n",
        "  if save_model:\n",
        "    joblib.dump(logistic_model, \"logistic_model.pkl\")\n",
        "\n",
        "  return logistic_model"
      ],
      "metadata": {
        "id": "oFrd_5A4yY4j"
      },
      "execution_count": 177,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "logistic_model = Logistic_Regression_model(df, \"Outcome\", results=True, conf_matrix=True, c_report=True, roc=True, save_model=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "aXBMcaIS2G40",
        "outputId": "df683c2f-cc7e-4982-f86b-89b7a9d5e2f4"
      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "##################### Accuracy & R2 Metrics ######################\n",
            "Accuracy_train:  0.765\n",
            "Accuracy_test:  0.784\n",
            "R2:  0.771\n",
            "R2_train:  0.765\n",
            "R2_test:  0.784\n",
            "Cross Validation Score(Train):  0.756\n",
            "Cross Validation Score(Test):  0.766\n",
            "Cross Validate (Accuracy):  0.763\n",
            "Cross Validate (Precision):  0.722\n",
            "Cross Validate (Recall):  0.541\n",
            "Cross Validate (F1):  0.614\n",
            "Cross Validate (ROC AUC Score):  0.829\n",
            "##################### Confusion Matrix(Train) ######################\n",
            "[[317  37]\n",
            " [ 89  94]]\n",
            "##################### Confusion Matrix(Test) ######################\n",
            "[[131  15]\n",
            " [ 35  50]]\n",
            "##################### Confusion Matrix(All) ######################\n",
            "[[448  52]\n",
            " [124 144]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "###################### Classification Report(Train) ######################\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.90      0.83       354\n",
            "           1       0.72      0.51      0.60       183\n",
            "\n",
            "    accuracy                           0.77       537\n",
            "   macro avg       0.75      0.70      0.72       537\n",
            "weighted avg       0.76      0.77      0.75       537\n",
            "\n",
            "###################### Classification Report(Test) ######################\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.90      0.84       146\n",
            "           1       0.77      0.59      0.67        85\n",
            "\n",
            "    accuracy                           0.78       231\n",
            "   macro avg       0.78      0.74      0.75       231\n",
            "weighted avg       0.78      0.78      0.78       231\n",
            "\n",
            "###################### Classification Report(All) ######################\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.90      0.84       500\n",
            "           1       0.73      0.54      0.62       268\n",
            "\n",
            "    accuracy                           0.77       768\n",
            "   macro avg       0.76      0.72      0.73       768\n",
            "weighted avg       0.77      0.77      0.76       768\n",
            "\n",
            "###################### Roc Auc Score(Train) ######################\n",
            "Roc Auc Train:  0.705\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "###################### Roc Auc Score(Test) ######################\n",
            "Roc Auc Test:  0.743\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading a Logistic Regression Model and Then Prediction"
      ],
      "metadata": {
        "id": "uG-TQuoT-lsu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def load_model(pklfile):\n",
        "  model_disc = joblib.load(pklfile)\n",
        "  return model_disc"
      ],
      "metadata": {
        "id": "Xnjg2tMW9a3-"
      },
      "execution_count": 179,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_disc = load_model(\"logistic_model.pkl\")"
      ],
      "metadata": {
        "id": "MHq2VI20-zfX"
      },
      "execution_count": 180,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "patient = [6, 150, 69, 40, 130, 34, 0.69, 45]"
      ],
      "metadata": {
        "id": "Tbl4umeF-5FQ"
      },
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_disc.predict(pd.DataFrame(patient).T)[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JZ8p6bRw_NF7",
        "outputId": "301297dd-41b3-4fd7-d962-a1646fa09b99"
      },
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(\"Outcome\", axis=1)\n",
        "random_patient = X.sample(1)"
      ],
      "metadata": {
        "id": "EA9UGRk6_Yrp"
      },
      "execution_count": 183,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_disc.predict(random_patient)[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TDoC3sIe_59l",
        "outputId": "0217b82c-71b9-4ace-ff1b-7f3fefcf7a3d"
      },
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jXiHD0z1_9Op"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}