LightGBM_Classifier.ipynb

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      "source": [
        "# Light Gradient Boosting Machine (LightGBM)\n",
        "\n",
        "**LightGBM**: LightGBM (Light Gradient Boosting Machine) is a variant of the Gradient Boosting framework and is an open source machine learning library that provides fast and high-performance modeling on large data sets. LightGBM is an algorithm developed by Microsoft and gaining popularity.\n",
        "In the LightGBM algorithm, the \"leaf-wise\" tree growth strategy represents a different approach from the traditional \"level-wise\" tree growth strategy. This strategy used in LightGBM focuses on making the best split at each step while growing the tree, resulting in deeper and more complex tree structures."
      ],
      "metadata": {
        "id": "cca61SX6n7Vs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Necessary Libraries"
      ],
      "metadata": {
        "id": "SQq2B7R2oTfi"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b01893e5-565b-4bf9-8dbb-162b793a158c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (1.4.2)\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 lightgbm import LGBMClassifier\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": 206
        },
        "id": "UspS87IBoysh",
        "outputId": "12c50538-8b66-48d5-c891-b1fdb1efb5a8"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   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": 3
        }
      ]
    },
    {
      "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": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "check_df(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1veSft1SpNki",
        "outputId": "0883929b-e0a2-462e-f674-ee1844525b1f"
      },
      "execution_count": 5,
      "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": 6,
      "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": "42fba447-fdf5-480a-c2b0-feb4da28d0eb"
      },
      "execution_count": 7,
      "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": 8,
      "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": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_summary_df(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "9RLevWaYp0Sn",
        "outputId": "a55ba26b-e778-4f9c-a8dd-97f063a8c7b3"
      },
      "execution_count": 10,
      "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 640x480 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": 11,
      "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": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_summary_df(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jY151P7YqDW6",
        "outputId": "504ed072-8715-4f5f-e2ba-2106169d1707"
      },
      "execution_count": 13,
      "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 640x480 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 640x480 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 640x480 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 640x480 with 1 Axes>"
            ],
            "image/png": 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\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 640x480 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 640x480 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 640x480 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 640x480 with 1 Axes>"
            ],
            "image/png": 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444+rvLzc9br//vtbdgQAAKDD8foKSkZGhjIyMppsi4iIUGFhodu6v/zlLxo2bJgOHDighIQE1/pu3bopJibG290DAIBOwOuA4q2qqirZbDZFRka6rV+8eLGeeOIJJSQkaMqUKZo9e7aCgpqejsPhkMPhcC1XV1dLOvmWktPp9Go+p/o3t5090PJqTBN4WwdvxvTF2B0NtfIO9fIctfIctfKcv2rlzf5slmW1+LexzWZzuwfldMePH9fw4cOVlJSkvLw81/olS5Zo8ODBioqK0ocffqi5c+fqjjvu0JIlS5ocJycnR7m5uY3W5+fnKywsrKXTBwAAbaiurk5TpkxRVVWVwsPDm+3rs4DidDqVmZmpb7/9Vps2bWp2Ii+99JLuuece1dTUyG63N2pv6gpKfHy8Dh8+fNYDbGpehYWFSk9PV3BwcJN9Ls9516sxTbAnZ0yrj+lJrXAStfIO9fIctfIctfKcv2pVXV2tHj16eBRQfPIWj9Pp1C233KKvv/5aRUVFZ51EcnKyTpw4oa+++kr9+/dv1G6325sMLsHBwS0ubHPbOuptLRrTn3x5gp1LnTsbauUd6uU5auU5auW5tq6VN/tq9YByKpzs27dPH3zwgbp3737WbUpKShQQEKDo6OjWng4AAGiHvA4oNTU1Ki0tdS2XlZWppKREUVFRio2N1U033aRPPvlE69atU319vSoqKiRJUVFRCgkJUXFxsbZt26bU1FR169ZNxcXFmj17tm677Tadf/75rXdkAACg3fI6oOzYsUOpqamu5ezsbElSVlaWcnJytHbtWknSlVde6bbdBx98oJEjR8put6ugoEA5OTlyOBxKTEzU7NmzXeMAAAB4HVBGjhyp5u6rPds9t4MHD9ZHH33k7W4BAEAnwnfxAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMbxOqBs2bJFEyZMUFxcnGw2m9asWePWblmW5s+fr9jYWHXp0kVpaWnat2+fW5+jR49q6tSpCg8PV2RkpKZNm6aamppzOhAAANBxeB1QamtrNXDgQC1durTJ9ieffFLPPvusli1bpm3btum8887TmDFjdPz4cVefqVOn6rPPPlNhYaHWrVunLVu26O677275UQAAgA4lyNsNMjIylJGR0WSbZVl65pln9Nhjj+k3v/mNJOmvf/2revXqpTVr1mjy5Mn6/PPPtWHDBm3fvl1Dhw6VJD333HMaN26c/vSnPykuLu4cDgcAAHQEXgeU5pSVlamiokJpaWmudREREUpOTlZxcbEmT56s4uJiRUZGusKJJKWlpSkgIEDbtm3TDTfc0Ghch8Mhh8PhWq6urpYkOZ1OOZ1Or+Z4qn9z29kDLa/GNIG3dfBmTF+M3dFQK+9QL89RK89RK8/5q1be7K9VA0pFRYUkqVevXm7re/Xq5WqrqKhQdHS0+ySCghQVFeXqc7pFixYpNze30fr33ntPYWFhLZprYWHhGdueHNaiIf3q7bff9tnYzdUK7qiVd6iX56iV56iV59q6VnV1dR73bdWA4itz585Vdna2a7m6ulrx8fEaPXq0wsPDvRrL6XSqsLBQ6enpCg4ObrLP5TnvntN8/WFPzphWH9OTWuEkauUd6uU5auU5auU5f9Xq1DsgnmjVgBITEyNJOnjwoGJjY13rDx48qCuvvNLV59ChQ27bnThxQkePHnVtfzq73S673d5ofXBwcIsL29y2jnpbi8b0J1+eYOdS586GWnmHenmOWnmOWnmurWvlzb5a9TkoiYmJiomJ0caNG13rqqurtW3bNqWkpEiSUlJSVFlZqZ07d7r6FBUVqaGhQcnJya05HQAA0E55fQWlpqZGpaWlruWysjKVlJQoKipKCQkJevDBB/WHP/xBF110kRITEzVv3jzFxcVp4sSJkqRLLrlEY8eO1fTp07Vs2TI5nU7NnDlTkydP5hM8AABAUgsCyo4dO5SamupaPnVvSFZWllauXKmHH35YtbW1uvvuu1VZWakRI0Zow4YNCg0NdW2Tl5enmTNnatSoUQoICFBmZqaeffbZVjgcAADQEXgdUEaOHCnLOvPHcG02mx5//HE9/vjjZ+wTFRWl/Px8b3cNAAA6Cb6LBwAAGKddfMwYZ9dnzvpWH9MeaLXLZ8IAANo/rqAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGKfVA0qfPn1ks9kavWbMmCFJGjlyZKO2e++9t7WnAQAA2rGg1h5w+/btqq+vdy3v2bNH6enpuvnmm13rpk+frscff9y1HBYW1trTAAAA7VirB5SePXu6LS9evFj9+vXTdddd51oXFhammJiY1t41AADoIFo9oPzSTz/9pFdeeUXZ2dmy2Wyu9Xl5eXrllVcUExOjCRMmaN68ec1eRXE4HHI4HK7l6upqSZLT6ZTT6fRqTqf6N7edPdDyasyOyh5wsg7e1rgz8uS8ws+ol+eoleeolef8VStv9mezLMtnv41ff/11TZkyRQcOHFBcXJwk6cUXX1Tv3r0VFxen3bt365FHHtGwYcO0atWqM46Tk5Oj3NzcRuvz8/N5ewgAgHairq5OU6ZMUVVVlcLDw5vt69OAMmbMGIWEhOitt946Y5+ioiKNGjVKpaWl6tevX5N9mrqCEh8fr8OHD5/1AE/ndDpVWFio9PR0BQcHN9nn8px3vRqzo7IHWHpiaEOztcJJnpxX+Bn18hy18hy18py/alVdXa0ePXp4FFB89hbP119/rffff7/ZKyOSlJycLEnNBhS73S673d5ofXBwcIsL29y2jnpbk+s7q3Opc2dDrbxDvTxHrTxHrTzX1rXyZl8+ew7KihUrFB0drfHjxzfbr6SkRJIUGxvrq6kAAIB2xidXUBoaGrRixQplZWUpKOjnXezfv1/5+fkaN26cunfvrt27d2v27Nm69tprNWDAAF9MBQAAtEM+CSjvv/++Dhw4oDvvvNNtfUhIiN5//30988wzqq2tVXx8vDIzM/XYY4/5YhoAAKCd8klAGT16tJq69zY+Pl6bN2/2xS4BAEAHwnfxAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGCfI3xOA+S7PeVeOelurj/vV4vGtPiYAoGPgCgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwTqsHlJycHNlsNrdXUlKSq/348eOaMWOGunfvrq5duyozM1MHDx5s7WkAAIB2zCdXUC677DKVl5e7Xlu3bnW1zZ49W2+99ZbeeOMNbd68Wd9//71uvPFGX0wDAAC0U0E+GTQoSDExMY3WV1VVafny5crPz9e//du/SZJWrFihSy65RB999JGuvvpqX0wHnUyfOet9NvZXi8f7bGwAwM98ElD27dunuLg4hYaGKiUlRYsWLVJCQoJ27twpp9OptLQ0V9+kpCQlJCSouLj4jAHF4XDI4XC4lqurqyVJTqdTTqfTq7md6t/cdvZAy6sxOyp7gOX239bm7d+dp3z593emOXtyXuFn1Mtz1Mpz1Mpz/qqVN/uzWZbVqv+av/POO6qpqVH//v1VXl6u3Nxcfffdd9qzZ4/eeust3XHHHW5hQ5KGDRum1NRU/ed//meTY+bk5Cg3N7fR+vz8fIWFhbXm9AEAgI/U1dVpypQpqqqqUnh4eLN9Wz2gnK6yslK9e/fWkiVL1KVLlxYFlKauoMTHx+vw4cNnPcDTOZ1OFRYWKj09XcHBwU32uTznXa/G7KjsAZaeGNqgeTsC5Giwtfr4e3LGtPqYkm///s40Z0/OK/yMenmOWnmOWnnOX7Wqrq5Wjx49PAooPnmL55ciIyN18cUXq7S0VOnp6frpp59UWVmpyMhIV5+DBw82ec/KKXa7XXa7vdH64ODgFhe2uW0d9a3/y7g9czTYfFITX/1Q+PLv72xzPpdzsjOiXp6jVp6jVp5r61p5sy+fPwelpqZG+/fvV2xsrIYMGaLg4GBt3LjR1b53714dOHBAKSkpvp4KAABoJ1r9Csrvfvc7TZgwQb1799b333+vBQsWKDAwULfeeqsiIiI0bdo0ZWdnKyoqSuHh4br//vuVkpLCJ3gAAIBLqweUb7/9VrfeequOHDminj17asSIEfroo4/Us2dPSdJ//dd/KSAgQJmZmXI4HBozZoyef/751p4GAABox1o9oBQUFDTbHhoaqqVLl2rp0qWtvWvA5870jBV7oKUnh528Qbcl98DwfBUAcMd38QAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOK3+XTyAp870vTYAAHAFBQAAGIeAAgAAjENAAQAAxuEeFMAAvrwf56vF4302NgD4CldQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4POoe6OB4jD6A9ogrKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACM0+oBZdGiRbrqqqvUrVs3RUdHa+LEidq7d69bn5EjR8pms7m97r333taeCgAAaKdaPaBs3rxZM2bM0EcffaTCwkI5nU6NHj1atbW1bv2mT5+u8vJy1+vJJ59s7akAAIB2Kqi1B9ywYYPb8sqVKxUdHa2dO3fq2muvda0PCwtTTExMa+8eAAB0AK0eUE5XVVUlSYqKinJbn5eXp1deeUUxMTGaMGGC5s2bp7CwsCbHcDgccjgcruXq6mpJktPplNPp9Go+p/o3t5090PJqzI7KHmC5/Rdn1llr5e3P3+nbtXT7zoRaeY5aec5ftfJmfzbLsnz2L2pDQ4Ouv/56VVZWauvWra71L774onr37q24uDjt3r1bjzzyiIYNG6ZVq1Y1OU5OTo5yc3Mbrc/Pzz9jqAEAAGapq6vTlClTVFVVpfDw8Gb7+jSg3HfffXrnnXe0detWXXDBBWfsV1RUpFGjRqm0tFT9+vVr1N7UFZT4+HgdPnz4rAd4OqfTqcLCQqWnpys4OLjJPpfnvOvVmB2VPcDSE0MbNG9HgBwNNn9Px2idtVZ7csa0aDtPfg5xErXyHLXynL9qVV1drR49engUUHz2Fs/MmTO1bt06bdmypdlwIknJycmSdMaAYrfbZbfbG60PDg5ucWGb29ZR33l+wXjC0WCjJh7qbLU613/YzuVnuLOhVp6jVp5r61p5s69WDyiWZen+++/X6tWrtWnTJiUmJp51m5KSEklSbGxsa08HAAC0Q60eUGbMmKH8/Hy9+eab6tatmyoqKiRJERER6tKli/bv36/8/HyNGzdO3bt31+7duzV79mxde+21GjBgQGtPBwAAtEOtHlBeeOEFSScfxvZLK1as0O23366QkBC9//77euaZZ1RbW6v4+HhlZmbqsccea+2pAACAdsonb/E0Jz4+Xps3b27t3QIAgA6E7+IBAADG8fmD2gDAW33mrPfZ2F8tHu+zsQG0Hq6gAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDp/iAdBiLf20jT3Q0pPDTn4xZ2f67iIAnuMKCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4/ApHgBoBb76/qBTn3gCOhuuoAAAAOMQUAAAgHEIKAAAwDjcgwKgU/HVvSIAWhdXUAAAgHEIKAAAwDgEFAAAYBzuQQGAdsAX3/z81eLxrToe0Jq4ggIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDh8igcAOqn2+FRdPnnUeXAFBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMw6PuAQDtRnOP57cHWnpymHR5zrty1Nu8HpvH6JvFr1dQli5dqj59+ig0NFTJycn6+OOP/TkdAABgCL8FlNdee03Z2dlasGCBPvnkEw0cOFBjxozRoUOH/DUlAABgCL8FlCVLlmj69Om64447dOmll2rZsmUKCwvTSy+95K8pAQAAQ/jlHpSffvpJO3fu1Ny5c13rAgIClJaWpuLiYn9MCQDQyTV3f8u58OW9LS2dsyf36/j7nhy/BJTDhw+rvr5evXr1clvfq1cv/etf/2rU3+FwyOFwuJarqqokSUePHpXT6fRq306nU3V1dTpy5IiCg4Ob7BN0otarMTuqoAZLdXUNCnIGqL7B+xvOOhNq5R3q5Tlq5TlTa3XkyBGfjd3S31ee1MoX8z527JgkybKss/ZtF5/iWbRokXJzcxutT0xM9MNsOpcp/p5AO0KtvEO9PEetPGdirXo87e8ZNO1stfLlvI8dO6aIiIhm+/gloPTo0UOBgYE6ePCg2/qDBw8qJiamUf+5c+cqOzvbtdzQ0KCjR4+qe/fustm8S8nV1dWKj4/XN998o/Dw8JYdQCdBrTxHrbxDvTxHrTxHrTznr1pZlqVjx44pLi7urH39ElBCQkI0ZMgQbdy4URMnTpR0MnRs3LhRM2fObNTfbrfLbre7rYuMjDynOYSHh3MCe4haeY5aeYd6eY5aeY5aec4ftTrblZNT/PYWT3Z2trKysjR06FANGzZMzzzzjGpra3XHHXf4a0oAAMAQfgsokyZN0g8//KD58+eroqJCV155pTZs2NDoxlkAAND5+PUm2ZkzZzb5lo4v2e12LViwoNFbRmiMWnmOWnmHenmOWnmOWnmuPdTKZnnyWR8AAIA2xLcZAwAA4xBQAACAcQgoAADAOAQUAABgnA4ZUBYtWqSrrrpK3bp1U3R0tCZOnKi9e/e69Tl+/LhmzJih7t27q2vXrsrMzGz0ZNvO4oUXXtCAAQNcD+xJSUnRO++842qnVk1bvHixbDabHnzwQdc6avWznJwc2Ww2t1dSUpKrnVq5++6773Tbbbepe/fu6tKli6644grt2LHD1W5ZlubPn6/Y2Fh16dJFaWlp2rdvnx9n7B99+vRpdF7ZbDbNmDFDEufVL9XX12vevHlKTExUly5d1K9fPz3xxBNu34Nj9HlldUBjxoyxVqxYYe3Zs8cqKSmxxo0bZyUkJFg1NTWuPvfee68VHx9vbdy40dqxY4d19dVXW7/+9a/9OGv/Wbt2rbV+/Xrriy++sPbu3Ws9+uijVnBwsLVnzx7LsqhVUz7++GOrT58+1oABA6xZs2a51lOrny1YsMC67LLLrPLyctfrhx9+cLVTq58dPXrU6t27t3X77bdb27Zts7788kvr3XfftUpLS119Fi9ebEVERFhr1qyxdu3aZV1//fVWYmKi9eOPP/px5m3v0KFDbudUYWGhJcn64IMPLMvivPqlhQsXWt27d7fWrVtnlZWVWW+88YbVtWtX689//rOrj8nnVYcMKKc7dOiQJcnavHmzZVmWVVlZaQUHB1tvvPGGq8/nn39uSbKKi4v9NU2jnH/++db//u//UqsmHDt2zLrooouswsJC67rrrnMFFGrlbsGCBdbAgQObbKNW7h555BFrxIgRZ2xvaGiwYmJirKeeesq1rrKy0rLb7darr77aFlM01qxZs6x+/fpZDQ0NnFenGT9+vHXnnXe6rbvxxhutqVOnWpZl/nnVId/iOV1VVZUkKSoqSpK0c+dOOZ1OpaWlufokJSUpISFBxcXFfpmjKerr61VQUKDa2lqlpKRQqybMmDFD48ePd6uJxHnVlH379ikuLk59+/bV1KlTdeDAAUnU6nRr167V0KFDdfPNNys6OlqDBg3S//zP/7jay8rKVFFR4VaviIgIJScnd8p6nfLTTz/plVde0Z133imbzcZ5dZpf//rX2rhxo7744gtJ0q5du7R161ZlZGRIMv+88uuTZNtCQ0ODHnzwQQ0fPlyXX365JKmiokIhISGNvnCwV69eqqio8MMs/e/TTz9VSkqKjh8/rq5du2r16tW69NJLVVJSQq1+oaCgQJ988om2b9/eqI3zyl1ycrJWrlyp/v37q7y8XLm5ubrmmmu0Z88eanWaL7/8Ui+88IKys7P16KOPavv27XrggQcUEhKirKwsV01O/yqQzlqvU9asWaPKykrdfvvtkvgZPN2cOXNUXV2tpKQkBQYGqr6+XgsXLtTUqVMlyfjzqsMHlBkzZmjPnj3aunWrv6ditP79+6ukpERVVVX629/+pqysLG3evNnf0zLKN998o1mzZqmwsFChoaH+no7xTv1fmiQNGDBAycnJ6t27t15//XV16dLFjzMzT0NDg4YOHao//vGPkqRBgwZpz549WrZsmbKysvw8O3MtX75cGRkZiouL8/dUjPT6668rLy9P+fn5uuyyy1RSUqIHH3xQcXFx7eK86tBv8cycOVPr1q3TBx98oAsuuMC1PiYmRj/99JMqKyvd+h88eFAxMTFtPEszhISE6MILL9SQIUO0aNEiDRw4UH/+85+p1S/s3LlThw4d0uDBgxUUFKSgoCBt3rxZzz77rIKCgtSrVy9q1YzIyEhdfPHFKi0t5bw6TWxsrC699FK3dZdcconrLbFTNTn90yidtV6S9PXXX+v999/XXXfd5VrHeeXuP/7jPzRnzhxNnjxZV1xxhX77299q9uzZWrRokSTzz6sOGVAsy9LMmTO1evVqFRUVKTEx0a19yJAhCg4O1saNG13r9u7dqwMHDiglJaWtp2ukhoYGORwOavULo0aN0qeffqqSkhLXa+jQoZo6darrz9TqzGpqarR//37FxsZyXp1m+PDhjR6F8MUXX6h3796SpMTERMXExLjVq7q6Wtu2beuU9ZKkFStWKDo6WuPHj3et47xyV1dXp4AA91/zgYGBamhokNQOzit/36XrC/fdd58VERFhbdq0ye3jaHV1da4+9957r5WQkGAVFRVZO3bssFJSUqyUlBQ/ztp/5syZY23evNkqKyuzdu/ebc2ZM8ey2WzWe++9Z1kWtWrOLz/FY1nU6pceeugha9OmTVZZWZn1j3/8w0pLS7N69OhhHTp0yLIsavVLH3/8sRUUFGQtXLjQ2rdvn5WXl2eFhYVZr7zyiqvP4sWLrcjISOvNN9+0du/ebf3mN78x5uOgba2+vt5KSEiwHnnkkUZtnFc/y8rKsn71q1+5Pma8atUqq0ePHtbDDz/s6mPyedUhA4qkJl8rVqxw9fnxxx+tf//3f7fOP/98KywszLrhhhus8vJy/03aj+68806rd+/eVkhIiNWzZ09r1KhRrnBiWdSqOacHFGr1s0mTJlmxsbFWSEiI9atf/cqaNGmS23M9qJW7t956y7r88sstu91uJSUlWS+++KJbe0NDgzVv3jyrV69elt1ut0aNGmXt3bvXT7P1r3fffdeS1OTxc179rLq62po1a5aVkJBghYaGWn379rV+//vfWw6Hw9XH5PPKZlm/eKQcAACAATrkPSgAAKB9I6AAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAHQZoqLixUYGOj2/SkA0BSeJAugzdx1113q2rWrli9frr179youLs7fUwJgKK6gAGgTNTU1eu2113Tfffdp/PjxWrlypVv72rVrddFFFyk0NFSpqal6+eWXZbPZVFlZ6eqzdetWXXPNNerSpYvi4+P1wAMPqLa2tm0PBECbIKAAaBOvv/66kpKS1L9/f91222166aWXdOoCbllZmW666SZNnDhRu3bt0j333KPf//73btvv379fY8eOVWZmpnbv3q3XXntNW7du1cyZM/1xOAB8jLd4ALSJ4cOH65ZbbtGsWbN04sQJxcbG6o033tDIkSM1Z84crV+/Xp9++qmr/2OPPaaFCxfq//7v/xQZGam77rpLgYGB+u///m9Xn61bt+q6665TbW2tQkND/XFYAHyEKygAfG7v3r36+OOPdeutt0qSgoKCNGnSJC1fvtzVftVVV7ltM2zYMLflXbt2aeXKleratavrNWbMGDU0NKisrKxtDgRAmwny9wQAdHzLly/XiRMn3G6KtSxLdrtdf/nLXzwao6amRvfcc48eeOCBRm0JCQmtNlcAZiCgAPCpEydO6K9//auefvppjR492q1t4sSJevXVV9W/f3+9/fbbbm3bt293Wx48eLD++c9/6sILL/T5nAH4H/egAPCpNWvWaNKkSTp06JAiIiLc2h555BEVFRXp9ddfV//+/TV79mxNmzZNJSUleuihh/Ttt9+qsrJSERER2r17t66++mrdeeeduuuuu3Teeefpn//8pwoLCz2+CgOg/eAeFAA+tXz5cqWlpTUKJ5KUmZmpHTt26NixY/rb3/6mVatWacCAAXrhhRdcn+Kx2+2SpAEDBmjz5s364osvdM0112jQoEGaP38+z1IBOiiuoAAw0sKFC7Vs2TJ98803/p4KAD/gHhQARnj++ed11VVXqXv37vrHP/6hp556imecAJ0YAQWAEfbt26c//OEPOnr0qBISEvTQQw9p7ty5/p4WAD/hLR4AAGAcbpIFAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMb5f9okdJeo20YZAAAAAElFTkSuQmCC\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": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_num_summary(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "id": "kOXGFh9iqwS6",
        "outputId": "4293887a-948a-445e-df91-6e4200e6964e"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 8 Axes>"
            ],
            "image/png": 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          },
          "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": 16,
      "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": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target_summary_with_num_df(df, \"Outcome\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TBQztuEgqN0N",
        "outputId": "3c73e3ba-cabd-48c5-cc81-e59f112e0bd9"
      },
      "execution_count": 18,
      "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": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correlated_cols(df, plot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 870
        },
        "id": "LzcGyZNwrE_T",
        "outputId": "bdaf0262-5d13-4a73-f318-a600f75ec758"
      },
      "execution_count": 20,
      "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": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drop_list = high_correlated_cols(df, plot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 691
        },
        "id": "P1K5H9oIrSqy",
        "outputId": "f2351e29-1e2c-4164-b140-a115ffb01b9f"
      },
      "execution_count": 22,
      "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": "3f04cb1c-d838-4bc1-90c8-a9d964ea9cf1"
      },
      "execution_count": 23,
      "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": 23
        }
      ]
    },
    {
      "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": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oItjnUd-sCfn",
        "outputId": "13c45b9c-d49a-4735-a027-59406b0ebc1c"
      },
      "execution_count": 25,
      "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": 25
        }
      ]
    },
    {
      "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": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "na_columns = missing_value_table(df, na_names=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8azXUXk9sU15",
        "outputId": "0cde8690-d4cf-4139-a37a-c16e82f9ee57"
      },
      "execution_count": 27,
      "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": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = fill_na_with_median(df)"
      ],
      "metadata": {
        "id": "oYpyNeSGsicL"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dzU8EdZ3sl3E",
        "outputId": "5ec7f9f4-a2b7-4828-e437-1545b7143193"
      },
      "execution_count": 30,
      "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": 30
        }
      ]
    },
    {
      "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": 31,
      "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": 32,
      "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": 33,
      "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": "5930514b-b7ec-4612-c730-bca23b597cda"
      },
      "execution_count": 34,
      "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": "3303dcda-3518-4411-9bc8-94e0cbe349ec"
      },
      "execution_count": 35,
      "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": 36,
      "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": 37,
      "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": 38,
      "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": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = one_hot_encoding(df, cat_cols, drop_first=True)"
      ],
      "metadata": {
        "id": "kfxrKLDxt3Nc"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HWQoQD1Zt5B0",
        "outputId": "4c81f11d-a322-45cd-c79a-4c857e2e2775"
      },
      "execution_count": 41,
      "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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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "      } 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-df9cf251-9668-4fe3-bb53-7891aa9a44af 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": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create a Base Model LightGBM Classifier\n",
        "\n",
        "**LightGBM**: LightGBM (Light Gradient Boosting Machine) is a variant of the Gradient Boosting framework and is an open source machine learning library that provides fast and high-performance modeling on large data sets. LightGBM is an algorithm developed by Microsoft and gaining popularity.\n",
        "In the LightGBM algorithm, the \"leaf-wise\" tree growth strategy represents a different approach from the traditional \"level-wise\" tree growth strategy. This strategy used in LightGBM focuses on making the best split at each step while growing the tree, resulting in deeper and more complex tree structures."
      ],
      "metadata": {
        "id": "ILhVlgSHuCnX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def LGBM_Model(dataframe, target, test_size=0.30, cv=10, results=False, conf_matrix=False, c_report=False, roc=False, plot_importance=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",
        "\n",
        "  lgbm_model = LGBMClassifier(verbose=-1).fit(X_train, y_train)\n",
        "\n",
        "  if results:\n",
        "    acc_train = accuracy_score(y_train, lgbm_model.predict(X_train))\n",
        "    acc_test = accuracy_score(y_test, lgbm_model.predict(X_test))\n",
        "    r2 = lgbm_model.score(X, y)\n",
        "    r2_train = lgbm_model.score(X_train, y_train)\n",
        "    r2_test = lgbm_model.score(X_test, y_test)\n",
        "    cv_train = cross_val_score(lgbm_model, X_train, y_train, cv=cv).mean()\n",
        "    cv_test = cross_val_score(lgbm_model, X_test, y_test, cv=cv).mean()\n",
        "    cv_results = cross_validate(lgbm_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, lgbm_model.predict(X_train)))\n",
        "    print(\"##################### Confusion Matrix(Test) ######################\")\n",
        "    print(confusion_matrix(y_test, lgbm_model.predict(X_test)))\n",
        "    print(\"##################### Confusion Matrix(All) ######################\")\n",
        "    print(confusion_matrix(y, lgbm_model.predict(X)))\n",
        "    plt.figure(figsize=(5,5))\n",
        "    acc = accuracy_score(y, lgbm_model.predict(X))\n",
        "    cm = confusion_matrix(y, lgbm_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, lgbm_model.predict(X_train)))\n",
        "    print(\"###################### Classification Report(Test) ######################\")\n",
        "    print(classification_report(y_test, lgbm_model.predict(X_test)))\n",
        "    print(\"###################### Classification Report(All) ######################\")\n",
        "    print(classification_report(y, lgbm_model.predict(X)))\n",
        "\n",
        "  if roc:\n",
        "    print(\"###################### Roc Auc Score(Train) ######################\")\n",
        "    lgbm_roc_auc_train = roc_auc_score(y_train, lgbm_model.predict(X_train))\n",
        "    print(\"Roc Auc Train: \", \"%.3f\" % lgbm_roc_auc_train)\n",
        "    fpr, tpr, thresholds = roc_curve(y_train, lgbm_model.predict_proba(X_train)[:,1])\n",
        "    plt.figure(figsize=(5,5))\n",
        "    plt.plot(fpr, tpr, label='AUC (area = %.3f)' % lgbm_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",
        "    lgbm_roc_auc_test = roc_auc_score(y_test, lgbm_model.predict(X_test))\n",
        "    print(\"Roc Auc Test: \", \"%.3f\" % lgbm_roc_auc_test)\n",
        "    fpr, tpr, thresholds = roc_curve(y_test, lgbm_model.predict_proba(X_test)[:,1])\n",
        "    plt.figure(figsize=(5,5))\n",
        "    plt.plot(fpr, tpr, label='AUC (area = %.3f)' % lgbm_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 plot_importance:\n",
        "    feature_imp = pd.DataFrame({'Value': lgbm_model.feature_importances_, 'Feature': X.columns})\n",
        "    plt.figure(figsize=(8, 8))\n",
        "    sns.set(font_scale=1)\n",
        "    sns.barplot(x=\"Value\", y=\"Feature\", data=feature_imp.sort_values(by=\"Value\",\n",
        "                                                                     ascending=False)[0:len(X)])\n",
        "    plt.title('Features')\n",
        "    plt.tight_layout()\n",
        "    plt.savefig('importances.png')\n",
        "    plt.show()\n",
        "\n",
        "  if save_model:\n",
        "    joblib.dump(lgbm_model, \"lgbm_model.pkl\")\n",
        "\n",
        "  return lgbm_model"
      ],
      "metadata": {
        "id": "5nL-tJlQB5IZ"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lgbm_model = LGBM_Model(df, \"Outcome\", results=True, conf_matrix=True, c_report=True, roc=True, plot_importance=True, save_model=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "F82kZ-YOBr5d",
        "outputId": "8fc1276c-4a51-47c2-f621-1bc7d9de015d"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "##################### Accuracy & R2 Metrics ######################\n",
            "Accuracy_train:  1.000\n",
            "Accuracy_test:  0.779\n",
            "R2:  0.934\n",
            "R2_train:  1.000\n",
            "R2_test:  0.779\n",
            "Cross Validation Score(Train):  0.736\n",
            "Cross Validation Score(Test):  0.744\n",
            "Cross Validate (Accuracy):  0.741\n",
            "Cross Validate (Precision):  0.636\n",
            "Cross Validate (Recall):  0.608\n",
            "Cross Validate (F1):  0.615\n",
            "Cross Validate (ROC AUC Score):  0.809\n",
            "##################### Confusion Matrix(Train) ######################\n",
            "[[354   0]\n",
            " [  0 183]]\n",
            "##################### Confusion Matrix(Test) ######################\n",
            "[[127  19]\n",
            " [ 32  53]]\n",
            "##################### Confusion Matrix(All) ######################\n",
            "[[481  19]\n",
            " [ 32 236]]\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       1.00      1.00      1.00       354\n",
            "           1       1.00      1.00      1.00       183\n",
            "\n",
            "    accuracy                           1.00       537\n",
            "   macro avg       1.00      1.00      1.00       537\n",
            "weighted avg       1.00      1.00      1.00       537\n",
            "\n",
            "###################### Classification Report(Test) ######################\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.87      0.83       146\n",
            "           1       0.74      0.62      0.68        85\n",
            "\n",
            "    accuracy                           0.78       231\n",
            "   macro avg       0.77      0.75      0.75       231\n",
            "weighted avg       0.78      0.78      0.77       231\n",
            "\n",
            "###################### Classification Report(All) ######################\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.96      0.95       500\n",
            "           1       0.93      0.88      0.90       268\n",
            "\n",
            "    accuracy                           0.93       768\n",
            "   macro avg       0.93      0.92      0.93       768\n",
            "weighted avg       0.93      0.93      0.93       768\n",
            "\n",
            "###################### Roc Auc Score(Train) ######################\n",
            "Roc Auc Train:  1.000\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:  0.747\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading a Base LGBM Classifier Model and Then Prediction"
      ],
      "metadata": {
        "id": "ofkeIABShfKg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def load_model(pklfile):\n",
        "  model_disc = joblib.load(pklfile)\n",
        "  return model_disc"
      ],
      "metadata": {
        "id": "0uTSbUYwgH1K"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_disc = load_model(\"lgbm_model.pkl\")"
      ],
      "metadata": {
        "id": "qeNyABqOhnhM"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "patient = [5, 140, 90, 25, 150, 39, 0.55, 65]"
      ],
      "metadata": {
        "id": "XeDbv8XkhuDB"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_disc.predict(pd.DataFrame(patient).T)[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DFSem9YGhzR_",
        "outputId": "efbe2084-4058-4002-a978-dda87732f372"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(\"Outcome\", axis=1)\n",
        "random_patient = X.sample(1)"
      ],
      "metadata": {
        "id": "HSCtRr6Hh54A"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_patient"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "L6VbJ3HBiEgT",
        "outputId": "940d1535-5c74-45e3-979f-1c0006120575"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin     BMI  DiabetesPedigreeFunction  Age\n",
              "410            6 102.0000        90.0000        39.0000 125.0000 35.7000                    0.6740   28"
            ],
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              "      <th>Pregnancies</th>\n",
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              "      <th>410</th>\n",
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              "      <td>39.0000</td>\n",
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              "      <td>35.7000</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "random_patient",
              "summary": "{\n  \"name\": \"random_patient\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 6,\n        \"max\": 6,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 102.0,\n        \"max\": 102.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          102.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 90.0,\n        \"max\": 90.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          90.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 39.0,\n        \"max\": 39.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          39.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 125.0,\n        \"max\": 125.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          125.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 35.7,\n        \"max\": 35.7,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          35.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.674,\n        \"max\": 0.674,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.674\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 28,\n        \"max\": 28,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_disc.predict(random_patient)[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yiG_12n_iFs9",
        "outputId": "a5127a69-94c9-42da-bc54-e43db8451be6"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create a Model Tuning: LGBM Classifier"
      ],
      "metadata": {
        "id": "qJa2N9oeDSPE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def LGBM_Model_Tuned(dataframe, target, test_size=0.30, cv=10, results=False, conf_matrix=False, c_report=False, roc=False, plot_importance=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",
        "\n",
        "  lgbm_model = LGBMClassifier(verbose=-1)\n",
        "  lgbm_params = {\n",
        "    \"n_estimators\": [500, 1000],\n",
        "    \"subsample\": [0.6, 0.8],\n",
        "    \"max_depth\": [5, 8],\n",
        "    \"learning_rate\": [0.1, 0.01],\n",
        "    \"min_child_samles\": [5, 10]\n",
        "  }\n",
        "  lgbm_cv_model = GridSearchCV(lgbm_model, lgbm_params, cv=cv, verbose=True, n_jobs=-1).fit(X_train, y_train)\n",
        "  lgbm_model_tuned = lgbm_model.set_params(**lgbm_cv_model.best_params_).fit(X, y)\n",
        "\n",
        "  if results:\n",
        "    acc_train = accuracy_score(y_train, lgbm_model_tuned.predict(X_train))\n",
        "    acc_test = accuracy_score(y_test, lgbm_model_tuned.predict(X_test))\n",
        "    r2 = lgbm_model_tuned.score(X, y)\n",
        "    r2_train = lgbm_model_tuned.score(X_train, y_train)\n",
        "    r2_test = lgbm_model_tuned.score(X_test, y_test)\n",
        "    cv_train = cross_val_score(lgbm_model_tuned, X_train, y_train, cv=cv).mean()\n",
        "    cv_test = cross_val_score(lgbm_model_tuned, X_test, y_test, cv=cv).mean()\n",
        "    cv_results = cross_validate(lgbm_model_tuned, X, y, cv=cv, scoring=[\"accuracy\", \"precision\", \"recall\", \"f1\", \"roc_auc\"])\n",
        "    print(\"##################### Accuracy & R2 Metrics ######################\")\n",
        "    print(\"LGBM Classifier Best Params: \", lgbm_cv_model.best_params_)\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, lgbm_model_tuned.predict(X_train)))\n",
        "    print(\"##################### Confusion Matrix(Test) ######################\")\n",
        "    print(confusion_matrix(y_test, lgbm_model_tuned.predict(X_test)))\n",
        "    print(\"##################### Confusion Matrix(All) ######################\")\n",
        "    print(confusion_matrix(y, lgbm_model_tuned.predict(X)))\n",
        "    plt.figure(figsize=(5,5))\n",
        "    acc = accuracy_score(y, lgbm_model_tuned.predict(X))\n",
        "    cm = confusion_matrix(y, lgbm_model_tuned.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, lgbm_model_tuned.predict(X_train)))\n",
        "    print(\"###################### Classification Report(Test) ######################\")\n",
        "    print(classification_report(y_test, lgbm_model_tuned.predict(X_test)))\n",
        "    print(\"###################### Classification Report(All) ######################\")\n",
        "    print(classification_report(y, lgbm_model_tuned.predict(X)))\n",
        "\n",
        "  if roc:\n",
        "    print(\"###################### Roc Auc Score(Train) ######################\")\n",
        "    lgbm_roc_auc_train = roc_auc_score(y_train, lgbm_model_tuned.predict(X_train))\n",
        "    print(\"Roc Auc Train: \", \"%.3f\" % lgbm_roc_auc_train)\n",
        "    fpr, tpr, thresholds = roc_curve(y_train, lgbm_model_tuned.predict_proba(X_train)[:,1])\n",
        "    plt.figure(figsize=(5,5))\n",
        "    plt.plot(fpr, tpr, label='AUC (area = %.3f)' % lgbm_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",
        "    lgbm_roc_auc_test = roc_auc_score(y_test, lgbm_model_tuned.predict(X_test))\n",
        "    print(\"Roc Auc Test: \", \"%.3f\" % lgbm_roc_auc_test)\n",
        "    fpr, tpr, thresholds = roc_curve(y_test, lgbm_model_tuned.predict_proba(X_test)[:,1])\n",
        "    plt.figure(figsize=(5,5))\n",
        "    plt.plot(fpr, tpr, label='AUC (area = %.3f)' % lgbm_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 plot_importance:\n",
        "    feature_imp = pd.DataFrame({'Value': lgbm_model_tuned.feature_importances_, 'Feature': X.columns})\n",
        "    plt.figure(figsize=(8, 8))\n",
        "    sns.set(font_scale=1)\n",
        "    sns.barplot(x=\"Value\", y=\"Feature\", data=feature_imp.sort_values(by=\"Value\",\n",
        "                                                                     ascending=False)[0:len(X)])\n",
        "    plt.title('Features')\n",
        "    plt.tight_layout()\n",
        "    plt.savefig('importances.png')\n",
        "    plt.show()\n",
        "\n",
        "  if save_model:\n",
        "    joblib.dump(lgbm_model_tuned, \"lgbm_model_tuned.pkl\")\n",
        "\n",
        "  return lgbm_model_tuned"
      ],
      "metadata": {
        "id": "P3xNmWSZEjF5"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lgbm_model_tuned = LGBM_Model_Tuned(df, \"Outcome\", results=True, conf_matrix=True, c_report=True, roc=True, plot_importance=True, save_model=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fpeHASk9EcQY",
        "outputId": "d0bfe42f-9597-44ac-f429-6cb39a97010a"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n",
            "##################### Accuracy & R2 Metrics ######################\n",
            "LGBM Classifier Best Params:  {'learning_rate': 0.01, 'max_depth': 8, 'min_child_samles': 5, 'n_estimators': 500, 'subsample': 0.6}\n",
            "Accuracy_train:  0.937\n",
            "Accuracy_test:  0.948\n",
            "R2:  0.940\n",
            "R2_train:  0.937\n",
            "R2_test:  0.948\n",
            "Cross Validation Score(Train):  0.758\n",
            "Cross Validation Score(Test):  0.753\n",
            "Cross Validate (Accuracy):  0.760\n",
            "Cross Validate (Precision):  0.675\n",
            "Cross Validate (Recall):  0.619\n",
            "Cross Validate (F1):  0.638\n",
            "Cross Validate (ROC AUC Score):  0.815\n",
            "##################### Confusion Matrix(Train) ######################\n",
            "[[340  14]\n",
            " [ 20 163]]\n",
            "##################### Confusion Matrix(Test) ######################\n",
            "[[142   4]\n",
            " [  8  77]]\n",
            "##################### Confusion Matrix(All) ######################\n",
            "[[482  18]\n",
            " [ 28 240]]\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       0.94      0.96      0.95       354\n",
            "           1       0.92      0.89      0.91       183\n",
            "\n",
            "    accuracy                           0.94       537\n",
            "   macro avg       0.93      0.93      0.93       537\n",
            "weighted avg       0.94      0.94      0.94       537\n",
            "\n",
            "###################### Classification Report(Test) ######################\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.97      0.96       146\n",
            "           1       0.95      0.91      0.93        85\n",
            "\n",
            "    accuracy                           0.95       231\n",
            "   macro avg       0.95      0.94      0.94       231\n",
            "weighted avg       0.95      0.95      0.95       231\n",
            "\n",
            "###################### Classification Report(All) ######################\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.96      0.95       500\n",
            "           1       0.93      0.90      0.91       268\n",
            "\n",
            "    accuracy                           0.94       768\n",
            "   macro avg       0.94      0.93      0.93       768\n",
            "weighted avg       0.94      0.94      0.94       768\n",
            "\n",
            "###################### Roc Auc Score(Train) ######################\n",
            "Roc Auc Train:  0.926\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:  0.939\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading a LGBM Classifier Model Tuned and Then Prediction"
      ],
      "metadata": {
        "id": "FC368y2bFjQA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def load_model(pklfile):\n",
        "  model_disc = joblib.load(pklfile)\n",
        "  return model_disc"
      ],
      "metadata": {
        "id": "ZuTQ0vOUFjQC"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_disc = load_model(\"lgbm_model_tuned.pkl\")"
      ],
      "metadata": {
        "id": "YDVrgHxkFjQD"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "patient = [5, 140, 90, 25, 150, 39, 0.55, 65]"
      ],
      "metadata": {
        "id": "BRnzK_s1FjQE"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_disc.predict(pd.DataFrame(patient).T)[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49c3094c-abe9-43fe-ee07-fa9cae038914",
        "id": "x_a00e_4FjQG"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(\"Outcome\", axis=1)\n",
        "random_patient = X.sample(1)"
      ],
      "metadata": {
        "id": "fp21S8rAFjQH"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_patient"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "outputId": "02cbbb22-3eb5-47df-c0de-04649b7af65a",
        "id": "lRmbYQziFjQH"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin     BMI  DiabetesPedigreeFunction  Age\n",
              "585            1  93.0000        56.0000        11.0000 125.0000 22.5000                    0.4170   22"
            ],
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              "type": "dataframe",
              "variable_name": "random_patient",
              "summary": "{\n  \"name\": \"random_patient\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 93.0,\n        \"max\": 93.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          93.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 56.0,\n        \"max\": 56.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          56.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 11.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          11.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 125.0,\n        \"max\": 125.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          125.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 22.5,\n        \"max\": 22.5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          22.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.417,\n        \"max\": 0.417,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.417\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 22,\n        \"max\": 22,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_disc.predict(random_patient)[0]"
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        "outputId": "342c920a-bc8f-4d82-ab98-a4c9688008b3",
        "id": "wuHXknPDFjQI"
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      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
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          "metadata": {},
          "execution_count": 83
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      "execution_count": null,
      "outputs": []
    }
  ]
}