Project_9_Cardiac_Disease_Classifier.ipynb

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  "metadata": {
    "colab": {
      "name": "pending Cardiac_Disease_Classifier.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPDDTCqR50rg/TQdWUzlT13",
      "include_colab_link": true
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ind-kum/M.L.-EVELUTION-PROJECTS/blob/master/Project_9_Cardiac_Disease_Classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "DWKLUkbKhevy",
        "outputId": "caf60dd9-0f44-4201-a8ff-db1afa6f4755"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "df=pd.read_csv(\"https://raw.githubusercontent.com/ind-kum/Datasets/master/cardio_train.csv\", sep=';')\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>ap_hi</th>\n",
              "      <th>ap_lo</th>\n",
              "      <th>cholesterol</th>\n",
              "      <th>gluc</th>\n",
              "      <th>smoke</th>\n",
              "      <th>alco</th>\n",
              "      <th>active</th>\n",
              "      <th>cardio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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            ],
            "text/plain": [
              "          id    age  gender  height  weight  ...  gluc  smoke  alco  active  cardio\n",
              "0        988  22469       1     155    69.0  ...     2      0     0       1       0\n",
              "1        989  14648       1     163    71.0  ...     1      0     0       1       1\n",
              "2        990  21901       1     165    70.0  ...     1      0     0       1       0\n",
              "3        991  14549       2     165    85.0  ...     1      1     1       1       0\n",
              "4        992  23393       1     155    62.0  ...     1      0     0       1       0\n",
              "...      ...    ...     ...     ...     ...  ...   ...    ...   ...     ...     ...\n",
              "69296  99993  19240       2     168    76.0  ...     1      1     0       1       0\n",
              "69297  99995  22601       1     158   126.0  ...     2      0     0       1       1\n",
              "69298  99996  19066       2     183   105.0  ...     1      0     1       0       1\n",
              "69299  99998  22431       1     163    72.0  ...     2      0     0       0       1\n",
              "69300  99999  20540       1     170    72.0  ...     1      0     0       1       0\n",
              "\n",
              "[69301 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1eIh0bwYh0Rq",
        "outputId": "117967bf-0622-431e-b3e8-79a294575f8d"
      },
      "source": [
        "#Lets check the null values \n",
        "print(df.isnull().sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "id             0\n",
            "age            0\n",
            "gender         0\n",
            "height         0\n",
            "weight         0\n",
            "ap_hi          0\n",
            "ap_lo          0\n",
            "cholesterol    0\n",
            "gluc           0\n",
            "smoke          0\n",
            "alco           0\n",
            "active         0\n",
            "cardio         0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zKHqVuS3htkP",
        "outputId": "5228faa1-1797-4d74-9c4d-7fadcb336fee"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id               int64\n",
              "age              int64\n",
              "gender           int64\n",
              "height           int64\n",
              "weight         float64\n",
              "ap_hi            int64\n",
              "ap_lo            int64\n",
              "cholesterol      int64\n",
              "gluc             int64\n",
              "smoke            int64\n",
              "alco             int64\n",
              "active           int64\n",
              "cardio           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 705
        },
        "id": "IzpOs0QBiR55",
        "outputId": "70ddf4f7-ff1d-4026-cda8-2f40d17c6a3d"
      },
      "source": [
        "corr_hmap=df.corr()\n",
        "plt.figure(figsize=(13,12))\n",
        "sns.heatmap(corr_hmap,annot=True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "P8emXuWwh8nc",
        "outputId": "61a83241-555f-4863-9917-f5039747ccde"
      },
      "source": [
        "df.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>ap_hi</th>\n",
              "      <th>ap_lo</th>\n",
              "      <th>cholesterol</th>\n",
              "      <th>gluc</th>\n",
              "      <th>smoke</th>\n",
              "      <th>alco</th>\n",
              "      <th>active</th>\n",
              "      <th>cardio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.003587</td>\n",
              "      <td>0.003773</td>\n",
              "      <td>-0.003735</td>\n",
              "      <td>-0.001549</td>\n",
              "      <td>0.003268</td>\n",
              "      <td>-0.002757</td>\n",
              "      <td>0.006400</td>\n",
              "      <td>0.002543</td>\n",
              "      <td>-0.003294</td>\n",
              "      <td>0.000384</td>\n",
              "      <td>0.002700</td>\n",
              "      <td>0.004269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0.003587</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.023150</td>\n",
              "      <td>-0.081304</td>\n",
              "      <td>0.054409</td>\n",
              "      <td>0.020663</td>\n",
              "      <td>0.017717</td>\n",
              "      <td>0.154343</td>\n",
              "      <td>0.099095</td>\n",
              "      <td>-0.047680</td>\n",
              "      <td>-0.029435</td>\n",
              "      <td>-0.009926</td>\n",
              "      <td>0.238095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gender</th>\n",
              "      <td>0.003773</td>\n",
              "      <td>-0.023150</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.499373</td>\n",
              "      <td>0.155649</td>\n",
              "      <td>0.005980</td>\n",
              "      <td>0.015396</td>\n",
              "      <td>-0.036335</td>\n",
              "      <td>-0.021154</td>\n",
              "      <td>0.337608</td>\n",
              "      <td>0.171015</td>\n",
              "      <td>0.006241</td>\n",
              "      <td>0.008592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>height</th>\n",
              "      <td>-0.003735</td>\n",
              "      <td>-0.081304</td>\n",
              "      <td>0.499373</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.290333</td>\n",
              "      <td>0.005492</td>\n",
              "      <td>0.006215</td>\n",
              "      <td>-0.050632</td>\n",
              "      <td>-0.018929</td>\n",
              "      <td>0.187899</td>\n",
              "      <td>0.094164</td>\n",
              "      <td>-0.005974</td>\n",
              "      <td>-0.011015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weight</th>\n",
              "      <td>-0.001549</td>\n",
              "      <td>0.054409</td>\n",
              "      <td>0.155649</td>\n",
              "      <td>0.290333</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.030665</td>\n",
              "      <td>0.043652</td>\n",
              "      <td>0.141901</td>\n",
              "      <td>0.107255</td>\n",
              "      <td>0.068304</td>\n",
              "      <td>0.067259</td>\n",
              "      <td>-0.016514</td>\n",
              "      <td>0.182094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ap_hi</th>\n",
              "      <td>0.003268</td>\n",
              "      <td>0.020663</td>\n",
              "      <td>0.005980</td>\n",
              "      <td>0.005492</td>\n",
              "      <td>0.030665</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.015919</td>\n",
              "      <td>0.023573</td>\n",
              "      <td>0.011821</td>\n",
              "      <td>-0.000933</td>\n",
              "      <td>0.001385</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.054211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ap_lo</th>\n",
              "      <td>-0.002757</td>\n",
              "      <td>0.017717</td>\n",
              "      <td>0.015396</td>\n",
              "      <td>0.006215</td>\n",
              "      <td>0.043652</td>\n",
              "      <td>0.015919</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.024023</td>\n",
              "      <td>0.010895</td>\n",
              "      <td>0.004948</td>\n",
              "      <td>0.010764</td>\n",
              "      <td>0.004962</td>\n",
              "      <td>0.065290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cholesterol</th>\n",
              "      <td>0.006400</td>\n",
              "      <td>0.154343</td>\n",
              "      <td>-0.036335</td>\n",
              "      <td>-0.050632</td>\n",
              "      <td>0.141901</td>\n",
              "      <td>0.023573</td>\n",
              "      <td>0.024023</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.451741</td>\n",
              "      <td>0.010237</td>\n",
              "      <td>0.036015</td>\n",
              "      <td>0.009708</td>\n",
              "      <td>0.220752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gluc</th>\n",
              "      <td>0.002543</td>\n",
              "      <td>0.099095</td>\n",
              "      <td>-0.021154</td>\n",
              "      <td>-0.018929</td>\n",
              "      <td>0.107255</td>\n",
              "      <td>0.011821</td>\n",
              "      <td>0.010895</td>\n",
              "      <td>0.451741</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.004608</td>\n",
              "      <td>0.011442</td>\n",
              "      <td>-0.007301</td>\n",
              "      <td>0.089062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoke</th>\n",
              "      <td>-0.003294</td>\n",
              "      <td>-0.047680</td>\n",
              "      <td>0.337608</td>\n",
              "      <td>0.187899</td>\n",
              "      <td>0.068304</td>\n",
              "      <td>-0.000933</td>\n",
              "      <td>0.004948</td>\n",
              "      <td>0.010237</td>\n",
              "      <td>-0.004608</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.339709</td>\n",
              "      <td>0.025794</td>\n",
              "      <td>-0.015225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alco</th>\n",
              "      <td>0.000384</td>\n",
              "      <td>-0.029435</td>\n",
              "      <td>0.171015</td>\n",
              "      <td>0.094164</td>\n",
              "      <td>0.067259</td>\n",
              "      <td>0.001385</td>\n",
              "      <td>0.010764</td>\n",
              "      <td>0.036015</td>\n",
              "      <td>0.011442</td>\n",
              "      <td>0.339709</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.025745</td>\n",
              "      <td>-0.007217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>active</th>\n",
              "      <td>0.002700</td>\n",
              "      <td>-0.009926</td>\n",
              "      <td>0.006241</td>\n",
              "      <td>-0.005974</td>\n",
              "      <td>-0.016514</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.004962</td>\n",
              "      <td>0.009708</td>\n",
              "      <td>-0.007301</td>\n",
              "      <td>0.025794</td>\n",
              "      <td>0.025745</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.035592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cardio</th>\n",
              "      <td>0.004269</td>\n",
              "      <td>0.238095</td>\n",
              "      <td>0.008592</td>\n",
              "      <td>-0.011015</td>\n",
              "      <td>0.182094</td>\n",
              "      <td>0.054211</td>\n",
              "      <td>0.065290</td>\n",
              "      <td>0.220752</td>\n",
              "      <td>0.089062</td>\n",
              "      <td>-0.015225</td>\n",
              "      <td>-0.007217</td>\n",
              "      <td>-0.035592</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   id       age    gender  ...      alco    active    cardio\n",
              "id           1.000000  0.003587  0.003773  ...  0.000384  0.002700  0.004269\n",
              "age          0.003587  1.000000 -0.023150  ... -0.029435 -0.009926  0.238095\n",
              "gender       0.003773 -0.023150  1.000000  ...  0.171015  0.006241  0.008592\n",
              "height      -0.003735 -0.081304  0.499373  ...  0.094164 -0.005974 -0.011015\n",
              "weight      -0.001549  0.054409  0.155649  ...  0.067259 -0.016514  0.182094\n",
              "ap_hi        0.003268  0.020663  0.005980  ...  0.001385  0.000032  0.054211\n",
              "ap_lo       -0.002757  0.017717  0.015396  ...  0.010764  0.004962  0.065290\n",
              "cholesterol  0.006400  0.154343 -0.036335  ...  0.036015  0.009708  0.220752\n",
              "gluc         0.002543  0.099095 -0.021154  ...  0.011442 -0.007301  0.089062\n",
              "smoke       -0.003294 -0.047680  0.337608  ...  0.339709  0.025794 -0.015225\n",
              "alco         0.000384 -0.029435  0.171015  ...  1.000000  0.025745 -0.007217\n",
              "active       0.002700 -0.009926  0.006241  ...  0.025745  1.000000 -0.035592\n",
              "cardio       0.004269  0.238095  0.008592  ... -0.007217 -0.035592  1.000000\n",
              "\n",
              "[13 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "dggRTxNLh-s0",
        "outputId": "db08da1d-2a23-416d-e4a8-abb575f9d5fa"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>ap_hi</th>\n",
              "      <th>ap_lo</th>\n",
              "      <th>cholesterol</th>\n",
              "      <th>gluc</th>\n",
              "      <th>smoke</th>\n",
              "      <th>alco</th>\n",
              "      <th>active</th>\n",
              "      <th>cardio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>69301.000000</td>\n",
              "      <td>69301.000000</td>\n",
              "      <td>69301.000000</td>\n",
              "      <td>69301.000000</td>\n",
              "      <td>69301.000000</td>\n",
              "      <td>69301.000000</td>\n",
              "      <td>69301.000000</td>\n",
              "      <td>69301.000000</td>\n",
              "      <td>69301.000000</td>\n",
              "      <td>69301.000000</td>\n",
              "      <td>69301.000000</td>\n",
              "      <td>69301.000000</td>\n",
              "      <td>69301.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>50471.480397</td>\n",
              "      <td>19468.786280</td>\n",
              "      <td>1.349519</td>\n",
              "      <td>164.362217</td>\n",
              "      <td>74.203027</td>\n",
              "      <td>128.829584</td>\n",
              "      <td>96.650092</td>\n",
              "      <td>1.366806</td>\n",
              "      <td>1.226447</td>\n",
              "      <td>0.088051</td>\n",
              "      <td>0.053881</td>\n",
              "      <td>0.803986</td>\n",
              "      <td>0.499589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>28563.100347</td>\n",
              "      <td>2467.261818</td>\n",
              "      <td>0.476821</td>\n",
              "      <td>8.205337</td>\n",
              "      <td>14.383469</td>\n",
              "      <td>154.775805</td>\n",
              "      <td>189.096240</td>\n",
              "      <td>0.680270</td>\n",
              "      <td>0.572246</td>\n",
              "      <td>0.283371</td>\n",
              "      <td>0.225784</td>\n",
              "      <td>0.396982</td>\n",
              "      <td>0.500003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>988.000000</td>\n",
              "      <td>10798.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>-150.000000</td>\n",
              "      <td>-70.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>25745.000000</td>\n",
              "      <td>17664.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>50494.000000</td>\n",
              "      <td>19704.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>75150.000000</td>\n",
              "      <td>21326.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>99999.000000</td>\n",
              "      <td>23713.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>16020.000000</td>\n",
              "      <td>11000.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id           age  ...        active        cardio\n",
              "count  69301.000000  69301.000000  ...  69301.000000  69301.000000\n",
              "mean   50471.480397  19468.786280  ...      0.803986      0.499589\n",
              "std    28563.100347   2467.261818  ...      0.396982      0.500003\n",
              "min      988.000000  10798.000000  ...      0.000000      0.000000\n",
              "25%    25745.000000  17664.000000  ...      1.000000      0.000000\n",
              "50%    50494.000000  19704.000000  ...      1.000000      0.000000\n",
              "75%    75150.000000  21326.000000  ...      1.000000      1.000000\n",
              "max    99999.000000  23713.000000  ...      1.000000      1.000000\n",
              "\n",
              "[8 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "IW4rosF8iA9A",
        "outputId": "d1ed1dc9-b868-48b4-9f14-efd00d4cc9a1"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.barplot(x=\"cardio\",y=\"weight\",data=df)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "SpbLtek0iD3Y",
        "outputId": "f13ae69e-18d8-4df4-c535-7636d1c3f744"
      },
      "source": [
        "sns.countplot(x=\"cardio\",data=df)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kc1iawxPiGc9"
      },
      "source": [
        "import numpy as np\n",
        "df_x=df.drop(columns=[\"cardio\",\"id\",])\n",
        "y=df[[\"cardio\"]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "r_1EVDpFiIul",
        "outputId": "f9c129d7-376e-41e4-cf34-0a5cad9e5dfe"
      },
      "source": [
        "#Lets bring input dataset features to same scale\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "x = sc.fit_transform(df_x)\n",
        "x=pd.DataFrame(x,columns=df_x.columns)\n",
        "x"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>ap_hi</th>\n",
              "      <th>ap_lo</th>\n",
              "      <th>cholesterol</th>\n",
              "      <th>gluc</th>\n",
              "      <th>smoke</th>\n",
              "      <th>alco</th>\n",
              "      <th>active</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.216018</td>\n",
              "      <td>-0.733023</td>\n",
              "      <td>-1.140999</td>\n",
              "      <td>-0.361739</td>\n",
              "      <td>0.007562</td>\n",
              "      <td>-0.088052</td>\n",
              "      <td>0.930805</td>\n",
              "      <td>1.351794</td>\n",
              "      <td>-0.310728</td>\n",
              "      <td>-0.238641</td>\n",
              "      <td>0.493765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.953915</td>\n",
              "      <td>-0.733023</td>\n",
              "      <td>-0.166017</td>\n",
              "      <td>-0.222690</td>\n",
              "      <td>-0.121658</td>\n",
              "      <td>-0.140935</td>\n",
              "      <td>-0.539210</td>\n",
              "      <td>-0.395719</td>\n",
              "      <td>-0.310728</td>\n",
              "      <td>-0.238641</td>\n",
              "      <td>0.493765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.985802</td>\n",
              "      <td>-0.733023</td>\n",
              "      <td>0.077728</td>\n",
              "      <td>-0.292214</td>\n",
              "      <td>-0.057048</td>\n",
              "      <td>-0.088052</td>\n",
              "      <td>-0.539210</td>\n",
              "      <td>-0.395719</td>\n",
              "      <td>-0.310728</td>\n",
              "      <td>-0.238641</td>\n",
              "      <td>0.493765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.994041</td>\n",
              "      <td>1.364213</td>\n",
              "      <td>0.077728</td>\n",
              "      <td>0.750657</td>\n",
              "      <td>-0.057048</td>\n",
              "      <td>-0.088052</td>\n",
              "      <td>-0.539210</td>\n",
              "      <td>-0.395719</td>\n",
              "      <td>3.218244</td>\n",
              "      <td>4.190400</td>\n",
              "      <td>0.493765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.590525</td>\n",
              "      <td>-0.733023</td>\n",
              "      <td>-1.140999</td>\n",
              "      <td>-0.848413</td>\n",
              "      <td>-0.057048</td>\n",
              "      <td>-0.088052</td>\n",
              "      <td>-0.539210</td>\n",
              "      <td>-0.395719</td>\n",
              "      <td>-0.310728</td>\n",
              "      <td>-0.238641</td>\n",
              "      <td>0.493765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69296</th>\n",
              "      <td>-0.092729</td>\n",
              "      <td>1.364213</td>\n",
              "      <td>0.443347</td>\n",
              "      <td>0.124934</td>\n",
              "      <td>-0.057048</td>\n",
              "      <td>-0.088052</td>\n",
              "      <td>-0.539210</td>\n",
              "      <td>-0.395719</td>\n",
              "      <td>3.218244</td>\n",
              "      <td>-0.238641</td>\n",
              "      <td>0.493765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69297</th>\n",
              "      <td>1.269519</td>\n",
              "      <td>-0.733023</td>\n",
              "      <td>-0.775381</td>\n",
              "      <td>3.601172</td>\n",
              "      <td>0.072172</td>\n",
              "      <td>-0.035168</td>\n",
              "      <td>0.930805</td>\n",
              "      <td>1.351794</td>\n",
              "      <td>-0.310728</td>\n",
              "      <td>-0.238641</td>\n",
              "      <td>0.493765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69298</th>\n",
              "      <td>-0.163254</td>\n",
              "      <td>1.364213</td>\n",
              "      <td>2.271438</td>\n",
              "      <td>2.141152</td>\n",
              "      <td>0.330612</td>\n",
              "      <td>-0.035168</td>\n",
              "      <td>2.400819</td>\n",
              "      <td>-0.395719</td>\n",
              "      <td>-0.310728</td>\n",
              "      <td>4.190400</td>\n",
              "      <td>-2.025256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69299</th>\n",
              "      <td>1.200616</td>\n",
              "      <td>-0.733023</td>\n",
              "      <td>-0.166017</td>\n",
              "      <td>-0.153165</td>\n",
              "      <td>0.039867</td>\n",
              "      <td>-0.088052</td>\n",
              "      <td>-0.539210</td>\n",
              "      <td>1.351794</td>\n",
              "      <td>-0.310728</td>\n",
              "      <td>-0.238641</td>\n",
              "      <td>-2.025256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69300</th>\n",
              "      <td>0.434174</td>\n",
              "      <td>-0.733023</td>\n",
              "      <td>0.687092</td>\n",
              "      <td>-0.153165</td>\n",
              "      <td>-0.057048</td>\n",
              "      <td>-0.088052</td>\n",
              "      <td>0.930805</td>\n",
              "      <td>-0.395719</td>\n",
              "      <td>-0.310728</td>\n",
              "      <td>-0.238641</td>\n",
              "      <td>0.493765</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>69301 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            age    gender    height  ...     smoke      alco    active\n",
              "0      1.216018 -0.733023 -1.140999  ... -0.310728 -0.238641  0.493765\n",
              "1     -1.953915 -0.733023 -0.166017  ... -0.310728 -0.238641  0.493765\n",
              "2      0.985802 -0.733023  0.077728  ... -0.310728 -0.238641  0.493765\n",
              "3     -1.994041  1.364213  0.077728  ...  3.218244  4.190400  0.493765\n",
              "4      1.590525 -0.733023 -1.140999  ... -0.310728 -0.238641  0.493765\n",
              "...         ...       ...       ...  ...       ...       ...       ...\n",
              "69296 -0.092729  1.364213  0.443347  ...  3.218244 -0.238641  0.493765\n",
              "69297  1.269519 -0.733023 -0.775381  ... -0.310728 -0.238641  0.493765\n",
              "69298 -0.163254  1.364213  2.271438  ... -0.310728  4.190400 -2.025256\n",
              "69299  1.200616 -0.733023 -0.166017  ... -0.310728 -0.238641 -2.025256\n",
              "69300  0.434174 -0.733023  0.687092  ... -0.310728 -0.238641  0.493765\n",
              "\n",
              "[69301 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7C0IZIzEjiUw"
      },
      "source": [
        "#We will use f1 score as the metrics as it is balanced dataset problem\n",
        "#Maximum f1 score in between random states 42 to 100\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "def maxf1_score(clf,x,y):\n",
        "    maxf=0\n",
        "    rs=0\n",
        "    for r_state in range(42,100):\n",
        "        x_train,x_test,y_train,y_test=train_test_split(x, y,random_state = r_state,test_size=0.25,stratify=y)\n",
        "        clf.fit(x_train,y_train)\n",
        "        y_pred=clf.predict(x_test)\n",
        "        tmp=f1_score(y_test,y_pred)\n",
        "        print(\"random state :\",r_state,\" and f1 score: \",tmp)\n",
        "        if tmp>maxf:\n",
        "            maxf=tmp\n",
        "            rs=r_state\n",
        "    print(\"maximum f1_score is at random state :\",rs,\" and it is :\",maxf)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FEKTl3Fvjr-G"
      },
      "source": [
        "# 1. LogisticRegression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dIpYs9yejwbd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "547b9aa5-ea0c-41ca-b9c0-a9fc3f20349f"
      },
      "source": [
        "#lets use logistic regression\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "lg_clf=LogisticRegression()\n",
        "maxf1_score(lg_clf,x,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "random state : 42  and f1 score:  0.7003792667509482\n",
            "random state : 43  and f1 score:  0.7069722673404318\n",
            "random state : 44  and f1 score:  0.7018524142119648\n",
            "random state : 45  and f1 score:  0.7159383803872206\n",
            "random state : 46  and f1 score:  0.7072834999102281\n",
            "random state : 47  and f1 score:  0.7090010844680082\n",
            "random state : 48  and f1 score:  0.704664459825459\n",
            "random state : 49  and f1 score:  0.7066780404582014\n",
            "random state : 50  and f1 score:  0.7101966717095309\n",
            "random state : 51  and f1 score:  0.703420910238682\n",
            "random state : 52  and f1 score:  0.7049655672345053\n",
            "random state : 53  and f1 score:  0.7035438575162604\n",
            "random state : 54  and f1 score:  0.6994853163790492\n",
            "random state : 55  and f1 score:  0.7062095652699679\n",
            "random state : 56  and f1 score:  0.7122397400409195\n",
            "random state : 57  and f1 score:  0.7064838205220738\n",
            "random state : 58  and f1 score:  0.7052189562087583\n",
            "random state : 59  and f1 score:  0.7019562715765247\n",
            "random state : 60  and f1 score:  0.7110685194058456\n",
            "random state : 61  and f1 score:  0.7109295199182839\n",
            "random state : 62  and f1 score:  0.7036813518406759\n",
            "random state : 63  and f1 score:  0.707619966240656\n",
            "random state : 64  and f1 score:  0.7078427821046913\n",
            "random state : 65  and f1 score:  0.7129534925696408\n",
            "random state : 66  and f1 score:  0.7080423790031302\n",
            "random state : 67  and f1 score:  0.7121093514782922\n",
            "random state : 68  and f1 score:  0.7036790108491424\n",
            "random state : 69  and f1 score:  0.7085369532428357\n",
            "random state : 70  and f1 score:  0.6999395770392749\n",
            "random state : 71  and f1 score:  0.7097904209699826\n",
            "random state : 72  and f1 score:  0.7051605918440996\n",
            "random state : 73  and f1 score:  0.7097789887980624\n",
            "random state : 74  and f1 score:  0.7078909266409267\n",
            "random state : 75  and f1 score:  0.7085152838427948\n",
            "random state : 76  and f1 score:  0.7121039707731928\n",
            "random state : 77  and f1 score:  0.7028007984997883\n",
            "random state : 78  and f1 score:  0.7066916068303869\n",
            "random state : 79  and f1 score:  0.7064442026357152\n",
            "random state : 80  and f1 score:  0.7044783316255802\n",
            "random state : 81  and f1 score:  0.7105437466128741\n",
            "random state : 82  and f1 score:  0.704684317718941\n",
            "random state : 83  and f1 score:  0.70510191774213\n",
            "random state : 84  and f1 score:  0.7018284955645405\n",
            "random state : 85  and f1 score:  0.7100941801497223\n",
            "random state : 86  and f1 score:  0.7076811506617513\n",
            "random state : 87  and f1 score:  0.701820160851424\n",
            "random state : 88  and f1 score:  0.7089561162262347\n",
            "random state : 89  and f1 score:  0.7072658546829064\n",
            "random state : 90  and f1 score:  0.7122414622414622\n",
            "random state : 91  and f1 score:  0.7080094015548725\n",
            "random state : 92  and f1 score:  0.7065969090143726\n",
            "random state : 93  and f1 score:  0.7087454294791103\n",
            "random state : 94  and f1 score:  0.7136009586578789\n",
            "random state : 95  and f1 score:  0.7064894069082742\n",
            "random state : 96  and f1 score:  0.7051869722557298\n",
            "random state : 97  and f1 score:  0.7029535864978903\n",
            "random state : 98  and f1 score:  0.7098298104294107\n",
            "random state : 99  and f1 score:  0.7089036947304664\n",
            "maximum f1_score is at random state : 45  and it is : 0.7159383803872206\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G-b6xN93jypa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c273e3ab-6a43-46da-aef4-824423b05794"
      },
      "source": [
        "#Lets use cross_val_score and evaluate the logistic regression model\n",
        "from sklearn.model_selection import cross_val_score\n",
        "print(\"Mean f1 score for logistic classifier: \",cross_val_score(lg_clf,x,y,cv=5,scoring=\"f1\").mean())\n",
        "print(\"Standard deviation in f1 score for logistic classifier: \",cross_val_score(lg_clf,x,y,cv=5,scoring=\"f1\").std())\n",
        "print(cross_val_score(lg_clf,x,y,cv=5,scoring=\"f1\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean f1 score for logistic classifier:  0.7073847683571441\n",
            "Standard deviation in f1 score for logistic classifier:  0.006282594720711762\n",
            "[0.69754397 0.71374736 0.70551181 0.71465413 0.70546658]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ub5xtp1XlivT"
      },
      "source": [
        "# **2. GradientBoostingClassifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9rGcqsN3lmDO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99c542c5-4b66-43e1-a371-489c1231b79c"
      },
      "source": [
        "#Lets use Gradient boosting classifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "parameters={\"learning_rate\":[0.001,0.01,0.1,1],\"n_estimators\":[10,100,500,1000]}\n",
        "gb_clf=GradientBoostingClassifier()\n",
        "clf = GridSearchCV(gb_clf, parameters, cv=5,scoring=\"f1\")\n",
        "clf.fit(x,y)\n",
        "clf.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'learning_rate': 1, 'n_estimators': 10}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qq6bTKbOk1yt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "038e904e-d835-413f-ed49-35e1daafb284"
      },
      "source": [
        "gb_clf=GradientBoostingClassifier(learning_rate=1,n_estimators=10)\n",
        "maxf1_score(gb_clf,x,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "random state : 42  and f1 score:  0.7195325542570952\n",
            "random state : 43  and f1 score:  0.7256626578112846\n",
            "random state : 44  and f1 score:  0.7259621112832122\n",
            "random state : 45  and f1 score:  0.7292778370686589\n",
            "random state : 46  and f1 score:  0.7240823634735899\n",
            "random state : 47  and f1 score:  0.72597736933485\n",
            "random state : 48  and f1 score:  0.7227251071939019\n",
            "random state : 49  and f1 score:  0.7216346153846154\n",
            "random state : 50  and f1 score:  0.7298233046800382\n",
            "random state : 51  and f1 score:  0.7236521428996018\n",
            "random state : 52  and f1 score:  0.7243868965928755\n",
            "random state : 53  and f1 score:  0.7190770694576593\n",
            "random state : 54  and f1 score:  0.7223604692499112\n",
            "random state : 55  and f1 score:  0.7284831193533048\n",
            "random state : 56  and f1 score:  0.7248138218647603\n",
            "random state : 57  and f1 score:  0.7322737669344908\n",
            "random state : 58  and f1 score:  0.7254843870356106\n",
            "random state : 59  and f1 score:  0.7196802481656029\n",
            "random state : 60  and f1 score:  0.7334003429314728\n",
            "random state : 61  and f1 score:  0.7271327926114499\n",
            "random state : 62  and f1 score:  0.7263517493226528\n",
            "random state : 63  and f1 score:  0.7305417276720352\n",
            "random state : 64  and f1 score:  0.7245798573328497\n",
            "random state : 65  and f1 score:  0.7339352097716411\n",
            "random state : 66  and f1 score:  0.7239097033447583\n",
            "random state : 67  and f1 score:  0.7290681173131505\n",
            "random state : 68  and f1 score:  0.7220531184720979\n",
            "random state : 69  and f1 score:  0.7246168582375478\n",
            "random state : 70  and f1 score:  0.7190864212216737\n",
            "random state : 71  and f1 score:  0.7285620642393184\n",
            "random state : 72  and f1 score:  0.726488148016788\n",
            "random state : 73  and f1 score:  0.7251699058066053\n",
            "random state : 74  and f1 score:  0.7236763475558742\n",
            "random state : 75  and f1 score:  0.7231827699670953\n",
            "random state : 76  and f1 score:  0.7304612147074298\n",
            "random state : 77  and f1 score:  0.7233763914518722\n",
            "random state : 78  and f1 score:  0.7278103009875231\n",
            "random state : 79  and f1 score:  0.7268301371489639\n",
            "random state : 80  and f1 score:  0.7322397991944426\n",
            "random state : 81  and f1 score:  0.7244982781142383\n",
            "random state : 82  and f1 score:  0.7228758942825046\n",
            "random state : 83  and f1 score:  0.7274678111587982\n",
            "random state : 84  and f1 score:  0.7248154322457727\n",
            "random state : 85  and f1 score:  0.7269392343648305\n",
            "random state : 86  and f1 score:  0.7266263775328831\n",
            "random state : 87  and f1 score:  0.722345198923123\n",
            "random state : 88  and f1 score:  0.7245287520878072\n",
            "random state : 89  and f1 score:  0.7338681137194761\n",
            "random state : 90  and f1 score:  0.7254225184479887\n",
            "random state : 91  and f1 score:  0.72769533345253\n",
            "random state : 92  and f1 score:  0.7204165918477284\n",
            "random state : 93  and f1 score:  0.7296711027730551\n",
            "random state : 94  and f1 score:  0.7280414458405288\n",
            "random state : 95  and f1 score:  0.7219962358083905\n",
            "random state : 96  and f1 score:  0.7269681742043551\n",
            "random state : 97  and f1 score:  0.7253818268259344\n",
            "random state : 98  and f1 score:  0.726316418624111\n",
            "random state : 99  and f1 score:  0.7277580071174378\n",
            "maximum f1_score is at random state : 65  and it is : 0.7339352097716411\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxMCXT_0k12R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3baf2c43-bb2a-4a33-fb01-2d4aed6ee162"
      },
      "source": [
        "#Lets use cross_val_score and evaluate the model\n",
        "from sklearn.model_selection import cross_val_score\n",
        "print(\"Mean f1 score for GradientBoostingClassifier: \",cross_val_score(gb_clf,x,y,cv=5,scoring=\"f1\").mean())\n",
        "print(\"standard deviation in f1 score for GradientBoostingClassifier: \",cross_val_score(gb_clf,x,y,cv=5,scoring=\"f1\").std())\n",
        "print(cross_val_score(gb_clf,x,y,cv=5,scoring=\"f1\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean f1 score for GradientBoostingClassifier:  0.7257852030405181\n",
            "standard deviation in f1 score for GradientBoostingClassifier:  0.003005989346274857\n",
            "[0.72164339 0.72866945 0.72976964 0.72418438 0.72465916]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vLz1CiZElu9O"
      },
      "source": [
        "# **3. XGBClassifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RCk1cXno5GnC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0618769c-dcd8-4853-cf1d-ea7c02a67f71"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from xgboost import XGBClassifier\n",
        "xgb_clf = XGBClassifier()\n",
        "params = {\n",
        "        'min_child_weight': [1, 5, 10],\n",
        "        'gamma': [0.5, 1, 1.5, 2, 5],\n",
        "        'subsample': [0.6, 0.8, 1.0],\n",
        "        'colsample_bytree': [0.6, 0.8, 1.0],\n",
        "        'max_depth': [3, 4, 5]\n",
        "        }\n",
        "clf = GridSearchCV(xgb_clf, params, cv=5,scoring=\"f1\")\n",
        "clf.fit(x,y)\n",
        "clf.best_params_"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'colsample_bytree': 1.0,\n",
              " 'gamma': 5,\n",
              " 'max_depth': 3,\n",
              " 'min_child_weight': 10,\n",
              " 'subsample': 0.6}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c6qBs2aElzNV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "791671d8-bacc-4b6d-e048-56bfb38c642b"
      },
      "source": [
        "from xgboost import XGBClassifier\n",
        "xgb_clf = XGBClassifier(colsample_bytree=1.0, gamma= 5, max_depth= 3, min_child_weight= 10, subsample= 0.6)\n",
        "maxf1_score(xgb_clf,x,y)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "random state : 42  and f1 score:  0.719188767550702\n",
            "random state : 43  and f1 score:  0.7263088137839628\n",
            "random state : 44  and f1 score:  0.7243799153055052\n",
            "random state : 45  and f1 score:  0.7302455556216184\n",
            "random state : 46  and f1 score:  0.7255207086425665\n",
            "random state : 47  and f1 score:  0.7261146496815287\n",
            "random state : 48  and f1 score:  0.7223226117335261\n",
            "random state : 49  and f1 score:  0.7227746786320641\n",
            "random state : 50  and f1 score:  0.7266674736714684\n",
            "random state : 51  and f1 score:  0.723526249776026\n",
            "random state : 52  and f1 score:  0.7256189386181554\n",
            "random state : 53  and f1 score:  0.7201013207888548\n",
            "random state : 54  and f1 score:  0.7186293436293436\n",
            "random state : 55  and f1 score:  0.7268899981957058\n",
            "random state : 56  and f1 score:  0.7255183986575572\n",
            "random state : 57  and f1 score:  0.7271096226302254\n",
            "random state : 58  and f1 score:  0.7250608272506083\n",
            "random state : 59  and f1 score:  0.7166958940557538\n",
            "random state : 60  and f1 score:  0.7332377196127644\n",
            "random state : 61  and f1 score:  0.7253736718890689\n",
            "random state : 62  and f1 score:  0.7238347585210165\n",
            "random state : 63  and f1 score:  0.7280649397158886\n",
            "random state : 64  and f1 score:  0.7257801653890263\n",
            "random state : 65  and f1 score:  0.7296632046587019\n",
            "random state : 66  and f1 score:  0.723750298971538\n",
            "random state : 67  and f1 score:  0.7270342771982117\n",
            "random state : 68  and f1 score:  0.7216668673973262\n",
            "random state : 69  and f1 score:  0.7255847600675187\n",
            "random state : 70  and f1 score:  0.7193382088478599\n",
            "random state : 71  and f1 score:  0.7260651029200575\n",
            "random state : 72  and f1 score:  0.7242636075760291\n",
            "random state : 73  and f1 score:  0.7247407764649144\n",
            "random state : 74  and f1 score:  0.724152465827663\n",
            "random state : 75  and f1 score:  0.7236762754794356\n",
            "random state : 76  and f1 score:  0.7295013685588481\n",
            "random state : 77  and f1 score:  0.7224330021077989\n",
            "random state : 78  and f1 score:  0.7269784172661872\n",
            "random state : 79  and f1 score:  0.7260940032414912\n",
            "random state : 80  and f1 score:  0.7268804315253221\n",
            "random state : 81  and f1 score:  0.7249610171524529\n",
            "random state : 82  and f1 score:  0.7226730310262529\n",
            "random state : 83  and f1 score:  0.7266440112084899\n",
            "random state : 84  and f1 score:  0.7216296028445731\n",
            "random state : 85  and f1 score:  0.7231746792950486\n",
            "random state : 86  and f1 score:  0.7232078638216254\n",
            "random state : 87  and f1 score:  0.72016933776837\n",
            "random state : 88  and f1 score:  0.7274467575975113\n",
            "random state : 89  and f1 score:  0.7325491132740192\n",
            "random state : 90  and f1 score:  0.7251286962767868\n",
            "random state : 91  and f1 score:  0.7256573418177451\n",
            "random state : 92  and f1 score:  0.7213590472160087\n",
            "random state : 93  and f1 score:  0.7256224995521586\n",
            "random state : 94  and f1 score:  0.7282758620689654\n",
            "random state : 95  and f1 score:  0.7226087482606329\n",
            "random state : 96  and f1 score:  0.7262556390977444\n",
            "random state : 97  and f1 score:  0.7260306807286673\n",
            "random state : 98  and f1 score:  0.7282575985555221\n",
            "random state : 99  and f1 score:  0.7276646706586826\n",
            "maximum f1_score is at random state : 60  and it is : 0.7332377196127644\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PoZHVWOhk160"
      },
      "source": [
        "#Lets use cross_val_score and evaluate the model\n",
        "from sklearn.model_selection import cross_val_score\n",
        "print(\"Mean f1 score for XGBClassifier: \",cross_val_score(xgb_clf,x,y,cv=5,scoring=\"f1\").mean())\n",
        "print(\"standard deviation in f1 score for XGBClassifier: \",cross_val_score(xgb_clf,x,y,cv=5,scoring=\"f1\").std())\n",
        "print(cross_val_score(xgb_clf,x,y,cv=5,scoring=\"f1\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pu3b2Sq7bDqb"
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "gb_clf=GradientBoostingClassifier(learning_rate=1,n_estimators=10)\n",
        "x_train,x_test,y_train,y_test=train_test_split(x, y,random_state = 65,test_size=0.20,stratify=y)\n",
        "gb_clf.fit(x_train,y_train)\n",
        "y_pred=gb_clf.predict(x_test)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vXQNwGPqbEW6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9259db2e-2497-44c2-eb59-b09c79221c80"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import roc_auc_score\n",
        "print(\"Confusion matrix \\n\",confusion_matrix(y_test,y_pred))\n",
        "print(\"f1 score is : \",f1_score(y_test,y_pred))\n",
        "print(\"classification report \\n\",classification_report(y_test,y_pred))\n",
        "print(\"AUC ROC Score: \",roc_auc_score(y_test,y_pred))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix \n",
            " [[5337 1599]\n",
            " [2016 4909]]\n",
            "f1 score is :  0.7308866224968362\n",
            "classification report \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      0.77      0.75      6936\n",
            "           1       0.75      0.71      0.73      6925\n",
            "\n",
            "    accuracy                           0.74     13861\n",
            "   macro avg       0.74      0.74      0.74     13861\n",
            "weighted avg       0.74      0.74      0.74     13861\n",
            "\n",
            "AUC ROC Score:  0.7391722671230311\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}