Strokes.ipynb

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  "metadata": {
    "colab": {
      "name": "Strokes.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNZDIHKaRIF+jejqvLEAuSH",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/giacomoaccursi/DataIntensiveExercises/blob/main/Strokes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vtdepR_T5dY0"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "from urllib.request import urlretrieve\n",
        "def download(file, url):\n",
        "    if not os.path.isfile(file):\n",
        "        urlretrieve(url, file)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kmgPi0aZg2dp"
      },
      "source": [
        "download(\"stroke.csv\", \"https://gist.githubusercontent.com/aishwarya8615/d2107f828d3f904839cbcb7eaa85bd04/raw/cec0340503d82d270821e03254993b6dede60afb/healthcare-dataset-stroke-data.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jpbb500P6nK_"
      },
      "source": [
        "data = pd.read_csv(\"stroke.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "nTE_4_i766vD",
        "outputId": "fc093345-188f-4dc4-b505-e41e0b6fdd2c"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\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>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>work_type</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>smoking_status</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9046</td>\n",
              "      <td>Male</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>228.69</td>\n",
              "      <td>36.6</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>51676</td>\n",
              "      <td>Female</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>202.21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31112</td>\n",
              "      <td>Male</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>105.92</td>\n",
              "      <td>32.5</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>60182</td>\n",
              "      <td>Female</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>171.23</td>\n",
              "      <td>34.4</td>\n",
              "      <td>smokes</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1665</td>\n",
              "      <td>Female</td>\n",
              "      <td>79.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>174.12</td>\n",
              "      <td>24.0</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id  gender   age  ...   bmi   smoking_status stroke\n",
              "0   9046    Male  67.0  ...  36.6  formerly smoked      1\n",
              "1  51676  Female  61.0  ...   NaN     never smoked      1\n",
              "2  31112    Male  80.0  ...  32.5     never smoked      1\n",
              "3  60182  Female  49.0  ...  34.4           smokes      1\n",
              "4   1665  Female  79.0  ...  24.0     never smoked      1\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "96CCtuNX6-G-",
        "outputId": "45442eeb-e14b-45b3-f555-e5bc146f14fa"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5110 entries, 0 to 5109\n",
            "Data columns (total 12 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   id                 5110 non-null   int64  \n",
            " 1   gender             5110 non-null   object \n",
            " 2   age                5110 non-null   float64\n",
            " 3   hypertension       5110 non-null   int64  \n",
            " 4   heart_disease      5110 non-null   int64  \n",
            " 5   ever_married       5110 non-null   object \n",
            " 6   work_type          5110 non-null   object \n",
            " 7   Residence_type     5110 non-null   object \n",
            " 8   avg_glucose_level  5110 non-null   float64\n",
            " 9   bmi                4909 non-null   float64\n",
            " 10  smoking_status     5110 non-null   object \n",
            " 11  stroke             5110 non-null   int64  \n",
            "dtypes: float64(3), int64(4), object(5)\n",
            "memory usage: 479.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XmsfPfvi7A5k",
        "outputId": "ac30a484-5515-4202-c10f-f4ca814b1cdd"
      },
      "source": [
        "data[\"stroke\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4861\n",
              "1     249\n",
              "Name: stroke, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B56DJUzi7xoe",
        "outputId": "2ee374b1-9be8-4b53-81aa-31ebff605bce"
      },
      "source": [
        "data[\"stroke\"].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0487279843444227"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 297
        },
        "id": "CAhPbVQC7RBL",
        "outputId": "46e6545e-9a74-49f0-da11-cddcf94f043c"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "\n",
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              "</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>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5110.000000</td>\n",
              "      <td>5110.000000</td>\n",
              "      <td>5110.000000</td>\n",
              "      <td>5110.000000</td>\n",
              "      <td>5110.000000</td>\n",
              "      <td>4909.000000</td>\n",
              "      <td>5110.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>36517.829354</td>\n",
              "      <td>43.226614</td>\n",
              "      <td>0.097456</td>\n",
              "      <td>0.054012</td>\n",
              "      <td>106.147677</td>\n",
              "      <td>28.893237</td>\n",
              "      <td>0.048728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>21161.721625</td>\n",
              "      <td>22.612647</td>\n",
              "      <td>0.296607</td>\n",
              "      <td>0.226063</td>\n",
              "      <td>45.283560</td>\n",
              "      <td>7.854067</td>\n",
              "      <td>0.215320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>67.000000</td>\n",
              "      <td>0.080000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>55.120000</td>\n",
              "      <td>10.300000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>17741.250000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>77.245000</td>\n",
              "      <td>23.500000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>36932.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>91.885000</td>\n",
              "      <td>28.100000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>54682.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>114.090000</td>\n",
              "      <td>33.100000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>72940.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>271.740000</td>\n",
              "      <td>97.600000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id          age  ...          bmi       stroke\n",
              "count   5110.000000  5110.000000  ...  4909.000000  5110.000000\n",
              "mean   36517.829354    43.226614  ...    28.893237     0.048728\n",
              "std    21161.721625    22.612647  ...     7.854067     0.215320\n",
              "min       67.000000     0.080000  ...    10.300000     0.000000\n",
              "25%    17741.250000    25.000000  ...    23.500000     0.000000\n",
              "50%    36932.000000    45.000000  ...    28.100000     0.000000\n",
              "75%    54682.000000    61.000000  ...    33.100000     0.000000\n",
              "max    72940.000000    82.000000  ...    97.600000     1.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "oznRfAiRAEhp",
        "outputId": "86cd7db5-8f87-4921-f439-65bf8b16a9bb"
      },
      "source": [
        "data[\"age\"].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc30130ddd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "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": 283
        },
        "id": "ts3H5Pgv7UHd",
        "outputId": "e6c10eff-61fe-407b-b534-dfccb6cfeefe"
      },
      "source": [
        "data[data[\"stroke\"] == 1][\"age\"].plot.hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc300b2a610>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "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": 284
        },
        "id": "2OCR6C4E8D09",
        "outputId": "0255887c-6c5b-4744-f076-5ad466e931fe"
      },
      "source": [
        "data[data[\"stroke\"] == 1][\"work_type\"].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc300abea10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "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": 283
        },
        "id": "ommgpBln82R7",
        "outputId": "43e0db6d-86ed-4ae2-9b2a-76a65df0039b"
      },
      "source": [
        "data[data[\"stroke\"] == 1][\"smoking_status\"].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc300a9a290>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "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": 427
        },
        "id": "rtI-8rim_8JS",
        "outputId": "dcb7b0a9-13c7-48f8-970d-d097f646f3d0"
      },
      "source": [
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 5), sharey=True)\n",
        "((data[data[\"age\"] < 60].groupby(\"smoking_status\")[\"stroke\"].mean())*100).plot.bar(ax=axes[0])\n",
        "((data[data[\"age\"] >= 60].groupby(\"smoking_status\")[\"stroke\"].mean())*100).plot.bar(ax=axes[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc3009985d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "FEAoNyo1D0vB",
        "outputId": "4cdf40aa-7f22-4e58-b79a-f18080c0cb3c"
      },
      "source": [
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n",
        "\n",
        "data[data[\"stroke\"] == 1][\"avg_glucose_level\"].hist(ax=axes[0])\n",
        "data[data[\"stroke\"] == 0][\"avg_glucose_level\"].hist(ax=axes[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc300885210>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "dycLpUwFJWdk",
        "outputId": "0e158865-acee-4d9c-c871-2cabcc132b83"
      },
      "source": [
        "data.groupby(\"heart_disease\")[\"stroke\"].mean().plot.bar()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc3008b8bd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEECAYAAAAmiP8hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAATxElEQVR4nO3df6xf9X3f8ecrdkxSWCGBuyjFdu0OR91F6Vh6cbZuoVmiEXtR8LSaBadVoWLyptb7oTZqXEVyqVNNZV1D18WV4hQWCksNRWvkDS8Og05MjBKbH4FcmNNboNjOtFx+hI5khBje++N7rHz59tr32PdeX/Px8yF9dc/5fD7nfN9fZL2+h88553tSVUiS2vWmxS5AkrSwDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYtXewCRl1wwQW1atWqxS5Dkt5QHnzwwWeramymvtMu6FetWsX+/fsXuwxJekNJ8ufH6nPqRpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktS40+6GKUlzt2rrnYtdQjOe/o0PL3YJc+YRvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxvYI+ybokB5JMJdk6Q/9lSR5KciTJxpG+lUm+nOSJJI8nWTU/pUuS+pg16JMsAXYA64FxYFOS8ZFhzwDXAF+YYRe/D/xmVf11YC3wzbkULEk6MX1+AmEtMFVVTwIk2QVsAB4/OqCqnu76XhvesPtCWFpVd3XjXpqfsiVJffWZurkQODi0fqhr6+NdwLeS/KckDyf5ze7/ECRJp8hCn4xdCrwP+DhwKfAjDKZ4XifJ5iT7k+yfnp5e4JIk6czSJ+gPAyuG1pd3bX0cAh6pqier6gjwReA9o4OqamdVTVTVxNjYWM9dS5L66BP0+4A1SVYnWQZcBezuuf99wHlJjqb3Bxia25ckLbxZg747Et8C7AWeAG6vqskk25NcAZDk0iSHgCuBzyaZ7LZ9lcG0zd1JHgMCfG5hPookaSa9HjxSVXuAPSNt24aW9zGY0plp27uAH5tDjZKkOfDOWElqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhrXK+iTrEtyIMlUkq0z9F+W5KEkR5JsnKH/B5McSvKZ+ShaktTfrEGfZAmwA1gPjAObkoyPDHuGwUO/v3CM3XwKuPfky5Qknaw+R/RrganuAd+vALuADcMDqurpqnoUeG104yQ/DrwD+PI81CtJOkF9gv5C4ODQ+qGubVZJ3gT8FoPnxkqSFsFCn4z9eWBPVR063qAkm5PsT7J/enp6gUuSpDNLn4eDHwZWDK0v79r6+NvA+5L8PHAOsCzJS1X1uhO6VbUT2AkwMTFRPfctSeqhT9DvA9YkWc0g4K8CPtZn51X100eXk1wDTIyGvCRpYc06dVNVR4AtwF7gCeD2qppMsj3JFQBJLk1yCLgS+GySyYUsWpLUX58jeqpqD7BnpG3b0PI+BlM6x9vH54HPn3CFkqQ58c5YSWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjegV9knVJDiSZSvKXHgWY5LIkDyU5kmTjUPslSe5PMpnk0SQfnc/iJUmzmzXokywBdgDrgXFgU5LxkWHPANcAXxhp/w7ws1V1MbAO+O0k5821aElSf30eJbgWmKqqJwGS7AI2AI8fHVBVT3d9rw1vWFVfH1r+RpJvAmPAt+ZcuSSplz5TNxcCB4fWD3VtJyTJWmAZ8Gcnuq0k6eSdkpOxSd4J3AL8XFW9NkP/5iT7k+yfnp4+FSVJ0hmjT9AfBlYMrS/v2npJ8oPAncAnq+pPZhpTVTuraqKqJsbGxvruWpLUQ5+g3wesSbI6yTLgKmB3n5134/8I+P2quuPky5QknaxZg76qjgBbgL3AE8DtVTWZZHuSKwCSXJrkEHAl8Nkkk93m/xi4DLgmySPd65IF+SSSpBn1ueqGqtoD7Blp2za0vI/BlM7odrcCt86xRknSHHhnrCQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY3rFfRJ1iU5kGQqydYZ+i9L8lCSI0k2jvRdneRPu9fV81W4JKmfWYM+yRJgB7AeGAc2JRkfGfYMcA3whZFt3w78KvBeYC3wq0neNveyJUl99TmiXwtMVdWTVfUKsAvYMDygqp6uqkeB10a2/RBwV1U9X1UvAHcB6+ahbklST32C/kLg4ND6oa6tj7lsK0maB6fFydgkm5PsT7J/enp6scuRpKb0CfrDwIqh9eVdWx+9tq2qnVU1UVUTY2NjPXctSeqjT9DvA9YkWZ1kGXAVsLvn/vcClyd5W3cS9vKuTZJ0iswa9FV1BNjCIKCfAG6vqskk25NcAZDk0iSHgCuBzyaZ7LZ9HvgUgy+LfcD2rk2SdIos7TOoqvYAe0batg0t72MwLTPTtjcBN82hRknSHJwWJ2MlSQvHoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjesV9EnWJTmQZCrJ1hn6z0pyW9f/QJJVXfubk9yc5LEkTyT5lfktX5I0m1mDPskSYAewHhgHNiUZHxl2LfBCVV0E3ABc37VfCZxVVe8Gfhz4p0e/BCRJp0afI/q1wFRVPVlVrwC7gA0jYzYAN3fLdwAfTBKggLOTLAXeCrwC/MW8VC5J6qVP0F8IHBxaP9S1zTime8bsi8D5DEL/28D/Bp4B/q3PjJWkU2uhT8auBV4FfghYDfxSkh8ZHZRkc5L9SfZPT08vcEmSdGbpE/SHgRVD68u7thnHdNM05wLPAR8DvlRV36uqbwL3AROjb1BVO6tqoqomxsbGTvxTSJKOqU/Q7wPWJFmdZBlwFbB7ZMxu4OpueSNwT1UVg+maDwAkORv4W8D/mo/CJUn9zBr03Zz7FmAv8ARwe1VNJtme5Ipu2I3A+UmmgF8Ejl6CuQM4J8kkgy+M/1BVj873h5AkHdvSPoOqag+wZ6Rt29DyywwupRzd7qWZ2iVJp453xkpS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGtcr6JOsS3IgyVSSrTP0n5Xktq7/gSSrhvp+LMn9SSaTPJbkLfNXviRpNrMGfZIlDB4JuB4YBzYlGR8Zdi3wQlVdBNwAXN9tuxS4FfhnVXUx8H7ge/NWvSRpVn2O6NcCU1X1ZFW9AuwCNoyM2QDc3C3fAXwwSYDLgUer6qsAVfVcVb06P6VLkvroE/QXAgeH1g91bTOO6R4m/iJwPvAuoJLsTfJQkl+ee8mSpBPR6+Hgc9z/3wUuBb4D3J3kwaq6e3hQks3AZoCVK1cucEmSdGbpc0R/GFgxtL68a5txTDcvfy7wHIOj/3ur6tmq+g6wB3jP6BtU1c6qmqiqibGxsRP/FJKkY+oT9PuANUlWJ1kGXAXsHhmzG7i6W94I3FNVBewF3p3kB7ovgJ8EHp+f0iVJfcw6dVNVR5JsYRDaS4CbqmoyyXZgf1XtBm4EbkkyBTzP4MuAqnohyacZfFkUsKeq7lygzyJJmkGvOfqq2sNg2mW4bdvQ8svAlcfY9lYGl1hKkhaBd8ZKUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUuF5Bn2RdkgNJppJsnaH/rCS3df0PJFk10r8yyUtJPj4/ZUuS+po16JMsAXYA64FxYFOS8ZFh1wIvVNVFwA3A9SP9nwb+69zLlSSdqD5PmFoLTFXVkwBJdgEbeP2zXzcA13XLdwCfSZKqqiT/EHgK+Pa8VX0aWLXVJyLOp6d/48OLXYLUrD5TNxcCB4fWD3VtM46pqiPAi8D5Sc4BPgH82txLlSSdjIU+GXsdcENVvXS8QUk2J9mfZP/09PQClyRJZ5Y+UzeHgRVD68u7tpnGHEqyFDgXeA54L7Axyb8BzgNeS/JyVX1meOOq2gnsBJiYmKiT+SCSpJn1Cfp9wJokqxkE+lXAx0bG7AauBu4HNgL3VFUB7zs6IMl1wEujIS9JWlizBn1VHUmyBdgLLAFuqqrJJNuB/VW1G7gRuCXJFPA8gy8DSdJpoM8RPVW1B9gz0rZtaPll4MpZ9nHdSdQnSZoj74yVpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcb2CPsm6JAeSTCXZOkP/WUlu6/ofSLKqa//7SR5M8lj39wPzW74kaTazBn2SJcAOYD0wDmxKMj4y7Frghaq6CLgBuL5rfxb4SFW9m8GjBm+Zr8IlSf30OaJfC0xV1ZNV9QqwC9gwMmYDcHO3fAfwwSSpqoer6htd+yTw1iRnzUfhkqR++gT9hcDBofVDXduMY6rqCPAicP7ImJ8CHqqq755cqZKkk9HrmbFzleRiBtM5lx+jfzOwGWDlypWnoiRJOmP0OaI/DKwYWl/etc04JslS4FzguW59OfBHwM9W1Z/N9AZVtbOqJqpqYmxs7MQ+gSTpuPoE/T5gTZLVSZYBVwG7R8bsZnCyFWAjcE9VVZLzgDuBrVV133wVLUnqb9ag7+bctwB7gSeA26tqMsn2JFd0w24Ezk8yBfwicPQSzC3ARcC2JI90r786759CknRMveboq2oPsGekbdvQ8svAlTNs9+vAr8+xRknSHHhnrCQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcb2CPsm6JAeSTCXZOkP/WUlu6/ofSLJqqO9XuvYDST40f6VLkvqYNeiTLAF2AOuBcWBTkvGRYdcCL1TVRcANwPXdtuMMnjF7MbAO+N1uf5KkU6TPEf1aYKqqnqyqV4BdwIaRMRuAm7vlO4APJknXvquqvltVTwFT3f4kSadIn6C/EDg4tH6oa5txTPcw8ReB83tuK0laQL0eDr7QkmwGNnerLyU5sJj1NOYC4NnFLmI2uX6xK9AiOe3/fb6B/m3+8LE6+gT9YWDF0Pryrm2mMYeSLAXOBZ7ruS1VtRPY2aMWnaAk+6tqYrHrkGbiv89To8/UzT5gTZLVSZYxOLm6e2TMbuDqbnkjcE9VVdd+VXdVzmpgDfCV+SldktTHrEf0VXUkyRZgL7AEuKmqJpNsB/ZX1W7gRuCWJFPA8wy+DOjG3Q48DhwBfqGqXl2gzyJJmkEGB95qVZLN3dSYdNrx3+epYdBLUuP8CQRJapxBL0mNOy2uo9f8SfKjDO5IPnpj2mFgd1U9sXhVSVpMHtE3JMknGPxERRhcxvqVbvkPZvoxOul0keTnFruGlnkytiFJvg5cXFXfG2lfBkxW1ZrFqUw6viTPVNXKxa6jVU7dtOU14IeAPx9pf2fXJy2aJI8eqwt4x6ms5Uxj0LflXwF3J/lTvv9jciuBi4Ati1aVNPAO4EPACyPtAf7nqS/nzGHQN6SqvpTkXQx+Cnr4ZOw+70jWaeC/AOdU1SOjHUn++6kv58zhHL0kNc6rbiSpcQa9JDXOoJekxhn0ekNIsirJ1xZgv5ck+QcnuM3TSS7olr1aRKc9g15nrO5paJcAJxT0w6rqJ+avImlhGPR6I1mS5HNJJpN8Oclbk/y1JF9K8mCS/9H91g9JPpLkgSQPJ/lvSd7RtV+X5JYk9wG3ANuBjyZ5JMlHZ3rTJOd37zeZ5PcYXPd9tO+l7u87k9zb7edrSd7XtV+e5P4kDyX5wyTndO3bkuzrxu5Mkq79XyR5PMmjSXZ1bWcnuSnJV7rPs2Gh/gOrUVXly9dp/wJWMXhK2SXd+u3AzwB3A2u6tvcyeIwlwNv4/uXD/wT4rW75OuBB4K3d+jXAZ2Z5798BtnXLHwYKuKBbf6n7+0vAJ7vlJcBfYfDg63uBs7v2Twzt5+1D+78F+Ei3/A3grG75vO7vvwZ+5mgb8PWj+/Tlq8/LG6b0RvJUff9mmwcZhP9PAH/YHRADnNX9XQ7cluSdwDLgqaH97K6q/3cC73sZ8I8AqurOJKN3dsLg2co3JXkz8MWqeiTJTwLjwH1dfcuA+7vxfy/JLwM/ALwdmAT+M/Ao8B+TfBH4Yjf2cuCKJB/v1t/C4I5nf5FUvRj0eiP57tDyqwxuqf9WVV0yw9h/D3y6qnYneT+DI/mjvj3fhVXVvUkuY3DE//kkn2Zwq/9dVbVpeGyStwC/C0xU1cEk1zEIb7rtLwM+AnwyybsZTBX9VFUdmO+6dWZwjl5vZH8BPJXkSoAM/I2u71wGP/8AcPVx9vF/GUyzHM+9wMe691jPYFrodZL8MPB/qupzwO8B7wH+BPg7SS7qxpzd/UTF0VB/tpuz39j1vwlYUVV/zGCa51zgHGAv8M+H5vH/5iz1Sq9j0OuN7qeBa5N8lcH0x9ETldcxmNJ5EHj2ONv/MTB+vJOxwK8BlyWZZDCF88wMY94PfDXJw8BHgX9XVdMMzgH8QffLjfcDP1pV3wI+B3yNQYjv6/axBLg1yWPAw8DvdGM/BbwZeLSr4VPH+TzSX+Jv3UhS4zyil6TGeTJW6nSPs/uXI833VdUvLEY90nxx6kaSGufUjSQ1zqCXpMYZ9JLUOINekhpn0EtS4/4/PYMTA5s7cSEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d-2AMpSiMqVH"
      },
      "source": [
        "## Data Processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Qtc12MIS1Esl",
        "outputId": "ba0fd4ec-2d6b-445e-920e-de153db5e2a9"
      },
      "source": [
        "data.head()"
      ],
      "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>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>work_type</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>smoking_status</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9046</td>\n",
              "      <td>Male</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>228.69</td>\n",
              "      <td>36.6</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>51676</td>\n",
              "      <td>Female</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>202.21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31112</td>\n",
              "      <td>Male</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>105.92</td>\n",
              "      <td>32.5</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>60182</td>\n",
              "      <td>Female</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>171.23</td>\n",
              "      <td>34.4</td>\n",
              "      <td>smokes</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1665</td>\n",
              "      <td>Female</td>\n",
              "      <td>79.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>174.12</td>\n",
              "      <td>24.0</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id  gender   age  ...   bmi   smoking_status stroke\n",
              "0   9046    Male  67.0  ...  36.6  formerly smoked      1\n",
              "1  51676  Female  61.0  ...   NaN     never smoked      1\n",
              "2  31112    Male  80.0  ...  32.5     never smoked      1\n",
              "3  60182  Female  49.0  ...  34.4           smokes      1\n",
              "4   1665  Female  79.0  ...  24.0     never smoked      1\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mxqPz_uaLmrR",
        "outputId": "789a5fcd-9196-4e43-8bf4-e1dca0664e9e"
      },
      "source": [
        "data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                     0\n",
              "gender                 0\n",
              "age                    0\n",
              "hypertension           0\n",
              "heart_disease          0\n",
              "ever_married           0\n",
              "work_type              0\n",
              "Residence_type         0\n",
              "avg_glucose_level      0\n",
              "bmi                  201\n",
              "smoking_status         0\n",
              "stroke                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KB2hTVvwMvgW"
      },
      "source": [
        "bmi_std = data[\"bmi\"].std()\n",
        "bmi_mean = data[\"bmi\"].mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7H3PZf-_nMNO"
      },
      "source": [
        "null_bmi = data[data[\"bmi\"].isnull()][\"bmi\"]\n",
        "null_bmi_number = data[\"bmi\"].isnull().sum()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VpuGWMmVnekg"
      },
      "source": [
        "random_bmi = np.random.randint(low=bmi_mean - bmi_std, high=bmi_mean + bmi_std, size = null_bmi_number)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z8Smd665oWap",
        "outputId": "e66f4001-146a-4fa6-8c4a-3ef85ae52737"
      },
      "source": [
        "data[\"bmi\"][data[\"bmi\"].isnull()] = random_bmi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7rvMltPsoYCg",
        "outputId": "eb910aee-5c32-4485-d0ee-52de94f04e01"
      },
      "source": [
        "data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                   0\n",
              "gender               0\n",
              "age                  0\n",
              "hypertension         0\n",
              "heart_disease        0\n",
              "ever_married         0\n",
              "work_type            0\n",
              "Residence_type       0\n",
              "avg_glucose_level    0\n",
              "bmi                  0\n",
              "smoking_status       0\n",
              "stroke               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gEyIlzeU14xJ"
      },
      "source": [
        "data = data.drop(\"id\", axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9rSSicVSpPim",
        "outputId": "0442df89-e2b5-4be8-d2da-45987de02101"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5110 entries, 0 to 5109\n",
            "Data columns (total 11 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   gender             5110 non-null   object \n",
            " 1   age                5110 non-null   float64\n",
            " 2   hypertension       5110 non-null   int64  \n",
            " 3   heart_disease      5110 non-null   int64  \n",
            " 4   ever_married       5110 non-null   object \n",
            " 5   work_type          5110 non-null   object \n",
            " 6   Residence_type     5110 non-null   object \n",
            " 7   avg_glucose_level  5110 non-null   float64\n",
            " 8   bmi                5110 non-null   float64\n",
            " 9   smoking_status     5110 non-null   object \n",
            " 10  stroke             5110 non-null   int64  \n",
            "dtypes: float64(3), int64(3), object(5)\n",
            "memory usage: 439.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VoNxsbhx-UQs",
        "outputId": "f75e1684-f8d0-4787-cbd7-27f75c7fbf65"
      },
      "source": [
        "from imblearn.over_sampling import SMOTENC\n",
        "smote = SMOTENC(random_state = 42, categorical_features=[0, 4, 5, 6, 9])\n",
        "X = data.drop(\"stroke\", axis =1)\n",
        "X_col = X.columns\n",
        "y = data[\"stroke\"]\n",
        "X, y = smote.fit_resample(X, y)\n",
        "X = pd.DataFrame(X, columns=X_col)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1a58tij77MQF"
      },
      "source": [
        "data[\"age\"] = data[\"age\"].astype(int)\n",
        "data[\"avg_glucose_level\"] = data[\"avg_glucose_level\"].astype(int)\n",
        "data[\"bmi\"] = data[\"bmi\"].astype(int)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FjO25aqJnOjm"
      },
      "source": [
        "X[\"age\"] = X[\"age\"].astype(int)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0M1s_DqVpZwM"
      },
      "source": [
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "from keras.utils import to_categorical\n",
        "ohe = OneHotEncoder(sparse=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eerZQgNYtHBQ"
      },
      "source": [
        "from sklearn.compose import make_column_transformer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g6sopl4xtJvV"
      },
      "source": [
        "column_trans = make_column_transformer(\n",
        "    (OneHotEncoder(), [\"gender\", \"ever_married\", \"work_type\", \"Residence_type\", \"smoking_status\"]), \n",
        "    (StandardScaler(), [\"age\", \"avg_glucose_level\", \"bmi\"])\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oDiIyRZ0t91F"
      },
      "source": [
        "X = column_trans.fit_transform(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BlmlNB4Wy0NN"
      },
      "source": [
        "from sklearn.model_selection import train_test_split, KFold, cross_validate"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m8LAhCg5qyn6"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_pvMi3qqHCe"
      },
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "15kwXk2QzPQw"
      },
      "source": [
        "kf = KFold(4, shuffle=True, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BvxRPsOUr9pb"
      },
      "source": [
        "model = Pipeline([\n",
        "                  (\"poly\", PolynomialFeatures(degree = 2, include_bias=False)), \n",
        "                  (\"logreg\", LogisticRegression())\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vvvUF5zZzUDR",
        "outputId": "d09b5ed9-171a-43bd-b207-f32e79cd4cb9"
      },
      "source": [
        "model = cross_validate(model, X_train, y_train, cv = kf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "57-tG3ZusSED",
        "outputId": "ccff82a8-e40c-4e41-9b13-c0e30b4c5c1e"
      },
      "source": [
        "model[\"test_score\"].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8163879178376403"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKDrNIK20R63"
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82CaJ8al0__v"
      },
      "source": [
        "yc_train = to_categorical(y_train)\n",
        "yc_test = to_categorical(y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3fPVQwyQ2_EE"
      },
      "source": [
        "model = Sequential([\n",
        "  Dense(32, activation = \"relu\", input_dim = X.shape[1]),\n",
        "  Dense(2, activation=\"softmax\")\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rX2_5cgw3IwW"
      },
      "source": [
        "model.compile(optimizer=\"adam\", \n",
        "              loss=\"categorical_crossentropy\", \n",
        "              metrics = [\"accuracy\"]\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g31g_whn3T_J",
        "outputId": "f9a1cb5c-08ea-4f16-9155-3925196d12dd"
      },
      "source": [
        "model.fit(X_train, yc_train, batch_size=10, epochs=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.5384 - accuracy: 0.7142\n",
            "Epoch 2/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3995 - accuracy: 0.8072\n",
            "Epoch 3/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.4045 - accuracy: 0.8069\n",
            "Epoch 4/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3871 - accuracy: 0.8249\n",
            "Epoch 5/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3692 - accuracy: 0.8365\n",
            "Epoch 6/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3629 - accuracy: 0.8419\n",
            "Epoch 7/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3687 - accuracy: 0.8383\n",
            "Epoch 8/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3630 - accuracy: 0.8402\n",
            "Epoch 9/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3502 - accuracy: 0.8541\n",
            "Epoch 10/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3478 - accuracy: 0.8518\n",
            "Epoch 11/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3379 - accuracy: 0.8571\n",
            "Epoch 12/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3390 - accuracy: 0.8617\n",
            "Epoch 13/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3253 - accuracy: 0.8651\n",
            "Epoch 14/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3358 - accuracy: 0.8610\n",
            "Epoch 15/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3180 - accuracy: 0.8693\n",
            "Epoch 16/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3226 - accuracy: 0.8679\n",
            "Epoch 17/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3237 - accuracy: 0.8666\n",
            "Epoch 18/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3209 - accuracy: 0.8631\n",
            "Epoch 19/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3292 - accuracy: 0.8604\n",
            "Epoch 20/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3121 - accuracy: 0.8707\n",
            "Epoch 21/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3040 - accuracy: 0.8770\n",
            "Epoch 22/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2986 - accuracy: 0.8769\n",
            "Epoch 23/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3145 - accuracy: 0.8770\n",
            "Epoch 24/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.3124 - accuracy: 0.8745\n",
            "Epoch 25/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2902 - accuracy: 0.8810\n",
            "Epoch 26/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2892 - accuracy: 0.8847\n",
            "Epoch 27/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2998 - accuracy: 0.8825\n",
            "Epoch 28/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2920 - accuracy: 0.8786\n",
            "Epoch 29/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2857 - accuracy: 0.8907\n",
            "Epoch 30/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2819 - accuracy: 0.8957\n",
            "Epoch 31/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2955 - accuracy: 0.8828\n",
            "Epoch 32/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2940 - accuracy: 0.8867\n",
            "Epoch 33/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2803 - accuracy: 0.8910\n",
            "Epoch 34/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2870 - accuracy: 0.8880\n",
            "Epoch 35/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2869 - accuracy: 0.8886\n",
            "Epoch 36/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2867 - accuracy: 0.8896\n",
            "Epoch 37/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2853 - accuracy: 0.8867\n",
            "Epoch 38/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2814 - accuracy: 0.8952\n",
            "Epoch 39/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2727 - accuracy: 0.8967\n",
            "Epoch 40/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2879 - accuracy: 0.8877\n",
            "Epoch 41/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2747 - accuracy: 0.8972\n",
            "Epoch 42/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2775 - accuracy: 0.8947\n",
            "Epoch 43/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2717 - accuracy: 0.8963\n",
            "Epoch 44/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2749 - accuracy: 0.8939\n",
            "Epoch 45/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2679 - accuracy: 0.8892\n",
            "Epoch 46/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2819 - accuracy: 0.8917\n",
            "Epoch 47/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2675 - accuracy: 0.8962\n",
            "Epoch 48/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2750 - accuracy: 0.8974\n",
            "Epoch 49/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2726 - accuracy: 0.8942\n",
            "Epoch 50/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2756 - accuracy: 0.8921\n",
            "Epoch 51/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2568 - accuracy: 0.9042\n",
            "Epoch 52/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2589 - accuracy: 0.9034\n",
            "Epoch 53/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2656 - accuracy: 0.8996\n",
            "Epoch 54/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2628 - accuracy: 0.8985\n",
            "Epoch 55/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2556 - accuracy: 0.9060\n",
            "Epoch 56/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2758 - accuracy: 0.8962\n",
            "Epoch 57/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2607 - accuracy: 0.8987\n",
            "Epoch 58/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2461 - accuracy: 0.9061\n",
            "Epoch 59/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2592 - accuracy: 0.8978\n",
            "Epoch 60/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2672 - accuracy: 0.8955\n",
            "Epoch 61/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2589 - accuracy: 0.9010\n",
            "Epoch 62/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2654 - accuracy: 0.8947\n",
            "Epoch 63/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2556 - accuracy: 0.9012\n",
            "Epoch 64/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2509 - accuracy: 0.9055\n",
            "Epoch 65/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2696 - accuracy: 0.8993\n",
            "Epoch 66/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2561 - accuracy: 0.9063\n",
            "Epoch 67/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2524 - accuracy: 0.9029\n",
            "Epoch 68/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2675 - accuracy: 0.8991\n",
            "Epoch 69/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2566 - accuracy: 0.9014\n",
            "Epoch 70/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2470 - accuracy: 0.9076\n",
            "Epoch 71/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2657 - accuracy: 0.8947\n",
            "Epoch 72/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2481 - accuracy: 0.9001\n",
            "Epoch 73/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2493 - accuracy: 0.9046\n",
            "Epoch 74/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2526 - accuracy: 0.9004\n",
            "Epoch 75/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2447 - accuracy: 0.9074\n",
            "Epoch 76/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2669 - accuracy: 0.8960\n",
            "Epoch 77/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2508 - accuracy: 0.9020\n",
            "Epoch 78/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2540 - accuracy: 0.9055\n",
            "Epoch 79/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2596 - accuracy: 0.9033\n",
            "Epoch 80/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2438 - accuracy: 0.9071\n",
            "Epoch 81/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2612 - accuracy: 0.9013\n",
            "Epoch 82/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2485 - accuracy: 0.9043\n",
            "Epoch 83/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2452 - accuracy: 0.9091\n",
            "Epoch 84/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2572 - accuracy: 0.9004\n",
            "Epoch 85/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2548 - accuracy: 0.9001\n",
            "Epoch 86/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2626 - accuracy: 0.9002\n",
            "Epoch 87/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2669 - accuracy: 0.8974\n",
            "Epoch 88/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2430 - accuracy: 0.9099\n",
            "Epoch 89/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2645 - accuracy: 0.8954\n",
            "Epoch 90/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2566 - accuracy: 0.9031\n",
            "Epoch 91/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2522 - accuracy: 0.9019\n",
            "Epoch 92/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2415 - accuracy: 0.9100\n",
            "Epoch 93/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2423 - accuracy: 0.9048\n",
            "Epoch 94/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2466 - accuracy: 0.9052\n",
            "Epoch 95/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2403 - accuracy: 0.9057\n",
            "Epoch 96/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2449 - accuracy: 0.9096\n",
            "Epoch 97/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2400 - accuracy: 0.9121\n",
            "Epoch 98/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2356 - accuracy: 0.9125\n",
            "Epoch 99/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2403 - accuracy: 0.9104\n",
            "Epoch 100/100\n",
            "649/649 [==============================] - 1s 1ms/step - loss: 0.2288 - accuracy: 0.9139\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fc2ad777650>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O4F42aKU3eDt",
        "outputId": "5bd04806-a7b8-4122-ac3b-a4b229537953"
      },
      "source": [
        "model.evaluate(X_test, yc_test, batch_size=20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "163/163 [==============================] - 0s 894us/step - loss: 0.3130 - accuracy: 0.8821\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.31296780705451965, 0.8821351528167725]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ci5Ie5t931BG"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1D4dR6PYu_Rx"
      },
      "source": [
        "rf_model = RandomForestClassifier(n_estimators=100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jpcwcLe7vHKb",
        "outputId": "1101bef6-3eac-439a-fb96-f5a6908e11b3"
      },
      "source": [
        "rf_model.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vgAnC-0KvLD3",
        "outputId": "46598145-08f2-4d94-c794-5bb29de16a87"
      },
      "source": [
        "rf_model.score(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9385991977784635"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_9tQ0_isvPbd"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}