Mining_proje_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Mining proje .ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/saidoyk/MyTesting/blob/main/Mining_proje_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xJAKrCpeQzD-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "18a6fd3a-0391-4adc-8e83-c05b7ad855cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
              "0    75.0        0                       582         0                 20   \n",
              "1    55.0        0                      7861         0                 38   \n",
              "2    65.0        0                       146         0                 20   \n",
              "3    50.0        1                       111         0                 20   \n",
              "4    65.0        1                       160         1                 20   \n",
              "..    ...      ...                       ...       ...                ...   \n",
              "294  62.0        0                        61         1                 38   \n",
              "295  55.0        0                      1820         0                 38   \n",
              "296  45.0        0                      2060         1                 60   \n",
              "297  45.0        0                      2413         0                 38   \n",
              "298  50.0        0                       196         0                 45   \n",
              "\n",
              "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
              "0                      1  265000.00               1.9           130    1   \n",
              "1                      0  263358.03               1.1           136    1   \n",
              "2                      0  162000.00               1.3           129    1   \n",
              "3                      0  210000.00               1.9           137    1   \n",
              "4                      0  327000.00               2.7           116    0   \n",
              "..                   ...        ...               ...           ...  ...   \n",
              "294                    1  155000.00               1.1           143    1   \n",
              "295                    0  270000.00               1.2           139    0   \n",
              "296                    0  742000.00               0.8           138    0   \n",
              "297                    0  140000.00               1.4           140    1   \n",
              "298                    0  395000.00               1.6           136    1   \n",
              "\n",
              "     smoking  time  DEATH_EVENT  \n",
              "0          0     4            1  \n",
              "1          0     6            1  \n",
              "2          1     7            1  \n",
              "3          0     7            1  \n",
              "4          0     8            1  \n",
              "..       ...   ...          ...  \n",
              "294        1   270            0  \n",
              "295        0   271            0  \n",
              "296        0   278            0  \n",
              "297        1   280            0  \n",
              "298        1   285            0  \n",
              "\n",
              "[299 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bd17c294-f23a-4522-91ef-3ea4dc49699f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>anaemia</th>\n",
              "      <th>creatinine_phosphokinase</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>ejection_fraction</th>\n",
              "      <th>high_blood_pressure</th>\n",
              "      <th>platelets</th>\n",
              "      <th>serum_creatinine</th>\n",
              "      <th>serum_sodium</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoking</th>\n",
              "      <th>time</th>\n",
              "      <th>DEATH_EVENT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>75.0</td>\n",
              "      <td>0</td>\n",
              "      <td>582</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>265000.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>130</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>55.0</td>\n",
              "      <td>0</td>\n",
              "      <td>7861</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "      <td>263358.03</td>\n",
              "      <td>1.1</td>\n",
              "      <td>136</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65.0</td>\n",
              "      <td>0</td>\n",
              "      <td>146</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>162000.00</td>\n",
              "      <td>1.3</td>\n",
              "      <td>129</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50.0</td>\n",
              "      <td>1</td>\n",
              "      <td>111</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>210000.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>137</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>65.0</td>\n",
              "      <td>1</td>\n",
              "      <td>160</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>327000.00</td>\n",
              "      <td>2.7</td>\n",
              "      <td>116</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>294</th>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>155000.00</td>\n",
              "      <td>1.1</td>\n",
              "      <td>143</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>270</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>295</th>\n",
              "      <td>55.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1820</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.00</td>\n",
              "      <td>1.2</td>\n",
              "      <td>139</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>271</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>45.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2060</td>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>742000.00</td>\n",
              "      <td>0.8</td>\n",
              "      <td>138</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>278</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>297</th>\n",
              "      <td>45.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2413</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "      <td>140000.00</td>\n",
              "      <td>1.4</td>\n",
              "      <td>140</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>280</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>50.0</td>\n",
              "      <td>0</td>\n",
              "      <td>196</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>395000.00</td>\n",
              "      <td>1.6</td>\n",
              "      <td>136</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>285</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>299 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bd17c294-f23a-4522-91ef-3ea4dc49699f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bd17c294-f23a-4522-91ef-3ea4dc49699f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bd17c294-f23a-4522-91ef-3ea4dc49699f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 360
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.svm import SVC\n",
        "data_df=pd.read_csv('heart_failure_clinical_records_dataset.csv')\n",
        "data_df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df.info()"
      ],
      "metadata": {
        "id": "BgBNZqmPS3jk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78a108b3-136c-4842-d09a-9b70ded8dc49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 299 entries, 0 to 298\n",
            "Data columns (total 13 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   age                       299 non-null    float64\n",
            " 1   anaemia                   299 non-null    int64  \n",
            " 2   creatinine_phosphokinase  299 non-null    int64  \n",
            " 3   diabetes                  299 non-null    int64  \n",
            " 4   ejection_fraction         299 non-null    int64  \n",
            " 5   high_blood_pressure       299 non-null    int64  \n",
            " 6   platelets                 299 non-null    float64\n",
            " 7   serum_creatinine          299 non-null    float64\n",
            " 8   serum_sodium              299 non-null    int64  \n",
            " 9   sex                       299 non-null    int64  \n",
            " 10  smoking                   299 non-null    int64  \n",
            " 11  time                      299 non-null    int64  \n",
            " 12  DEATH_EVENT               299 non-null    int64  \n",
            "dtypes: float64(3), int64(10)\n",
            "memory usage: 30.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df.duplicated().any()"
      ],
      "metadata": {
        "id": "XBD5Ds3qS5P8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00ed0fd8-ef95-4b7a-b732-c604b4216ef0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 362
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import missingno as mso\n",
        "mso.bar(data_df)"
      ],
      "metadata": {
        "id": "aKsfF_9QS8PE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        },
        "outputId": "baf91a23-d2f2-4718-96d1-13d38d21dc6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdf34192150>"
            ]
          },
          "metadata": {},
          "execution_count": 363
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x720 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#import seaborn as sns\n",
        "#sns.pairplot(dataset_df)"
      ],
      "metadata": {
        "id": "NlJFQ9UgsLp2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_df = data_df.drop(data_df[(data_df.DEATH_EVENT == 0)].index)\n",
        "data_df"
      ],
      "metadata": {
        "id": "62hF8I4uYzoi",
        "outputId": "e11d619e-7b3d-4829-9c37-dc6f4b940cb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
              "0    75.0        0                       582         0                 20   \n",
              "1    55.0        0                      7861         0                 38   \n",
              "2    65.0        0                       146         0                 20   \n",
              "3    50.0        1                       111         0                 20   \n",
              "4    65.0        1                       160         1                 20   \n",
              "..    ...      ...                       ...       ...                ...   \n",
              "220  73.0        0                       582         0                 20   \n",
              "230  60.0        0                       166         0                 30   \n",
              "246  55.0        0                      2017         0                 25   \n",
              "262  65.0        1                       258         1                 25   \n",
              "266  55.0        0                      1199         0                 20   \n",
              "\n",
              "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
              "0                      1  265000.00              1.90           130    1   \n",
              "1                      0  263358.03              1.10           136    1   \n",
              "2                      0  162000.00              1.30           129    1   \n",
              "3                      0  210000.00              1.90           137    1   \n",
              "4                      0  327000.00              2.70           116    0   \n",
              "..                   ...        ...               ...           ...  ...   \n",
              "220                    0  263358.03              1.83           134    1   \n",
              "230                    0   62000.00              1.70           127    0   \n",
              "246                    0  314000.00              1.10           138    1   \n",
              "262                    0  198000.00              1.40           129    1   \n",
              "266                    0  263358.03              1.83           134    1   \n",
              "\n",
              "     smoking  time  DEATH_EVENT  \n",
              "0          0     4            1  \n",
              "1          0     6            1  \n",
              "2          1     7            1  \n",
              "3          0     7            1  \n",
              "4          0     8            1  \n",
              "..       ...   ...          ...  \n",
              "220        0   198            1  \n",
              "230        0   207            1  \n",
              "246        0   214            1  \n",
              "262        0   235            1  \n",
              "266        1   241            1  \n",
              "\n",
              "[96 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-30fa7dab-57db-4477-ae3d-53176f5b0a5c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>anaemia</th>\n",
              "      <th>creatinine_phosphokinase</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>ejection_fraction</th>\n",
              "      <th>high_blood_pressure</th>\n",
              "      <th>platelets</th>\n",
              "      <th>serum_creatinine</th>\n",
              "      <th>serum_sodium</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoking</th>\n",
              "      <th>time</th>\n",
              "      <th>DEATH_EVENT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>75.0</td>\n",
              "      <td>0</td>\n",
              "      <td>582</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>265000.00</td>\n",
              "      <td>1.90</td>\n",
              "      <td>130</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>55.0</td>\n",
              "      <td>0</td>\n",
              "      <td>7861</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "      <td>263358.03</td>\n",
              "      <td>1.10</td>\n",
              "      <td>136</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65.0</td>\n",
              "      <td>0</td>\n",
              "      <td>146</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>162000.00</td>\n",
              "      <td>1.30</td>\n",
              "      <td>129</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50.0</td>\n",
              "      <td>1</td>\n",
              "      <td>111</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>210000.00</td>\n",
              "      <td>1.90</td>\n",
              "      <td>137</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>65.0</td>\n",
              "      <td>1</td>\n",
              "      <td>160</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>327000.00</td>\n",
              "      <td>2.70</td>\n",
              "      <td>116</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>220</th>\n",
              "      <td>73.0</td>\n",
              "      <td>0</td>\n",
              "      <td>582</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>263358.03</td>\n",
              "      <td>1.83</td>\n",
              "      <td>134</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>230</th>\n",
              "      <td>60.0</td>\n",
              "      <td>0</td>\n",
              "      <td>166</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>62000.00</td>\n",
              "      <td>1.70</td>\n",
              "      <td>127</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>207</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>55.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>314000.00</td>\n",
              "      <td>1.10</td>\n",
              "      <td>138</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>214</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>262</th>\n",
              "      <td>65.0</td>\n",
              "      <td>1</td>\n",
              "      <td>258</td>\n",
              "      <td>1</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>198000.00</td>\n",
              "      <td>1.40</td>\n",
              "      <td>129</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>235</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>266</th>\n",
              "      <td>55.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1199</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>263358.03</td>\n",
              "      <td>1.83</td>\n",
              "      <td>134</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>241</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>96 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30fa7dab-57db-4477-ae3d-53176f5b0a5c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-30fa7dab-57db-4477-ae3d-53176f5b0a5c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-30fa7dab-57db-4477-ae3d-53176f5b0a5c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 365
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df['time'].describe()"
      ],
      "metadata": {
        "id": "6yOjyFt9nxnr",
        "outputId": "2a268019-eee1-4f86-dc26-5ea58449baba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     96.000000\n",
              "mean      70.885417\n",
              "std       62.378281\n",
              "min        4.000000\n",
              "25%       25.500000\n",
              "50%       44.500000\n",
              "75%      102.250000\n",
              "max      241.000000\n",
              "Name: time, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 366
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df['time'][data_df['time']<=71]=0\n",
        "data_df['time'][(data_df['time']>71) & (data_df['time']<=242)]=1\n",
        "data_df['time'][(data_df['time']>85) & (data_df['time']<=242)]=2\n",
        "\n",
        "data_df['creatinine_phosphokinase']=(data_df['creatinine_phosphokinase']-data_df['creatinine_phosphokinase'].min())/(data_df['creatinine_phosphokinase'].max()-data_df['creatinine_phosphokinase'].min())\n",
        "data_df['age']=(data_df['age']-data_df['age'].min())/(data_df['age'].max()-data_df['age'].min())\n",
        "data_df['ejection_fraction']=(data_df['ejection_fraction']-data_df['ejection_fraction'].min())/(data_df['ejection_fraction'].max()-data_df['ejection_fraction'].min())\n",
        "data_df['platelets']=(data_df['platelets']-data_df['platelets'].min())/(data_df['platelets'].max()-data_df['platelets'].min())\n",
        "data_df['serum_creatinine']=(data_df['serum_creatinine']-data_df['serum_creatinine'].min())/(data_df['serum_creatinine'].max()-data_df['serum_creatinine'].min())\n",
        "data_df['serum_sodium']=(data_df['serum_sodium']-data_df['serum_sodium'].min())/(data_df['serum_sodium'].max()-data_df['serum_sodium'].min())\n",
        "\n",
        "data_df"
      ],
      "metadata": {
        "id": "tEjBVRP6crMK",
        "outputId": "e6383cad-5b9c-47ea-acfe-9b8b5ff60269",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 748
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: 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",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: 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",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
              "0    0.622642        0                  0.071319         0           0.107143   \n",
              "1    0.245283        0                  1.000000         0           0.428571   \n",
              "2    0.433962        0                  0.015693         0           0.107143   \n",
              "3    0.150943        1                  0.011227         0           0.107143   \n",
              "4    0.433962        1                  0.017479         1           0.107143   \n",
              "..        ...      ...                       ...       ...                ...   \n",
              "220  0.584906        0                  0.071319         0           0.107143   \n",
              "230  0.339623        0                  0.018244         0           0.285714   \n",
              "246  0.245283        0                  0.254402         0           0.196429   \n",
              "262  0.433962        1                  0.029982         1           0.196429   \n",
              "266  0.245283        0                  0.150038         0           0.107143   \n",
              "\n",
              "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
              "0                      1   0.379791          0.147727      0.466667    1   \n",
              "1                      0   0.376930          0.056818      0.666667    1   \n",
              "2                      0   0.200348          0.079545      0.433333    1   \n",
              "3                      0   0.283972          0.147727      0.700000    1   \n",
              "4                      0   0.487805          0.238636      0.000000    0   \n",
              "..                   ...        ...               ...           ...  ...   \n",
              "220                    0   0.376930          0.139773      0.600000    1   \n",
              "230                    0   0.026132          0.125000      0.366667    0   \n",
              "246                    0   0.465157          0.056818      0.733333    1   \n",
              "262                    0   0.263066          0.090909      0.433333    1   \n",
              "266                    0   0.376930          0.139773      0.600000    1   \n",
              "\n",
              "     smoking  time  DEATH_EVENT  \n",
              "0          0     0            1  \n",
              "1          0     0            1  \n",
              "2          1     0            1  \n",
              "3          0     0            1  \n",
              "4          0     0            1  \n",
              "..       ...   ...          ...  \n",
              "220        0     1            1  \n",
              "230        0     1            1  \n",
              "246        0     1            1  \n",
              "262        0     1            1  \n",
              "266        1     1            1  \n",
              "\n",
              "[96 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-48f96021-eb70-4d33-bf07-08536f97e94c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>anaemia</th>\n",
              "      <th>creatinine_phosphokinase</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>ejection_fraction</th>\n",
              "      <th>high_blood_pressure</th>\n",
              "      <th>platelets</th>\n",
              "      <th>serum_creatinine</th>\n",
              "      <th>serum_sodium</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoking</th>\n",
              "      <th>time</th>\n",
              "      <th>DEATH_EVENT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.622642</td>\n",
              "      <td>0</td>\n",
              "      <td>0.071319</td>\n",
              "      <td>0</td>\n",
              "      <td>0.107143</td>\n",
              "      <td>1</td>\n",
              "      <td>0.379791</td>\n",
              "      <td>0.147727</td>\n",
              "      <td>0.466667</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.245283</td>\n",
              "      <td>0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0</td>\n",
              "      <td>0.376930</td>\n",
              "      <td>0.056818</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.433962</td>\n",
              "      <td>0</td>\n",
              "      <td>0.015693</td>\n",
              "      <td>0</td>\n",
              "      <td>0.107143</td>\n",
              "      <td>0</td>\n",
              "      <td>0.200348</td>\n",
              "      <td>0.079545</td>\n",
              "      <td>0.433333</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.150943</td>\n",
              "      <td>1</td>\n",
              "      <td>0.011227</td>\n",
              "      <td>0</td>\n",
              "      <td>0.107143</td>\n",
              "      <td>0</td>\n",
              "      <td>0.283972</td>\n",
              "      <td>0.147727</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.433962</td>\n",
              "      <td>1</td>\n",
              "      <td>0.017479</td>\n",
              "      <td>1</td>\n",
              "      <td>0.107143</td>\n",
              "      <td>0</td>\n",
              "      <td>0.487805</td>\n",
              "      <td>0.238636</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>220</th>\n",
              "      <td>0.584906</td>\n",
              "      <td>0</td>\n",
              "      <td>0.071319</td>\n",
              "      <td>0</td>\n",
              "      <td>0.107143</td>\n",
              "      <td>0</td>\n",
              "      <td>0.376930</td>\n",
              "      <td>0.139773</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>230</th>\n",
              "      <td>0.339623</td>\n",
              "      <td>0</td>\n",
              "      <td>0.018244</td>\n",
              "      <td>0</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0</td>\n",
              "      <td>0.026132</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>0.366667</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>0.245283</td>\n",
              "      <td>0</td>\n",
              "      <td>0.254402</td>\n",
              "      <td>0</td>\n",
              "      <td>0.196429</td>\n",
              "      <td>0</td>\n",
              "      <td>0.465157</td>\n",
              "      <td>0.056818</td>\n",
              "      <td>0.733333</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>262</th>\n",
              "      <td>0.433962</td>\n",
              "      <td>1</td>\n",
              "      <td>0.029982</td>\n",
              "      <td>1</td>\n",
              "      <td>0.196429</td>\n",
              "      <td>0</td>\n",
              "      <td>0.263066</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.433333</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>266</th>\n",
              "      <td>0.245283</td>\n",
              "      <td>0</td>\n",
              "      <td>0.150038</td>\n",
              "      <td>0</td>\n",
              "      <td>0.107143</td>\n",
              "      <td>0</td>\n",
              "      <td>0.376930</td>\n",
              "      <td>0.139773</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>96 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-48f96021-eb70-4d33-bf07-08536f97e94c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-48f96021-eb70-4d33-bf07-08536f97e94c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-48f96021-eb70-4d33-bf07-08536f97e94c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 367
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "h8sl-9Ca4gxb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "cm = np.corrcoef(data_df.values.T)\n",
        "sns.set(font_scale=1.5)\n",
        "fig,ax=plt.subplots(figsize=(28,20))\n",
        "cols=['age', 'anaemia', 'creatine','diabets', 'ejection','blood plessure','plateles','serum ceratine','serum sodium','sex','smokking','time']\n",
        "sns.heatmap(cm, annot=True, yticklabels=cols, xticklabels=cols)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6hiicvFip3-8",
        "outputId": "c02d3043-2d2c-4d6e-fd22-b29b158cce06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/lib/function_base.py:2691: RuntimeWarning: invalid value encountered in true_divide\n",
            "  c /= stddev[:, None]\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/lib/function_base.py:2692: RuntimeWarning: invalid value encountered in true_divide\n",
            "  c /= stddev[None, :]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdf34061610>"
            ]
          },
          "metadata": {},
          "execution_count": 368
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2016x1440 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.describe()"
      ],
      "metadata": {
        "id": "7FppTg92m0pT",
        "outputId": "d2a2901c-a6d0-4bd0-a813-8cc6dde9deb6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    96.000000\n",
              "mean      0.895833\n",
              "std       0.851984\n",
              "min       0.000000\n",
              "25%       0.000000\n",
              "50%       1.000000\n",
              "75%       2.000000\n",
              "max       2.000000\n",
              "Name: time, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 369
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=data_df.pop('time')\n",
        "data_df.drop('DEATH_EVENT',axis=1,inplace=True)\n",
        "X = data_df[['age', 'anaemia', 'ejection_fraction','high_blood_pressure']]\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test=train_test_split(X,y,stratify=y,test_size=0.1)\n",
        "\n"
      ],
      "metadata": {
        "id": "AGiyZTgrWLUd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(plt.boxplot(data_df[\"age\"]))\n",
        "plt.show()\n",
        "print(plt.boxplot(data_df[\"anaemia\"]))\n",
        "plt.show()\n",
        "print(plt.boxplot(data_df[\"ejection_fraction\"]))\n",
        "plt.show()\n",
        "print(plt.boxplot(data_df[\"high_blood_pressure\"]))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cLBMf0XbxdM-",
        "outputId": "f6df80ad-187b-4eb5-affc-df8100c5a53c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'whiskers': [<matplotlib.lines.Line2D object at 0x7fdf38cd3690>, <matplotlib.lines.Line2D object at 0x7fdf34fd0410>], 'caps': [<matplotlib.lines.Line2D object at 0x7fdf34fd0810>, <matplotlib.lines.Line2D object at 0x7fdf34fd0d90>], 'boxes': [<matplotlib.lines.Line2D object at 0x7fdf394c5b10>], 'medians': [<matplotlib.lines.Line2D object at 0x7fdf37b9b790>], 'fliers': [<matplotlib.lines.Line2D object at 0x7fdf3526acd0>], 'means': []}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'whiskers': [<matplotlib.lines.Line2D object at 0x7fdf342f6c90>, <matplotlib.lines.Line2D object at 0x7fdf342f6fd0>], 'caps': [<matplotlib.lines.Line2D object at 0x7fdf342f6050>, <matplotlib.lines.Line2D object at 0x7fdf34208d50>], 'boxes': [<matplotlib.lines.Line2D object at 0x7fdf3424a050>], 'medians': [<matplotlib.lines.Line2D object at 0x7fdf34208b90>], 'fliers': [<matplotlib.lines.Line2D object at 0x7fdf3423f490>], 'means': []}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'whiskers': [<matplotlib.lines.Line2D object at 0x7fdf3432d590>, <matplotlib.lines.Line2D object at 0x7fdf3432d110>], 'caps': [<matplotlib.lines.Line2D object at 0x7fdf342bbad0>, <matplotlib.lines.Line2D object at 0x7fdf342bbf90>], 'boxes': [<matplotlib.lines.Line2D object at 0x7fdf3432da50>], 'medians': [<matplotlib.lines.Line2D object at 0x7fdf34300090>], 'fliers': [<matplotlib.lines.Line2D object at 0x7fdf34300110>], 'means': []}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'whiskers': [<matplotlib.lines.Line2D object at 0x7fdf345e7b10>, <matplotlib.lines.Line2D object at 0x7fdf343d8550>], 'caps': [<matplotlib.lines.Line2D object at 0x7fdf343d8750>, <matplotlib.lines.Line2D object at 0x7fdf343d8ed0>], 'boxes': [<matplotlib.lines.Line2D object at 0x7fdf34385790>], 'medians': [<matplotlib.lines.Line2D object at 0x7fdf343f3fd0>], 'fliers': [<matplotlib.lines.Line2D object at 0x7fdf343f3f90>], 'means': []}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "clf=LogisticRegression()\n",
        "clf.fit(X_train,y_train)\n",
        "y_predict_train=clf.predict(X_train)\n",
        "y_predict_test=clf.predict(X_test)\n",
        "\n",
        "\n",
        "from sklearn.metrics import accuracy_score,f1_score,plot_confusion_matrix,classification_report\n",
        "print(classification_report(y_test,y_predict_test))\n",
        "\n",
        "plot_confusion_matrix(clf,X_test,y_test)\n",
        "\n",
        "from sklearn.metrics import accuracy_score,classification_report,plot_confusion_matrix\n",
        "print(\"Train:\",accuracy_score(y_train,y_predict_train))\n",
        "print(\"Test:\",accuracy_score(y_test,y_predict_test))"
      ],
      "metadata": {
        "id": "mJeE8779WvhT",
        "outputId": "af744dac-eb6f-4fa2-b1c7-8a3b6c1de7bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.60      1.00      0.75         6\n",
            "           1       0.00      0.00      0.00         4\n",
            "\n",
            "    accuracy                           0.60        10\n",
            "   macro avg       0.30      0.50      0.37        10\n",
            "weighted avg       0.36      0.60      0.45        10\n",
            "\n",
            "Train: 0.627906976744186\n",
            "Test: 0.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "clf=SVC(kernel='linear',C=1.0,class_weight='balanced')\n",
        "clf.fit(X_train,y_predict_train)\n",
        "y_predict_test=clf.predict(X_test)\n",
        "y_predict_train=clf.predict(X_train)\n",
        "print(classification_report(y_test,y_predict_test))\n",
        "\n",
        "plot_confusion_matrix(clf,X_test,y_test)\n",
        "\n",
        "print(\"Train:\",accuracy_score(y_train,y_predict_train))\n",
        "print(\"Test:\",accuracy_score(y_test,y_predict_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "NL8qCTqM3z3a",
        "outputId": "9cad9f8c-3809-459c-9ae7-d3ad75a0cdea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-373-66fbb838f0ff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msvm\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSVC\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'linear'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'balanced'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_predict_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0my_predict_test\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0my_predict_train\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/svm/_base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    197\u001b[0m             )\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m         sample_weight = np.asarray(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/svm/_base.py\u001b[0m in \u001b[0;36m_validate_targets\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m    720\u001b[0m             raise ValueError(\n\u001b[1;32m    721\u001b[0m                 \u001b[0;34m\"The number of classes has to be greater than one; got %d class\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m                 \u001b[0;34m%\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    723\u001b[0m             )\n\u001b[1;32m    724\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: The number of classes has to be greater than one; got 1 class"
          ]
        }
      ]
    }
  ]
}