Prediction_Heart_Failure.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import missingno as msno\n",
        "from sklearn.impute import SimpleImputer\n",
        "%matplotlib inline"
      ]
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      "source": [
        "df=pd.read_csv(\"/content/Heart Failure Clinical Records.csv\")\n",
        "df"
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              "      age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
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              "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
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              "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
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          },
          "metadata": {},
          "execution_count": 3
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    {
      "cell_type": "code",
      "source": [
        "df.tail()"
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      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 226
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        "id": "OX-qY9hwcl3-",
        "outputId": "d91dfa32-5bf5-4e65-b21b-3713452a3f41"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
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              "298  50.0        0                       196         0                 45   \n",
              "\n",
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              "    .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-5f8bcee4-3cbd-4850-9bc7-b4a6e02ada59 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-5f8bcee4-3cbd-4850-9bc7-b4a6e02ada59');\n",
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              "\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f292f225-67bc-4abf-9e69-b9aab06e117f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ncihwJ5RcohM",
        "outputId": "051b796a-112a-4fa8-9748-651a10379aa8"
      },
      "execution_count": 5,
      "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": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HvNZK3cGcqQ-",
        "outputId": "d5efbe9c-d701-447b-fc4d-0b84ebe2a51a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                         float64\n",
              "anaemia                       int64\n",
              "creatinine_phosphokinase      int64\n",
              "diabetes                      int64\n",
              "ejection_fraction             int64\n",
              "high_blood_pressure           int64\n",
              "platelets                   float64\n",
              "serum_creatinine            float64\n",
              "serum_sodium                  int64\n",
              "sex                           int64\n",
              "smoking                       int64\n",
              "time                          int64\n",
              "DEATH_EVENT                   int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "column_name=df.columns\n",
        "print(column_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3M6hRIyDcvT4",
        "outputId": "860964f2-e55b-4055-d1c1-3d5b1428ca8c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['age', 'anaemia', 'creatinine_phosphokinase', 'diabetes',\n",
            "       'ejection_fraction', 'high_blood_pressure', 'platelets',\n",
            "       'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time',\n",
            "       'DEATH_EVENT'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in df.columns:\n",
        "  print('{}is unqiue: {}'.format(i, df[i].is_unique))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S95RyjScc-dx",
        "outputId": "3e3d8a33-a366-4c2c-d75b-e6453d44a45f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ageis unqiue: False\n",
            "anaemiais unqiue: False\n",
            "creatinine_phosphokinaseis unqiue: False\n",
            "diabetesis unqiue: False\n",
            "ejection_fractionis unqiue: False\n",
            "high_blood_pressureis unqiue: False\n",
            "plateletsis unqiue: False\n",
            "serum_creatinineis unqiue: False\n",
            "serum_sodiumis unqiue: False\n",
            "sexis unqiue: False\n",
            "smokingis unqiue: False\n",
            "timeis unqiue: False\n",
            "DEATH_EVENTis unqiue: False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "4iJi8ReudxGR",
        "outputId": "168b8dac-4a55-48f0-a70d-8902cff18095"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          count           mean           std      min  \\\n",
              "age                       299.0      60.833893     11.894809     40.0   \n",
              "anaemia                   299.0       0.431438      0.496107      0.0   \n",
              "creatinine_phosphokinase  299.0     581.839465    970.287881     23.0   \n",
              "diabetes                  299.0       0.418060      0.494067      0.0   \n",
              "ejection_fraction         299.0      38.083612     11.834841     14.0   \n",
              "high_blood_pressure       299.0       0.351171      0.478136      0.0   \n",
              "platelets                 299.0  263358.029264  97804.236869  25100.0   \n",
              "serum_creatinine          299.0       1.393880      1.034510      0.5   \n",
              "serum_sodium              299.0     136.625418      4.412477    113.0   \n",
              "sex                       299.0       0.648829      0.478136      0.0   \n",
              "smoking                   299.0       0.321070      0.467670      0.0   \n",
              "time                      299.0     130.260870     77.614208      4.0   \n",
              "DEATH_EVENT               299.0       0.321070      0.467670      0.0   \n",
              "\n",
              "                               25%       50%       75%       max  \n",
              "age                           51.0      60.0      70.0      95.0  \n",
              "anaemia                        0.0       0.0       1.0       1.0  \n",
              "creatinine_phosphokinase     116.5     250.0     582.0    7861.0  \n",
              "diabetes                       0.0       0.0       1.0       1.0  \n",
              "ejection_fraction             30.0      38.0      45.0      80.0  \n",
              "high_blood_pressure            0.0       0.0       1.0       1.0  \n",
              "platelets                 212500.0  262000.0  303500.0  850000.0  \n",
              "serum_creatinine               0.9       1.1       1.4       9.4  \n",
              "serum_sodium                 134.0     137.0     140.0     148.0  \n",
              "sex                            0.0       1.0       1.0       1.0  \n",
              "smoking                        0.0       0.0       1.0       1.0  \n",
              "time                          73.0     115.0     203.0     285.0  \n",
              "DEATH_EVENT                    0.0       0.0       1.0       1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cac9e080-88be-420b-ae40-76b21d95a91e\" 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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>299.0</td>\n",
              "      <td>60.833893</td>\n",
              "      <td>11.894809</td>\n",
              "      <td>40.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>95.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>anaemia</th>\n",
              "      <td>299.0</td>\n",
              "      <td>0.431438</td>\n",
              "      <td>0.496107</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>creatinine_phosphokinase</th>\n",
              "      <td>299.0</td>\n",
              "      <td>581.839465</td>\n",
              "      <td>970.287881</td>\n",
              "      <td>23.0</td>\n",
              "      <td>116.5</td>\n",
              "      <td>250.0</td>\n",
              "      <td>582.0</td>\n",
              "      <td>7861.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>diabetes</th>\n",
              "      <td>299.0</td>\n",
              "      <td>0.418060</td>\n",
              "      <td>0.494067</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ejection_fraction</th>\n",
              "      <td>299.0</td>\n",
              "      <td>38.083612</td>\n",
              "      <td>11.834841</td>\n",
              "      <td>14.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>80.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>high_blood_pressure</th>\n",
              "      <td>299.0</td>\n",
              "      <td>0.351171</td>\n",
              "      <td>0.478136</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>platelets</th>\n",
              "      <td>299.0</td>\n",
              "      <td>263358.029264</td>\n",
              "      <td>97804.236869</td>\n",
              "      <td>25100.0</td>\n",
              "      <td>212500.0</td>\n",
              "      <td>262000.0</td>\n",
              "      <td>303500.0</td>\n",
              "      <td>850000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>serum_creatinine</th>\n",
              "      <td>299.0</td>\n",
              "      <td>1.393880</td>\n",
              "      <td>1.034510</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.4</td>\n",
              "      <td>9.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>serum_sodium</th>\n",
              "      <td>299.0</td>\n",
              "      <td>136.625418</td>\n",
              "      <td>4.412477</td>\n",
              "      <td>113.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>137.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>148.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>299.0</td>\n",
              "      <td>0.648829</td>\n",
              "      <td>0.478136</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoking</th>\n",
              "      <td>299.0</td>\n",
              "      <td>0.321070</td>\n",
              "      <td>0.467670</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <td>299.0</td>\n",
              "      <td>130.260870</td>\n",
              "      <td>77.614208</td>\n",
              "      <td>4.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>285.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DEATH_EVENT</th>\n",
              "      <td>299.0</td>\n",
              "      <td>0.321070</td>\n",
              "      <td>0.467670</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cac9e080-88be-420b-ae40-76b21d95a91e')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-cac9e080-88be-420b-ae40-76b21d95a91e 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-cac9e080-88be-420b-ae40-76b21d95a91e');\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",
              "\n",
              "\n",
              "<div id=\"df-9f3388c8-bca8-4498-9a98-456f39f53505\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9f3388c8-bca8-4498-9a98-456f39f53505')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9f3388c8-bca8-4498-9a98-456f39f53505 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.DEATH_EVENT.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S3-4flHcfA6k",
        "outputId": "9e958d4a-ac1d-48a1-a640-4fba5538a4b0"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"DEATH_EVENT\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ANNxr-6YfQQH",
        "outputId": "6a5942f5-1699-4efe-a12b-74ab181f2f41"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    203\n",
              "1     96\n",
              "Name: DEATH_EVENT, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"DEATH_EVENT\"].value_counts(normalize=True)\n",
        "#•\t67.89 % of the patients survived.\n",
        "#•\t32.11 % of the patients deceased.\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r80ifBDGfoem",
        "outputId": "eb816380-9835-4ac5-8dab-4e0f71b69a54"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.67893\n",
              "1    0.32107\n",
              "Name: DEATH_EVENT, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(29,13))\n",
        "df[\"DEATH_EVENT\"].value_counts().plot(kind=\"bar\",color=\"deeppink\")\n",
        "plt.ylabel(\"Number of patient\",fontsize=13)\n",
        "plt.xlabel(\"Death Event\",fontsize=13)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "bckO4Bz0f8rZ",
        "outputId": "0b5a4979-2f30-4dda-aff7-8d5f16d862bf"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Death Event')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2900x1300 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"DEATH_EVENT\"].value_counts(normalize=True).plot(kind=\"bar\",color=\"orange\",alpha=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "7FlTJqhohXe0",
        "outputId": "10b59b25-a7ed-4492-d810-6cb429f5b927"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGYCAYAAACQz+KaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAb4klEQVR4nO3df2ycd33A8Y/t1ueF1tcW03PqWbOgY1lUsIuduO5EyzQzT6s2Iu2HoduceZBprKoqTkjUY7NH2XC3ls6CBDyiRp1AUSNQx9CoDOi0TkNYMnUWoIwWMURjKHeJxbA7V7KRz/sDcZEXO/WlTr6x/XpJj0Qef5+7z0k83JvHd49rVlZWVgIAIJHa1AMAADubGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKSuSj3ARpTL5XjhhRfi2muvjZqamtTjAAAbsLKyEi+++GLcdNNNUVu7/vWPLREjL7zwQrS2tqYeAwC4CDMzM/HzP//z6/58S8TItddeGxE/fTGNjY2JpwEANmJ+fj5aW1sr7+PruagYOXLkSDz00ENRLBajvb09PvrRj8b+/fvXXPuWt7wl/v3f//28/b/5m78Zn//85zf0fD/71UxjY6MYAYAt5uU+YlH1B1hPnDgR+Xw+RkZG4uTJk9He3h59fX1x5syZNdc/8cQT8cMf/rCyPfPMM1FXVxe/93u/V+1TAwDbUNUx8sgjj8ShQ4dicHAw9u7dG+Pj47Fr1644duzYmutvuOGGaG5urmxf+tKXYteuXWIEAIiIKmNkaWkppqeno7e399wD1NZGb29vTE5ObugxHn300Xj7298er3rVq9Zds7i4GPPz86s2AGB7qipGZmdnY3l5OXK53Kr9uVwuisXiyx4/NTUVzzzzTLzrXe+64LrR0dHIZrOVzTdpAGD7uqw3PXv00UfjDW94w7ofdv2ZoaGhmJubq2wzMzOXaUIA4HKr6ts0TU1NUVdXF6VSadX+UqkUzc3NFzx2YWEhHn/88XjggQde9nkymUxkMplqRgMAtqiqrozU19dHZ2dnFAqFyr5yuRyFQiF6enoueOynP/3pWFxcjD/8wz+8uEkBgG2p6vuM5PP5OHjwYHR1dcX+/ftjbGwsFhYWYnBwMCIiBgYGoqWlJUZHR1cd9+ijj8aBAwfi1a9+9eZMDgBsC1XHSH9/f5w9ezaGh4ejWCxGR0dHTExMVD7Uevr06fPuP//cc8/Fl7/85fjiF7+4OVMDANtGzcrKykrqIV7O/Px8ZLPZmJubcwdWANgiNvr+fVm/TQMA8P+JEQAgKTECACQlRgCApKr+Ng2X2fEL/9lltpm7r/jPkwNsOldGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSFxUjR44ciba2tmhoaIju7u6Ympq64Pof//jHcc8998Tu3bsjk8nE61//+njyyScvamAAYHu5qtoDTpw4Efl8PsbHx6O7uzvGxsair68vnnvuubjxxhvPW7+0tBRvfetb48Ybb4zPfOYz0dLSEs8//3xcd911mzE/ALDF1aysrKxUc0B3d3fs27cvDh8+HBER5XI5Wltb4957743777//vPXj4+Px0EMPxbPPPhtXX331RQ05Pz8f2Ww25ubmorGx8aIeY8s6XpN6Ai6nu6s6HQGuaBt9/67q1zRLS0sxPT0dvb295x6gtjZ6e3tjcnJyzWM+97nPRU9PT9xzzz2Ry+XilltuiQ996EOxvLy87vMsLi7G/Pz8qg0A2J6qipHZ2dlYXl6OXC63an8ul4tisbjmMd/97nfjM5/5TCwvL8eTTz4Zf/VXfxUf/vCH42/+5m/WfZ7R0dHIZrOVrbW1tZoxAYAt5JJ/m6ZcLseNN94Yn/jEJ6KzszP6+/vj/e9/f4yPj697zNDQUMzNzVW2mZmZSz0mAJBIVR9gbWpqirq6uiiVSqv2l0qlaG5uXvOY3bt3x9VXXx11dXWVfb/8y78cxWIxlpaWor6+/rxjMplMZDKZakYDALaoqq6M1NfXR2dnZxQKhcq+crkchUIhenp61jzmV37lV+I73/lOlMvlyr5vf/vbsXv37jVDBADYWar+NU0+n4+jR4/GP/3TP8W3vvWtePe73x0LCwsxODgYEREDAwMxNDRUWf/ud787fvSjH8V9990X3/72t+Pzn/98fOhDH4p77rln814FALBlVX2fkf7+/jh79mwMDw9HsViMjo6OmJiYqHyo9fTp01Fbe65xWltb4wtf+EK85z3viTe+8Y3R0tIS9913X7zvfe/bvFcBAGxZVd9nJAX3GWHHcJ8RYBu5JPcZAQDYbGIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSFxUjR44ciba2tmhoaIju7u6Ymppad+1jjz0WNTU1q7aGhoaLHhgA2F6qjpETJ05EPp+PkZGROHnyZLS3t0dfX1+cOXNm3WMaGxvjhz/8YWV7/vnnX9HQAMD2UXWMPPLII3Ho0KEYHByMvXv3xvj4eOzatSuOHTu27jE1NTXR3Nxc2XK53CsaGgDYPqqKkaWlpZieno7e3t5zD1BbG729vTE5Obnucf/7v/8bv/ALvxCtra3xtre9Lb75zW9e8HkWFxdjfn5+1QYAbE9Vxcjs7GwsLy+fd2Ujl8tFsVhc85hf+qVfimPHjsW//Mu/xKc+9akol8tx++23x/e///11n2d0dDSy2Wxla21trWZMAGALueTfpunp6YmBgYHo6OiIO++8M5544ol4zWteE//4j/+47jFDQ0MxNzdX2WZmZi71mABAIldVs7ipqSnq6uqiVCqt2l8qlaK5uXlDj3H11VfHrbfeGt/5znfWXZPJZCKTyVQzGgCwRVV1ZaS+vj46OzujUChU9pXL5SgUCtHT07Ohx1heXo5vfOMbsXv37uomBQC2paqujERE5PP5OHjwYHR1dcX+/ftjbGwsFhYWYnBwMCIiBgYGoqWlJUZHRyMi4oEHHojbbrstbr755vjxj38cDz30UDz//PPxrne9a3NfCQCwJVUdI/39/XH27NkYHh6OYrEYHR0dMTExUflQ6+nTp6O29twFl//5n/+JQ4cORbFYjOuvvz46OzvjK1/5Suzdu3fzXgUAsGXVrKysrKQe4uXMz89HNpuNubm5aGxsTD3O5XW8JvUEXE53X/GnI8CGbfT929+mAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJK6qBg5cuRItLW1RUNDQ3R3d8fU1NSGjnv88cejpqYmDhw4cDFPCwBsQ1XHyIkTJyKfz8fIyEicPHky2tvbo6+vL86cOXPB4773ve/Fe9/73njzm9980cMCANtP1THyyCOPxKFDh2JwcDD27t0b4+PjsWvXrjh27Ni6xywvL8cf/MEfxAc+8IF47Wtf+4oGBgC2l6piZGlpKaanp6O3t/fcA9TWRm9vb0xOTq573AMPPBA33nhjvPOd79zQ8ywuLsb8/PyqDQDYnqqKkdnZ2VheXo5cLrdqfy6Xi2KxuOYxX/7yl+PRRx+No0ePbvh5RkdHI5vNVrbW1tZqxgQAtpBL+m2aF198Mf7oj/4ojh49Gk1NTRs+bmhoKObm5irbzMzMJZwSAEjpqmoWNzU1RV1dXZRKpVX7S6VSNDc3n7f+v//7v+N73/te/NZv/VZlX7lc/ukTX3VVPPfcc/G6173uvOMymUxkMplqRgMAtqiqrozU19dHZ2dnFAqFyr5yuRyFQiF6enrOW79nz574xje+EadOnapsv/3bvx2/+qu/GqdOnfLrFwCguisjERH5fD4OHjwYXV1dsX///hgbG4uFhYUYHByMiIiBgYFoaWmJ0dHRaGhoiFtuuWXV8dddd11ExHn7AYCdqeoY6e/vj7Nnz8bw8HAUi8Xo6OiIiYmJyodaT58+HbW1buwKAGxMzcrKykrqIV7O/Px8ZLPZmJubi8bGxtTjXF7Ha1JPwOV09xV/OgJs2Ebfv13CAACSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSV6UeAGDHOl6TegIup7tXUk9wxXJlBABISowAAEmJEQAgKTECACQlRgCApMQIAJDURcXIkSNHoq2tLRoaGqK7uzumpqbWXfvEE09EV1dXXHfddfGqV70qOjo64pOf/ORFDwwAbC9Vx8iJEycin8/HyMhInDx5Mtrb26Ovry/OnDmz5vobbrgh3v/+98fk5GR8/etfj8HBwRgcHIwvfOELr3h4AGDrq1lZWanqLizd3d2xb9++OHz4cERElMvlaG1tjXvvvTfuv//+DT3Gm970prjrrrvigx/84IbWz8/PRzabjbm5uWhsbKxm3K3PTZF2FjdF2lmc3zvLDjy/N/r+XdWVkaWlpZieno7e3t5zD1BbG729vTE5Ofmyx6+srEShUIjnnnsu7rjjjnXXLS4uxvz8/KoNANieqoqR2dnZWF5ejlwut2p/LpeLYrG47nFzc3NxzTXXRH19fdx1113x0Y9+NN761reuu350dDSy2Wxla21trWZMAGALuSzfprn22mvj1KlT8dWvfjX+9m//NvL5fDz11FPrrh8aGoq5ubnKNjMzcznGBAASqOoP5TU1NUVdXV2USqVV+0ulUjQ3N697XG1tbdx8880REdHR0RHf+ta3YnR0NN7ylresuT6TyUQmk6lmNABgi6rqykh9fX10dnZGoVCo7CuXy1EoFKKnp2fDj1Mul2NxcbGapwYAtqmqroxEROTz+Th48GB0dXXF/v37Y2xsLBYWFmJwcDAiIgYGBqKlpSVGR0cj4qef/+jq6orXve51sbi4GE8++WR88pOfjI9//OOb+0oAgC2p6hjp7++Ps2fPxvDwcBSLxejo6IiJiYnKh1pPnz4dtbXnLrgsLCzEn//5n8f3v//9+Lmf+7nYs2dPfOpTn4r+/v7NexUAwJZV9X1GUnCfEXaMHXgfgh3N+b2z7MDz+5LcZwQAYLOJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEjqomLkyJEj0dbWFg0NDdHd3R1TU1Prrj169Gi8+c1vjuuvvz6uv/766O3tveB6AGBnqTpGTpw4Efl8PkZGRuLkyZPR3t4efX19cebMmTXXP/XUU/GOd7wj/u3f/i0mJyejtbU1fv3Xfz1+8IMfvOLhAYCtr2ZlZWWlmgO6u7tj3759cfjw4YiIKJfL0draGvfee2/cf//9L3v88vJyXH/99XH48OEYGBjY0HPOz89HNpuNubm5aGxsrGbcre94TeoJuJzurup0ZKtzfu8sO/D83uj7d1VXRpaWlmJ6ejp6e3vPPUBtbfT29sbk5OSGHuOll16Kn/zkJ3HDDTdU89QAwDZ1VTWLZ2dnY3l5OXK53Kr9uVwunn322Q09xvve97646aabVgXN/7e4uBiLi4uVf8/Pz1czJgCwhVzWb9M8+OCD8fjjj8c///M/R0NDw7rrRkdHI5vNVrbW1tbLOCUAcDlVFSNNTU1RV1cXpVJp1f5SqRTNzc0XPPbhhx+OBx98ML74xS/GG9/4xguuHRoairm5uco2MzNTzZgAwBZSVYzU19dHZ2dnFAqFyr5yuRyFQiF6enrWPe7v//7v44Mf/GBMTExEV1fXyz5PJpOJxsbGVRsAsD1V9ZmRiIh8Ph8HDx6Mrq6u2L9/f4yNjcXCwkIMDg5GRMTAwEC0tLTE6OhoRET83d/9XQwPD8fx48ejra0tisViRERcc801cc0112ziSwEAtqKqY6S/vz/Onj0bw8PDUSwWo6OjIyYmJiofaj19+nTU1p674PLxj388lpaW4nd/93dXPc7IyEj89V//9SubHgDY8qq+z0gK7jPCjrED70Owozm/d5YdeH5fkvuMAABsNjECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACCpi4qRI0eORFtbWzQ0NER3d3dMTU2tu/ab3/xm/M7v/E60tbVFTU1NjI2NXeysAMA2VHWMnDhxIvL5fIyMjMTJkyejvb09+vr64syZM2uuf+mll+K1r31tPPjgg9Hc3PyKBwYAtpeqY+SRRx6JQ4cOxeDgYOzduzfGx8dj165dcezYsTXX79u3Lx566KF4+9vfHplM5hUPDABsL1XFyNLSUkxPT0dvb++5B6itjd7e3picnNz04QCA7e+qahbPzs7G8vJy5HK5VftzuVw8++yzmzbU4uJiLC4uVv49Pz+/aY8NAFxZrshv04yOjkY2m61sra2tqUcCAC6RqmKkqakp6urqolQqrdpfKpU29cOpQ0NDMTc3V9lmZmY27bEBgCtLVTFSX18fnZ2dUSgUKvvK5XIUCoXo6enZtKEymUw0Njau2gCA7amqz4xEROTz+Th48GB0dXXF/v37Y2xsLBYWFmJwcDAiIgYGBqKlpSVGR0cj4qcfev2v//qvyn/+wQ9+EKdOnYprrrkmbr755k18KQDAVlR1jPT398fZs2djeHg4isVidHR0xMTEROVDradPn47a2nMXXF544YW49dZbK/9++OGH4+GHH44777wznnrqqVf+CgCALa1mZWVlJfUQL2d+fj6y2WzMzc3tvF/ZHK9JPQGX091X/OnIZnJ+7yw78Pze6Pv3FfltGgBg5xAjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCSuqgYOXLkSLS1tUVDQ0N0d3fH1NTUBdd/+tOfjj179kRDQ0O84Q1viCeffPKihgUAtp+qY+TEiRORz+djZGQkTp48Ge3t7dHX1xdnzpxZc/1XvvKVeMc73hHvfOc74z//8z/jwIEDceDAgXjmmWde8fAAwNZXs7KyslLNAd3d3bFv3744fPhwRESUy+VobW2Ne++9N+6///7z1vf398fCwkL867/+a2XfbbfdFh0dHTE+Pr6h55yfn49sNhtzc3PR2NhYzbhb3/Ga1BNwOd1d1enIVuf83ll24Pm90ffvq6p50KWlpZieno6hoaHKvtra2ujt7Y3Jyck1j5mcnIx8Pr9qX19fX3z2s59d93kWFxdjcXGx8u+5ubmI+OmL2nFeSj0Al9VO/O/4Tub83ll24Pn9s/ftl7vuUVWMzM7OxvLycuRyuVX7c7lcPPvss2seUywW11xfLBbXfZ7R0dH4wAc+cN7+1tbWasaFredQNvUEwKWyg8/vF198MbLZ9V9/VTFyuQwNDa26mlIul+NHP/pRvPrVr46aGpc1t7v5+flobW2NmZmZnfdrOdjmnN87y8rKSrz44otx0003XXBdVTHS1NQUdXV1USqVVu0vlUrR3Ny85jHNzc1VrY+IyGQykclkVu277rrrqhmVbaCxsdH/WME25fzeOS50ReRnqvo2TX19fXR2dkahUKjsK5fLUSgUoqenZ81jenp6Vq2PiPjSl7607noAYGep+tc0+Xw+Dh48GF1dXbF///4YGxuLhYWFGBwcjIiIgYGBaGlpidHR0YiIuO++++LOO++MD3/4w3HXXXfF448/Hk8//XR84hOf2NxXAgBsSVXHSH9/f5w9ezaGh4ejWCxGR0dHTExMVD6kevr06aitPXfB5fbbb4/jx4/HX/7lX8Zf/MVfxC/+4i/GZz/72bjllls271WwrWQymRgZGTnvV3XA1uf8Zi1V32cEAGAz+ds0AEBSYgQASEqMAABJiREAICkxAgAkdUXeDp6dZXZ2No4dOxaTk5OVv1nU3Nwct99+e/zxH/9xvOY1r0k8IQCXkisjJPXVr341Xv/618dHPvKRyGazcccdd8Qdd9wR2Ww2PvKRj8SePXvi6aefTj0mcAnMzMzEn/zJn6QegyuA+4yQ1G233Rbt7e0xPj5+3h9BXFlZiT/7sz+Lr3/96zE5OZloQuBS+drXvhZvetObYnl5OfUoJObXNCT1ta99LR577LE1/xpzTU1NvOc974lbb701wWTAK/W5z33ugj//7ne/e5km4UonRkiqubk5pqamYs+ePWv+fGpqqvKnBoCt5cCBA1FTUxMXugC/1v8RYecRIyT13ve+N/70T/80pqen49d+7dcq4VEqlaJQKMTRo0fj4YcfTjwlcDF2794dH/vYx+Jtb3vbmj8/depUdHZ2XuapuBKJEZK65557oqmpKf7hH/4hPvaxj1V+d1xXVxednZ3x2GOPxe///u8nnhK4GJ2dnTE9Pb1ujLzcVRN2Dh9g5Yrxk5/8JGZnZyMioqmpKa6++urEEwGvxH/8x3/EwsJC/MZv/MaaP19YWIinn3467rzzzss8GVcaMQIAJOU+IwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCS+j+NCTl4XurYNAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#a)AGE\n",
        "df['age'].hist(bins=50,color=\"green\")\n",
        "plt.xlabel('age')\n",
        "plt.ylabel('patient')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "ZYAxur30h3Iu",
        "outputId": "2b5adb36-e81e-4949-fa85-7cad13a44c4a"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'patient')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"age\"].plot(kind=\"box\")\n",
        "plt.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "BcINggqoiR9n",
        "outputId": "7cefd386-090f-4083-f8f9-67e9ff333670"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#2B)SEX\n",
        "df[\"sex\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X5yNAvMLiwSX",
        "outputId": "a3a1aaa1-dd55-49a5-cae6-9c6c2b9c6683"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    194\n",
              "0    105\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16,12))\n",
        "df[\"sex\"].value_counts(normalize=True).plot(kind=\"bar\",title=\"sex\",color=\"yellow\",alpha=1)\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 778
        },
        "id": "6xCkMlO2i-YJ",
        "outputId": "c819c482-bccc-42b5-dfd6-36d3cee35c37"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7a56e380d810>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"sex\"].count()#missing value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eM1NiMXBjrmF",
        "outputId": "e9cccf6b-5e08-4660-ea70-b0fe97137283"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "299"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#C)Anaemia\n",
        "df[\"anaemia\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FcGPPByHjy6F",
        "outputId": "2917357b-10dd-4b38-90c6-94add71a4b56"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    170\n",
              "1    129\n",
              "Name: anaemia, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "df[\"anaemia\"].value_counts(normalize=True).plot(kind=\"bar\",color=\"red\",alpha=1,title=\"Anaemia\")\n",
        "plt.xlabel(\"Anaemia\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "kfXrQTTJkJzh",
        "outputId": "b0e3d2dc-5ba0-4194-8ddd-d44ce87d4e4d"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7a56e3aa8040>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"anaemia\"].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NS62uVdJk_LQ",
        "outputId": "a6cdeec8-40e1-420e-9c0d-dbeda9bc0328"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "299"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#D)High blood Pressure\n",
        "df[\"high_blood_pressure\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQtQHVdelE03",
        "outputId": "f2904e6e-5ee8-462e-9e1d-8af960f4939b"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    194\n",
              "1    105\n",
              "Name: high_blood_pressure, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"high_blood_pressure\"].value_counts(normalize=True).plot(kind=\"bar\",figsize=(16,13),title=\"High_Blood_Pressure\",color=\"violet\",alpha=1)\n",
        "plt.xlabel(\"High Blood Pressure\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        },
        "id": "nMsuSnLVlenk",
        "outputId": "69bb7d5b-580c-4200-c3bb-93d4e1c43415"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7a56e38776d0>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#e)Diabetes\n",
        "df['diabetes'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LC-0l-QjmwAp",
        "outputId": "207eb92f-51ea-4191-fde0-ab3d2770f37e"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    174\n",
              "1    125\n",
              "Name: diabetes, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(1)\n",
        "plt.subplot(221)\n",
        "df[\"diabetes\"].value_counts(normalize=True).plot(kind=\"bar\",figsize=(16,13),title=\"Diabetes\",color=\"indigo\",alpha=1)\n",
        "plt.xlabel(\"Diabetes\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "fdmJA6atnAQw",
        "outputId": "6d30124b-da89-49c6-e660-4e70a14e45d5"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7a56e0d75690>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"diabetes\"].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-eKIupwBn4YC",
        "outputId": "4114fe42-72bf-451d-d66b-2b97df50ac9e"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "299"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#f)smoking\n",
        "df[\"smoking\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "is2fYHQ3oBh5",
        "outputId": "0892f231-9262-46f0-a185-6c63789cd872"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    203\n",
              "1     96\n",
              "Name: smoking, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(1)\n",
        "plt.subplot(221)\n",
        "df[\"smoking\"].value_counts(normalize=True).plot(kind=\"bar\",figsize=(20,10),title=\"smoking\",color=\"purple\",alpha=1)\n",
        "plt.xlabel(\"smoking\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "NCrx0R95oU29",
        "outputId": "1729faa4-5992-46ee-e741-b9d6724acb93"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7a56e0de0e50>"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"smoking\"].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hwc97uZaqM2B",
        "outputId": "a62803f7-c91c-45ca-d88e-77571d2b7992"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "299"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    }
  ]
}