Stroke_Prediction_1.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
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      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/healthcare-dataset-stroke-data.csv')\n",
        "data.head(20)"
      ],
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              "       id  gender   age  hypertension  heart_disease ever_married  \\\n",
              "0    9046    Male  67.0             0              1          Yes   \n",
              "1   51676  Female  61.0             0              0          Yes   \n",
              "2   31112    Male  80.0             0              1          Yes   \n",
              "3   60182  Female  49.0             0              0          Yes   \n",
              "4    1665  Female  79.0             1              0          Yes   \n",
              "5   56669    Male  81.0             0              0          Yes   \n",
              "6   53882    Male  74.0             1              1          Yes   \n",
              "7   10434  Female  69.0             0              0           No   \n",
              "8   27419  Female  59.0             0              0          Yes   \n",
              "9   60491  Female  78.0             0              0          Yes   \n",
              "10  12109  Female  81.0             1              0          Yes   \n",
              "11  12095  Female  61.0             0              1          Yes   \n",
              "12  12175  Female  54.0             0              0          Yes   \n",
              "13   8213    Male  78.0             0              1          Yes   \n",
              "14   5317  Female  79.0             0              1          Yes   \n",
              "15  58202  Female  50.0             1              0          Yes   \n",
              "16  56112    Male  64.0             0              1          Yes   \n",
              "17  34120    Male  75.0             1              0          Yes   \n",
              "18  27458  Female  60.0             0              0           No   \n",
              "19  25226    Male  57.0             0              1           No   \n",
              "\n",
              "        work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
              "0         Private          Urban             228.69  36.6  formerly smoked   \n",
              "1   Self-employed          Rural             202.21   NaN     never smoked   \n",
              "2         Private          Rural             105.92  32.5     never smoked   \n",
              "3         Private          Urban             171.23  34.4           smokes   \n",
              "4   Self-employed          Rural             174.12  24.0     never smoked   \n",
              "5         Private          Urban             186.21  29.0  formerly smoked   \n",
              "6         Private          Rural              70.09  27.4     never smoked   \n",
              "7         Private          Urban              94.39  22.8     never smoked   \n",
              "8         Private          Rural              76.15   NaN          Unknown   \n",
              "9         Private          Urban              58.57  24.2          Unknown   \n",
              "10        Private          Rural              80.43  29.7     never smoked   \n",
              "11       Govt_job          Rural             120.46  36.8           smokes   \n",
              "12        Private          Urban             104.51  27.3           smokes   \n",
              "13        Private          Urban             219.84   NaN          Unknown   \n",
              "14        Private          Urban             214.09  28.2     never smoked   \n",
              "15  Self-employed          Rural             167.41  30.9     never smoked   \n",
              "16        Private          Urban             191.61  37.5           smokes   \n",
              "17        Private          Urban             221.29  25.8           smokes   \n",
              "18        Private          Urban              89.22  37.8     never smoked   \n",
              "19       Govt_job          Urban             217.08   NaN          Unknown   \n",
              "\n",
              "    stroke  \n",
              "0        1  \n",
              "1        1  \n",
              "2        1  \n",
              "3        1  \n",
              "4        1  \n",
              "5        1  \n",
              "6        1  \n",
              "7        1  \n",
              "8        1  \n",
              "9        1  \n",
              "10       1  \n",
              "11       1  \n",
              "12       1  \n",
              "13       1  \n",
              "14       1  \n",
              "15       1  \n",
              "16       1  \n",
              "17       1  \n",
              "18       1  \n",
              "19       1  "
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              "      <td>67.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>105.92</td>\n",
              "      <td>32.5</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
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              "      <td>49.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>171.23</td>\n",
              "      <td>34.4</td>\n",
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              "      <th>4</th>\n",
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              "      <td>79.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>174.12</td>\n",
              "      <td>24.0</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
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              "      <th>5</th>\n",
              "      <td>56669</td>\n",
              "      <td>Male</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>186.21</td>\n",
              "      <td>29.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>69.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>94.39</td>\n",
              "      <td>22.8</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
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              "      <th>8</th>\n",
              "      <td>27419</td>\n",
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              "      <td>59.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>76.15</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>1</td>\n",
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              "      <th>9</th>\n",
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              "      <td>1</td>\n",
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              "      <th>10</th>\n",
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              "      <td>81.0</td>\n",
              "      <td>1</td>\n",
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              "      <td>29.7</td>\n",
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              "      <td>1</td>\n",
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              "      <td>54.0</td>\n",
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              "      <td>104.51</td>\n",
              "      <td>27.3</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>Urban</td>\n",
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              "      <td>30.9</td>\n",
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              "      <td>1</td>\n",
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              "      <td>64.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>191.61</td>\n",
              "      <td>37.5</td>\n",
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              "      <td>1</td>\n",
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              "      <th>17</th>\n",
              "      <td>34120</td>\n",
              "      <td>Male</td>\n",
              "      <td>75.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>221.29</td>\n",
              "      <td>25.8</td>\n",
              "      <td>smokes</td>\n",
              "      <td>1</td>\n",
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              "      <th>18</th>\n",
              "      <td>27458</td>\n",
              "      <td>Female</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>89.22</td>\n",
              "      <td>37.8</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
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              "      <th>19</th>\n",
              "      <td>25226</td>\n",
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              "      <td>57.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>Govt_job</td>\n",
              "      <td>Urban</td>\n",
              "      <td>217.08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>1</td>\n",
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        "id": "QdXAjQIGtpYU",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5110 entries, 0 to 5109\n",
            "Data columns (total 12 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   id                 5110 non-null   int64  \n",
            " 1   gender             5110 non-null   object \n",
            " 2   age                5110 non-null   float64\n",
            " 3   hypertension       5110 non-null   int64  \n",
            " 4   heart_disease      5110 non-null   int64  \n",
            " 5   ever_married       5110 non-null   object \n",
            " 6   work_type          5110 non-null   object \n",
            " 7   Residence_type     5110 non-null   object \n",
            " 8   avg_glucose_level  5110 non-null   float64\n",
            " 9   bmi                4909 non-null   float64\n",
            " 10  smoking_status     5110 non-null   object \n",
            " 11  stroke             5110 non-null   int64  \n",
            "dtypes: float64(3), int64(4), object(5)\n",
            "memory usage: 479.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()\n",
        "#some values are missing for bmi\n",
        "#age values has decimals\n",
        "#we need to do some data preprocessing"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
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        "id": "wHlgZCzQtsqZ",
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                 id          age  hypertension  heart_disease  \\\n",
              "count   5110.000000  5110.000000   5110.000000    5110.000000   \n",
              "mean   36517.829354    43.226614      0.097456       0.054012   \n",
              "std    21161.721625    22.612647      0.296607       0.226063   \n",
              "min       67.000000     0.080000      0.000000       0.000000   \n",
              "25%    17741.250000    25.000000      0.000000       0.000000   \n",
              "50%    36932.000000    45.000000      0.000000       0.000000   \n",
              "75%    54682.000000    61.000000      0.000000       0.000000   \n",
              "max    72940.000000    82.000000      1.000000       1.000000   \n",
              "\n",
              "       avg_glucose_level          bmi       stroke  \n",
              "count        5110.000000  4909.000000  5110.000000  \n",
              "mean          106.147677    28.893237     0.048728  \n",
              "std            45.283560     7.854067     0.215320  \n",
              "min            55.120000    10.300000     0.000000  \n",
              "25%            77.245000    23.500000     0.000000  \n",
              "50%            91.885000    28.100000     0.000000  \n",
              "75%           114.090000    33.100000     0.000000  \n",
              "max           271.740000    97.600000     1.000000  "
            ],
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              "      <td>5110.000000</td>\n",
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              "      <td>4909.000000</td>\n",
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              "      <td>43.226614</td>\n",
              "      <td>0.097456</td>\n",
              "      <td>0.054012</td>\n",
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              "      <td>10.300000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>17741.250000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>77.245000</td>\n",
              "      <td>23.500000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>36932.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>91.885000</td>\n",
              "      <td>28.100000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>54682.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>114.090000</td>\n",
              "      <td>33.100000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>72940.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>271.740000</td>\n",
              "      <td>97.600000</td>\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_value = data['bmi'].mean()\n",
        "data['bmi'].fillna(value = mean_value, inplace = True)"
      ],
      "metadata": {
        "id": "eFbW8uwAtvnR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['age'] = (data['age'].apply(np.ceil)).astype('int64')"
      ],
      "metadata": {
        "id": "j90UxnAityYu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop(data[data.gender == 'Other'].index, inplace = True)"
      ],
      "metadata": {
        "id": "U4EiV-QUt0sM"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "data.drop(data[data.smoking_status == 'Unknown'].index, inplace = True)"
      ],
      "metadata": {
        "id": "phwCrlWVuD4V"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
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        "id": "c7GIzrRfuUwt",
        "outputId": "4dbef468-c5f1-47a0-a8f1-5d19a6d9dca8"
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      "execution_count": null,
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          "data": {
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              "         id  gender  age  hypertension  heart_disease  ever_married  \\\n",
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              "\n",
              "      work_type  Residence_type  avg_glucose_level        bmi  smoking_status  \\\n",
              "5074          0               1             104.00  25.600000               2   \n",
              "5076          2               0             174.37  23.000000               1   \n",
              "5080          3               1              90.52  28.000000               1   \n",
              "5081          0               0             108.35  40.800000               0   \n",
              "5082          2               0              72.03  37.500000               2   \n",
              "5084          2               0              97.04  26.900000               2   \n",
              "5085          2               1              83.89  33.100000               0   \n",
              "5087          3               0              75.93  34.700000               0   \n",
              "5090          0               0             100.85  21.000000               2   \n",
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              "5093          0               0              95.02  28.893237               2   \n",
              "5096          0               0              76.62  28.200000               1   \n",
              "5099          2               0              83.94  28.893237               2   \n",
              "5100          3               0              71.97  28.300000               1   \n",
              "5102          2               0              77.93  21.700000               1   \n",
              "5105          2               1              83.75  28.893237               1   \n",
              "5106          3               1             125.20  40.000000               1   \n",
              "5107          3               0              82.99  30.600000               1   \n",
              "5108          2               0             166.29  25.600000               0   \n",
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              "      <td>33.100000</td>\n",
              "      <td>0</td>\n",
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              "      <td>21.000000</td>\n",
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              "      <th>5091</th>\n",
              "      <td>6369</td>\n",
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              "      <td>59</td>\n",
              "      <td>1</td>\n",
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              "      <td>56799</td>\n",
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              "      <td>76</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <th>5093</th>\n",
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              "      <td>0</td>\n",
              "      <td>95.02</td>\n",
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              "      <td>2</td>\n",
              "      <td>0</td>\n",
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              "      <th>5096</th>\n",
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              "      <td>166.29</td>\n",
              "      <td>25.600000</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JN1n8DE8uYtU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "data['gender'] = data[['gender']].apply(le.fit_transform)\n",
        "data['ever_married'] = data[['ever_married']].apply(le.fit_transform)\n",
        "data ['Residence_type'] = data[['Residence_type']].apply(le.fit_transform)\n",
        "data['work_type'] = data[['work_type']].apply(le.fit_transform)\n",
        "data ['smoking_status'] = data[['smoking_status']].apply(le.fit_transform)"
      ],
      "metadata": {
        "id": "E8xynd9WuGHU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.tail(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 720
        },
        "id": "1SWacu9suI1l",
        "outputId": "7ea84a09-60ec-4b4b-c434-59b024f467c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id  gender  age  hypertension  heart_disease  ever_married  \\\n",
              "5074  30379       0   52             0              0             1   \n",
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              "\n",
              "      work_type  Residence_type  avg_glucose_level        bmi  smoking_status  \\\n",
              "5074          0               1             104.00  25.600000               2   \n",
              "5076          2               0             174.37  23.000000               1   \n",
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              "5105          2               1              83.75  28.893237               1   \n",
              "5106          3               1             125.20  40.000000               1   \n",
              "5107          3               0              82.99  30.600000               1   \n",
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              "      <td>83.89</td>\n",
              "      <td>33.100000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5087</th>\n",
              "      <td>26214</td>\n",
              "      <td>0</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>75.93</td>\n",
              "      <td>34.700000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5090</th>\n",
              "      <td>4211</td>\n",
              "      <td>1</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100.85</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5091</th>\n",
              "      <td>6369</td>\n",
              "      <td>1</td>\n",
              "      <td>59</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>95.05</td>\n",
              "      <td>30.900000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>5092</th>\n",
              "      <td>56799</td>\n",
              "      <td>1</td>\n",
              "      <td>76</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>82.35</td>\n",
              "      <td>38.900000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>5093</th>\n",
              "      <td>32235</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>95.02</td>\n",
              "      <td>28.893237</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>5096</th>\n",
              "      <td>41512</td>\n",
              "      <td>1</td>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>76.62</td>\n",
              "      <td>28.200000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>5099</th>\n",
              "      <td>7293</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>83.94</td>\n",
              "      <td>28.893237</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5100</th>\n",
              "      <td>68398</td>\n",
              "      <td>1</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>71.97</td>\n",
              "      <td>28.300000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5102</th>\n",
              "      <td>45010</td>\n",
              "      <td>0</td>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>77.93</td>\n",
              "      <td>21.700000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5105</th>\n",
              "      <td>18234</td>\n",
              "      <td>0</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>83.75</td>\n",
              "      <td>28.893237</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5106</th>\n",
              "      <td>44873</td>\n",
              "      <td>0</td>\n",
              "      <td>81</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>125.20</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5107</th>\n",
              "      <td>19723</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>82.99</td>\n",
              "      <td>30.600000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5108</th>\n",
              "      <td>37544</td>\n",
              "      <td>1</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>166.29</td>\n",
              "      <td>25.600000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f4d70bb2-cfc3-492d-9b1e-99133134546c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-f4d70bb2-cfc3-492d-9b1e-99133134546c');\n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "rRCgGNPzufsF",
        "outputId": "7bf70baa-20c6-4bde-bd44-fc22fd3b2f5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 id       gender          age  hypertension  heart_disease  \\\n",
              "count   3565.000000  3565.000000  3565.000000   3565.000000    3565.000000   \n",
              "mean   36780.317251     0.394670    48.860309      0.125105       0.063955   \n",
              "std    21240.500438     0.488848    18.873140      0.330885       0.244707   \n",
              "min       67.000000     0.000000    10.000000      0.000000       0.000000   \n",
              "25%    18040.000000     0.000000    34.000000      0.000000       0.000000   \n",
              "50%    37446.000000     0.000000    50.000000      0.000000       0.000000   \n",
              "75%    54946.000000     1.000000    63.000000      0.000000       0.000000   \n",
              "max    72915.000000     1.000000    82.000000      1.000000       1.000000   \n",
              "\n",
              "       ever_married    work_type  Residence_type  avg_glucose_level  \\\n",
              "count   3565.000000  3565.000000     3565.000000        3565.000000   \n",
              "mean       0.760168     1.920617        0.508836         108.972376   \n",
              "std        0.427041     0.928193        0.499992          48.295550   \n",
              "min        0.000000     0.000000        0.000000          55.120000   \n",
              "25%        1.000000     2.000000        0.000000          77.460000   \n",
              "50%        1.000000     2.000000        1.000000          92.650000   \n",
              "75%        1.000000     2.000000        1.000000         116.670000   \n",
              "max        1.000000     4.000000        1.000000         271.740000   \n",
              "\n",
              "               bmi  smoking_status       stroke  \n",
              "count  3565.000000     3565.000000  3565.000000  \n",
              "mean     30.237406        0.973352     0.056662  \n",
              "std       7.156210        0.684621     0.231228  \n",
              "min      11.500000        0.000000     0.000000  \n",
              "25%      25.400000        1.000000     0.000000  \n",
              "50%      28.893237        1.000000     0.000000  \n",
              "75%      33.800000        1.000000     0.000000  \n",
              "max      92.000000        2.000000     1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7d25d8ea-fabe-4dde-9a93-70699df33a1e\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>work_type</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>smoking_status</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3565.000000</td>\n",
              "      <td>3565.000000</td>\n",
              "      <td>3565.000000</td>\n",
              "      <td>3565.000000</td>\n",
              "      <td>3565.000000</td>\n",
              "      <td>3565.000000</td>\n",
              "      <td>3565.000000</td>\n",
              "      <td>3565.000000</td>\n",
              "      <td>3565.000000</td>\n",
              "      <td>3565.000000</td>\n",
              "      <td>3565.000000</td>\n",
              "      <td>3565.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>36780.317251</td>\n",
              "      <td>0.394670</td>\n",
              "      <td>48.860309</td>\n",
              "      <td>0.125105</td>\n",
              "      <td>0.063955</td>\n",
              "      <td>0.760168</td>\n",
              "      <td>1.920617</td>\n",
              "      <td>0.508836</td>\n",
              "      <td>108.972376</td>\n",
              "      <td>30.237406</td>\n",
              "      <td>0.973352</td>\n",
              "      <td>0.056662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>21240.500438</td>\n",
              "      <td>0.488848</td>\n",
              "      <td>18.873140</td>\n",
              "      <td>0.330885</td>\n",
              "      <td>0.244707</td>\n",
              "      <td>0.427041</td>\n",
              "      <td>0.928193</td>\n",
              "      <td>0.499992</td>\n",
              "      <td>48.295550</td>\n",
              "      <td>7.156210</td>\n",
              "      <td>0.684621</td>\n",
              "      <td>0.231228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>67.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>55.120000</td>\n",
              "      <td>11.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>18040.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>77.460000</td>\n",
              "      <td>25.400000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>37446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>92.650000</td>\n",
              "      <td>28.893237</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>54946.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>116.670000</td>\n",
              "      <td>33.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>72915.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>271.740000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "       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-7d25d8ea-fabe-4dde-9a93-70699df33a1e 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-7d25d8ea-fabe-4dde-9a93-70699df33a1e');\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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Split the data into those with stroke and without stroke\n",
        "no_stroke = data[data['stroke'] == 0] #extract the information of those without stroke\n",
        "no_stroke_extract = no_stroke['stroke'] #extract one column\n",
        "\n",
        "yes_stroke = data[data['stroke'] == 1] #extract the information of those with stroke\n",
        "yes_stroke_extract = yes_stroke['stroke'] #extract one column\n",
        "\n",
        "count = [yes_stroke_extract.count(), no_stroke_extract.count()]\n",
        "labels = ('stoke', 'no stroke')\n",
        "\n",
        "#plot stroke vs no stroke observations\n",
        "plt.bar(labels, count)\n",
        "print (\"number with stroke: \",yes_stroke_extract.count())\n",
        "print (\"number without stroke: \",no_stroke_extract.count())\n",
        "print (\"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "aQwi7bU2ujvU",
        "outputId": "cf7dac04-263d-4224-bf60-75d1ed0c29cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number with stroke:  202\n",
            "number without stroke:  3363\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#same plot as above: Using seaborn to visualize stroke vs no stroke observations\n",
        "import seaborn as sns\n",
        "g = sns.countplot(data['stroke'])\n",
        "g.set_xticklabels(['no stroke','stroke'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 776
        },
        "id": "S47P_3cGuoh6",
        "outputId": "ef043936-e25c-432f-df8c-dee015738128"
      },
      "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-15-052163907844>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'stroke'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xticklabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'no stroke'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'stroke'\u001b[0m\u001b[0;34m]\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      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\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/python3.10/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mget_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__module__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mowner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__module__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/_api/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    295\u001b[0m                 f\"for the old name will be dropped %(removal)s.\")\n\u001b[1;32m    296\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 297\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m     \u001b[0;31m# wrapper() must keep the same documented signature as func(): if we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mset_ticklabels\u001b[0;34m(self, labels, minor, fontdict, **kwargs)\u001b[0m\n\u001b[1;32m   1967\u001b[0m             \u001b[0;31m# remove all tick labels, so only error for > 0 labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1968\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1969\u001b[0;31m                 raise ValueError(\n\u001b[0m\u001b[1;32m   1970\u001b[0m                     \u001b[0;34m\"The number of FixedLocator locations\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1971\u001b[0m                     \u001b[0;34mf\" ({len(locator.locs)}), usually from a call to\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: The number of FixedLocator locations (1), usually from a call to set_ticks, does not match the number of labels (2)."
          ]
        },
        {
          "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": [
        "#same plot as above: Using seaborn to visualize stroke vs no stroke observations\n",
        "import seaborn as sns\n",
        "g = sns.countplot(data['stroke'])\n",
        "g.set_xticklabels(['no stroke','stroke'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 776
        },
        "id": "IhTVcEt9vsXW",
        "outputId": "85caa246-ef96-462e-f5ff-54bc5371a69a"
      },
      "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-17-052163907844>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'stroke'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xticklabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'no stroke'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'stroke'\u001b[0m\u001b[0;34m]\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      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\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/python3.10/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mget_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__module__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mowner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__module__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/_api/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    295\u001b[0m                 f\"for the old name will be dropped %(removal)s.\")\n\u001b[1;32m    296\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 297\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m     \u001b[0;31m# wrapper() must keep the same documented signature as func(): if we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mset_ticklabels\u001b[0;34m(self, labels, minor, fontdict, **kwargs)\u001b[0m\n\u001b[1;32m   1967\u001b[0m             \u001b[0;31m# remove all tick labels, so only error for > 0 labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1968\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1969\u001b[0;31m                 raise ValueError(\n\u001b[0m\u001b[1;32m   1970\u001b[0m                     \u001b[0;34m\"The number of FixedLocator locations\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1971\u001b[0m                     \u001b[0;34mf\" ({len(locator.locs)}), usually from a call to\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: The number of FixedLocator locations (1), usually from a call to set_ticks, does not match the number of labels (2)."
          ]
        },
        {
          "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": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "g = sns.countplot(data['stroke'])\n",
        "g.set_xticklabels(['No Stroke', 'Stroke'])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 776
        },
        "id": "oDeIvjTV1jkG",
        "outputId": "afc6b124-8f8c-4798-b9bf-ab2d82a1c6a2"
      },
      "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-21-6e8d6f25ea8a>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'stroke'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xticklabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'No Stroke'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Stroke'\u001b[0m\u001b[0;34m]\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      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\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/python3.10/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mget_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__module__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mowner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__module__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/_api/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    295\u001b[0m                 f\"for the old name will be dropped %(removal)s.\")\n\u001b[1;32m    296\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 297\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m     \u001b[0;31m# wrapper() must keep the same documented signature as func(): if we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mset_ticklabels\u001b[0;34m(self, labels, minor, fontdict, **kwargs)\u001b[0m\n\u001b[1;32m   1967\u001b[0m             \u001b[0;31m# remove all tick labels, so only error for > 0 labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1968\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1969\u001b[0;31m                 raise ValueError(\n\u001b[0m\u001b[1;32m   1970\u001b[0m                     \u001b[0;34m\"The number of FixedLocator locations\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1971\u001b[0m                     \u001b[0;34mf\" ({len(locator.locs)}), usually from a call to\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: The number of FixedLocator locations (1), usually from a call to set_ticks, does not match the number of labels (2)."
          ]
        },
        {
          "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": [
        "yes_stroke.describe() #observations with stroke\n",
        "#minimum age is 32"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "CrLxyror1Apw",
        "outputId": "3820c040-d199-4704-bb11-f1f3a7311a78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 id      gender         age  hypertension  heart_disease  \\\n",
              "count    202.000000  202.000000  202.000000    202.000000     202.000000   \n",
              "mean   36561.559406    0.425743   68.168317      0.306931       0.193069   \n",
              "std    22107.825562    0.495684   11.768555      0.462366       0.395688   \n",
              "min      210.000000    0.000000   32.000000      0.000000       0.000000   \n",
              "25%    16646.750000    0.000000   59.000000      0.000000       0.000000   \n",
              "50%    36522.000000    0.000000   71.000000      0.000000       0.000000   \n",
              "75%    56437.500000    1.000000   78.000000      1.000000       0.000000   \n",
              "max    72081.000000    1.000000   82.000000      1.000000       1.000000   \n",
              "\n",
              "       ever_married   work_type  Residence_type  avg_glucose_level  \\\n",
              "count    202.000000  202.000000      202.000000         202.000000   \n",
              "mean       0.896040    2.034653        0.524752         134.344653   \n",
              "std        0.305967    0.883113        0.500628          62.993827   \n",
              "min        0.000000    0.000000        0.000000          56.110000   \n",
              "25%        1.000000    2.000000        0.000000          79.875000   \n",
              "50%        1.000000    2.000000        1.000000         106.575000   \n",
              "75%        1.000000    3.000000        1.000000         196.867500   \n",
              "max        1.000000    3.000000        1.000000         271.740000   \n",
              "\n",
              "              bmi  smoking_status  stroke  \n",
              "count  202.000000      202.000000   202.0  \n",
              "mean    30.460155        0.861386     1.0  \n",
              "std      5.982315        0.733420     0.0  \n",
              "min     16.900000        0.000000     1.0  \n",
              "25%     27.025000        0.000000     1.0  \n",
              "50%     28.896618        1.000000     1.0  \n",
              "75%     32.975000        1.000000     1.0  \n",
              "max     56.600000        2.000000     1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8d1dbfed-086c-44f1-b3fc-76a47f3d53fc\">\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>id</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>work_type</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>smoking_status</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>202.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>202.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>36561.559406</td>\n",
              "      <td>0.425743</td>\n",
              "      <td>68.168317</td>\n",
              "      <td>0.306931</td>\n",
              "      <td>0.193069</td>\n",
              "      <td>0.896040</td>\n",
              "      <td>2.034653</td>\n",
              "      <td>0.524752</td>\n",
              "      <td>134.344653</td>\n",
              "      <td>30.460155</td>\n",
              "      <td>0.861386</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>22107.825562</td>\n",
              "      <td>0.495684</td>\n",
              "      <td>11.768555</td>\n",
              "      <td>0.462366</td>\n",
              "      <td>0.395688</td>\n",
              "      <td>0.305967</td>\n",
              "      <td>0.883113</td>\n",
              "      <td>0.500628</td>\n",
              "      <td>62.993827</td>\n",
              "      <td>5.982315</td>\n",
              "      <td>0.733420</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>210.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>56.110000</td>\n",
              "      <td>16.900000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>16646.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>79.875000</td>\n",
              "      <td>27.025000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>36522.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>106.575000</td>\n",
              "      <td>28.896618</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>56437.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>196.867500</td>\n",
              "      <td>32.975000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>72081.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>271.740000</td>\n",
              "      <td>56.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.0</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "no_stroke.describe() #observations without stroke"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "UlAwg4AZ1Gyl",
        "outputId": "83c334fe-e756-4322-a09e-a876ad01ed31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 id       gender          age  hypertension  heart_disease  \\\n",
              "count   3363.000000  3363.000000  3363.000000   3363.000000    3363.000000   \n",
              "mean   36793.457032     0.392804    47.700565      0.114184       0.056200   \n",
              "std    21190.616570     0.488446    18.589570      0.318082       0.230341   \n",
              "min       67.000000     0.000000    10.000000      0.000000       0.000000   \n",
              "25%    18160.500000     0.000000    33.000000      0.000000       0.000000   \n",
              "50%    37507.000000     0.000000    48.000000      0.000000       0.000000   \n",
              "75%    54863.500000     1.000000    62.000000      0.000000       0.000000   \n",
              "max    72915.000000     1.000000    82.000000      1.000000       1.000000   \n",
              "\n",
              "       ever_married    work_type  Residence_type  avg_glucose_level  \\\n",
              "count   3363.000000  3363.000000     3363.000000        3363.000000   \n",
              "mean       0.752007     1.913767        0.507880         107.448379   \n",
              "std        0.431912     0.930511        0.500012          46.843959   \n",
              "min        0.000000     0.000000        0.000000          55.120000   \n",
              "25%        1.000000     2.000000        0.000000          77.325000   \n",
              "50%        1.000000     2.000000        1.000000          92.060000   \n",
              "75%        1.000000     2.000000        1.000000         114.520000   \n",
              "max        1.000000     4.000000        1.000000         267.760000   \n",
              "\n",
              "               bmi  smoking_status  stroke  \n",
              "count  3363.000000     3363.000000  3363.0  \n",
              "mean     30.224027        0.980077     0.0  \n",
              "std       7.221185        0.681109     0.0  \n",
              "min      11.500000        0.000000     0.0  \n",
              "25%      25.300000        1.000000     0.0  \n",
              "50%      28.893237        1.000000     0.0  \n",
              "75%      33.900000        1.000000     0.0  \n",
              "max      92.000000        2.000000     0.0  "
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>id</th>\n",
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              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
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              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
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              "      <th>stroke</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3363.000000</td>\n",
              "      <td>3363.000000</td>\n",
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              "      <td>3363.000000</td>\n",
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              "      <td>3363.000000</td>\n",
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              "      <td>3363.000000</td>\n",
              "      <td>3363.0</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>36793.457032</td>\n",
              "      <td>0.392804</td>\n",
              "      <td>47.700565</td>\n",
              "      <td>0.114184</td>\n",
              "      <td>0.056200</td>\n",
              "      <td>0.752007</td>\n",
              "      <td>1.913767</td>\n",
              "      <td>0.507880</td>\n",
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              "      <td>0.980077</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>std</th>\n",
              "      <td>21190.616570</td>\n",
              "      <td>0.488446</td>\n",
              "      <td>18.589570</td>\n",
              "      <td>0.318082</td>\n",
              "      <td>0.230341</td>\n",
              "      <td>0.431912</td>\n",
              "      <td>0.930511</td>\n",
              "      <td>0.500012</td>\n",
              "      <td>46.843959</td>\n",
              "      <td>7.221185</td>\n",
              "      <td>0.681109</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>67.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>55.120000</td>\n",
              "      <td>11.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>18160.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>77.325000</td>\n",
              "      <td>25.300000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>37507.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>92.060000</td>\n",
              "      <td>28.893237</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>54863.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>114.520000</td>\n",
              "      <td>33.900000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>72915.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>267.760000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.0</td>\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-e05d7b8c-ee6e-4e16-9901-be13c71185f2');\n",
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              "            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",
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            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Stroke chances increases with age, starting from age 32\n",
        "sns.lineplot(data=data, x=\"stroke\", y=\"age\")\n",
        "sns.catplot(data=data, kind = \"swarm\", x=\"stroke\", y=\"age\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        },
        "id": "hinztgYf1JIQ",
        "outputId": "f888dec3-ac2d-4a42-9200-90849ee9ee16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f3ce02ea3b0>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#The dataset have a good gender distribution\n",
        "sns.catplot(data=data, kind=\"bar\", x=\"gender\", y=\"stroke\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "5OJwPT2c1LrP",
        "outputId": "ac39a25d-5994-4835-eafb-b60d518973b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f3ce0255f90>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#bmi, stroke\n",
        "#plot shows that people with stroke have higher bmi values\n",
        "\n",
        "sns.lineplot(data=data, x=\"stroke\", y=\"bmi\", hue=\"stroke\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "902z6JDe1zGB",
        "outputId": "3d3c076d-4717-4af1-c6ea-f9030709d555"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='stroke', ylabel='bmi'>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "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": [
        "#line plot for stroke, bmi vs heart diesase\n",
        "sns.lineplot(data=data, x=\"bmi\", y=\"heart_disease\", hue='stroke')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "GpjxIBVi13KS",
        "outputId": "85aa466d-d5c5-4008-8d9c-30c625da62e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='bmi', ylabel='heart_disease'>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "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": [
        "#hypertension and heart disease increases the risk of an individual having a stroke\n",
        "sns.catplot(data=data, kind=\"bar\", x=\"stroke\", y=\"heart_disease\", hue=\"stroke\") #heart disease vs stroke\n",
        "sns.catplot(data=data, kind=\"bar\", x=\"stroke\", y=\"hypertension\", hue=\"stroke\") #hypertension vs stroke\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8cNlruu81-ul",
        "outputId": "05c611b9-c47d-4c18-ce67-8e5d6dc7e67f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f3cdff7f970>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "69sk2CUk2CM1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}