DataMiningProject_Gaussian_Naive_Bayes .ipynb

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        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split #splitting the data \n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "\n"
      ]
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              "         id  gender   age  hypertension  heart_disease ever_married  \\\n",
              "0     28932  Female  36.0             0              0          Yes   \n",
              "1     37150  Female  34.0             0              0          Yes   \n",
              "2     71669    Male  60.0             0              0          Yes   \n",
              "3     27153  Female  75.0             0              0          Yes   \n",
              "4     58235    Male  76.0             0              0          Yes   \n",
              "...     ...     ...   ...           ...            ...          ...   \n",
              "4083  30457  Female  53.0             1              0          Yes   \n",
              "4084  24836  Female  61.0             0              0          Yes   \n",
              "4085  17079    Male  44.0             0              0          Yes   \n",
              "4086  72340    Male  21.0             0              0           No   \n",
              "4087  52242  Female  58.0             1              0          Yes   \n",
              "\n",
              "          work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
              "0           Private          Rural              67.29  36.7  formerly smoked   \n",
              "1           Private          Rural              83.53  48.5  formerly smoked   \n",
              "2           Private          Rural              65.16  30.8     never smoked   \n",
              "3     Self-employed          Rural              78.80  29.3  formerly smoked   \n",
              "4           Private          Urban              58.65  25.6           smokes   \n",
              "...             ...            ...                ...   ...              ...   \n",
              "4083       Govt_job          Rural              98.61  38.8           smokes   \n",
              "4084        Private          Rural              72.01  26.0  formerly smoked   \n",
              "4085        Private          Rural              94.71  28.4           smokes   \n",
              "4086        Private          Urban             120.94  29.7  formerly smoked   \n",
              "4087       Govt_job          Rural              59.52  33.2     never smoked   \n",
              "\n",
              "      stroke  \n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4088 entries, 0 to 4087\n",
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            " #   Column             Non-Null Count  Dtype  \n",
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            " 11  stroke             4088 non-null   int64  \n",
            "dtypes: float64(3), int64(4), object(5)\n",
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              "                 id          age  hypertension  heart_disease  \\\n",
              "count   4088.000000  4088.000000   4088.000000    4088.000000   \n",
              "mean   36507.843689    43.438865      0.099315       0.054305   \n",
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              "min       67.000000     0.080000      0.000000       0.000000   \n",
              "25%    17866.750000    25.000000      0.000000       0.000000   \n",
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              "       avg_glucose_level         bmi       stroke  \n",
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              "mean          106.255262    28.90527     0.047701  \n",
              "std            45.301823     7.99228     0.213158  \n",
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              "25%            77.197500    23.50000     0.000000  \n",
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              "      <td>45.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>92.040000</td>\n",
              "      <td>28.00000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>54752.500000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>114.165000</td>\n",
              "      <td>33.10000</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>97.60000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f50980c6-4b34-4bbe-9ac6-fbb341cf7cdb')\"\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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              "  "
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          },
          "metadata": {},
          "execution_count": 6
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      ],
      "source": [
        "stroke.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PFtyavUfq3JX",
        "outputId": "cef1b853-2e08-4370-b0d6-858f814e2d31"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                     0\n",
              "gender                 0\n",
              "age                    0\n",
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              "bmi                  160\n",
              "smoking_status         0\n",
              "stroke                 0\n",
              "dtype: int64"
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          "metadata": {},
          "execution_count": 7
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        "stroke.isna().sum()"
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    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "eAPgVDrDq3DL"
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      "outputs": [],
      "source": [
        "stroke.dropna(inplace=True)"
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    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "02bf3b39-4f87-4f96-f5b9-56ccdd69bc8a"
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      "outputs": [
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              "id                   0\n",
              "gender               0\n",
              "age                  0\n",
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              "bmi                  0\n",
              "smoking_status       0\n",
              "stroke               0\n",
              "dtype: int64"
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          "metadata": {},
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      "execution_count": 10,
      "metadata": {
        "colab": {
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        },
        "id": "epPnPef8q29J",
        "outputId": "9b936252-9860-466e-dc0c-267b0452a358"
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              "      id  gender   age  hypertension  heart_disease ever_married  \\\n",
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              "3  27153  Female  75.0             0              0          Yes   \n",
              "4  58235    Male  76.0             0              0          Yes   \n",
              "\n",
              "       work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\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",
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    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "nboY_hQXBbW5"
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        "id = stroke['id']"
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      "execution_count": 12,
      "metadata": {
        "id": "eAqOn1eoq26a"
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          },
          "metadata": {},
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      ],
      "source": [
        "gender = pd.get_dummies(stroke['gender'],drop_first=True)\n",
        "gender = gender.drop('Other', axis=1)\n",
        "gender"
      ]
    },
    {
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            "text/plain": [
              "      Yes\n",
              "0       1\n",
              "1       1\n",
              "2       1\n",
              "3       1\n",
              "4       1\n",
              "...   ...\n",
              "4083    1\n",
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              "[3928 rows x 1 columns]"
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "ever_married = pd.get_dummies(stroke['ever_married'],drop_first=True) \n",
        "ever_married"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
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        {
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            "text/plain": [
              "      Govt_job  Never_worked  Private  Self-employed  children\n",
              "0            0             0        1              0         0\n",
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              "2            0             0        1              0         0\n",
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              "...        ...           ...      ...            ...       ...\n",
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              "4087         1             0        0              0         0\n",
              "\n",
              "[3928 rows x 5 columns]"
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              "\n",
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              "                                                     [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": {},
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        }
      ],
      "source": [
        "work_type  = pd.get_dummies(stroke['work_type'])\n",
        "work_type "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
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        "id": "8d5lcP7r8i93",
        "outputId": "3aad914d-b7a9-4ebd-ff74-16074fde212e"
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      "outputs": [
        {
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          "data": {
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              "      Rural  Urban\n",
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      "source": [
        "Residence_type  = pd.get_dummies(stroke['Residence_type'])\n",
        "Residence_type "
      ]
    },
    {
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      ],
      "source": [
        "smoking_status = pd.get_dummies(stroke['smoking_status'])\n",
        "smoking_status"
      ]
    },
    {
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              "      Male   age  hypertension  heart_disease  Yes  Govt_job  Never_worked  \\\n",
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              "\n",
              "      Private  Self-employed  children  Rural  Urban  avg_glucose_level   bmi  \\\n",
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              "1           1              0         0      1      0              83.53  48.5   \n",
              "2           1              0         0      1      0              65.16  30.8   \n",
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              "      <td>0</td>\n",
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              "<p>3928 rows × 19 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "stroke=pd.concat([gender,stroke['age'],stroke['hypertension'],stroke['heart_disease'],ever_married,work_type,Residence_type,stroke['avg_glucose_level'],stroke['bmi'],smoking_status,stroke['stroke']],axis=1)\n",
        "stroke"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
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          "height": 505
        },
        "id": "ZkZfkcM-80XQ",
        "outputId": "c137b041-602c-4a41-e3ff-269bf63da53e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "      Gender   age  hypertension  heart_disease  ever_married  Govt_job  \\\n",
              "0          0  36.0             0              0             1         0   \n",
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              "\n",
              "      Never_worked  Private  Self-employed  children  Rural  Urban  \\\n",
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              "4087             0        0              0         0      1      0   \n",
              "\n",
              "      avg_glucose_level   bmi  Unknown_Smoking_Status  formerly smoked  \\\n",
              "0                 67.29  36.7                       0                1   \n",
              "1                 83.53  48.5                       0                1   \n",
              "2                 65.16  30.8                       0                0   \n",
              "3                 78.80  29.3                       0                1   \n",
              "4                 58.65  25.6                       0                0   \n",
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              "4086             120.94  29.7                       0                1   \n",
              "4087              59.52  33.2                       0                0   \n",
              "\n",
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              "      <td>58.65</td>\n",
              "      <td>25.6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4083</th>\n",
              "      <td>0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>98.61</td>\n",
              "      <td>38.8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4084</th>\n",
              "      <td>0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>72.01</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4085</th>\n",
              "      <td>1</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>94.71</td>\n",
              "      <td>28.4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4086</th>\n",
              "      <td>1</td>\n",
              "      <td>21.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>120.94</td>\n",
              "      <td>29.7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4087</th>\n",
              "      <td>0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>59.52</td>\n",
              "      <td>33.2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3928 rows × 19 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-98c00fc7-6e26-4d90-b082-6b751709c803')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </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-98c00fc7-6e26-4d90-b082-6b751709c803 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-98c00fc7-6e26-4d90-b082-6b751709c803');\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": 19
        }
      ],
      "source": [
        "stroke.rename(columns={'Male':'Gender','Yes':'ever_married','Unknown':'Unknown_Smoking_Status'},inplace=True)\n",
        "stroke"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "aNA4UZpMq24W"
      },
      "outputs": [],
      "source": [
        "X = stroke.drop('stroke', axis=1)\n",
        "y= stroke['stroke']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rpBKGRVI2C8v",
        "outputId": "36c3af54-cdf2-41bf-d57a-19a2128b0af7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3928, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rgWm-uMW2FPP",
        "outputId": "399061f6-348d-421f-a07a-63d3ca8c26e6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3928,)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "4w8oafKo2QYu"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "53vKj--j254Q"
      },
      "outputs": [],
      "source": [
        "from sklearn.naive_bayes import GaussianNB"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "5AUAR86P6Rgx"
      },
      "outputs": [],
      "source": [
        "model = GaussianNB()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "HqN26aUk7aJ5",
        "outputId": "93b3ca23-e6e9-4426-d2b1-f0cd32e91172"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict = model.predict(X_test)"
      ],
      "metadata": {
        "id": "SQXz_7Er7eb4"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "I0GmFR6G_SoB"
      },
      "outputs": [],
      "source": [
        "from sklearn import metrics "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "_NUmqTiH_EdL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95abd976-1800-4bda-ecce-e741625ed48b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.42464358452138495"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "metrics.accuracy_score(y_test,y_predict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "RIprdqRZA_rS"
      },
      "outputs": [],
      "source": [
        "testdata = pd.read_csv('/content/test (1).csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "FWFYSF67BmT8"
      },
      "outputs": [],
      "source": [
        "testdata['bmi']=testdata['bmi'].fillna(testdata['bmi'].mean())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "YAzKZCtnCeTv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4923b032-b948-45eb-c821-fec8834741b7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                   0\n",
              "gender               0\n",
              "age                  0\n",
              "hypertension         0\n",
              "heart_disease        0\n",
              "ever_married         0\n",
              "work_type            0\n",
              "Residence_type       0\n",
              "avg_glucose_level    0\n",
              "bmi                  0\n",
              "smoking_status       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "testdata.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "FSkkVKWkCyG8"
      },
      "outputs": [],
      "source": [
        "id2=testdata['id']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "RVIv2Ki7CnIG"
      },
      "outputs": [],
      "source": [
        "testdata.drop('id',axis=1, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "CEHZrYIeCt6l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "441296d5-e29b-42bd-e6b3-f192193dff91"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Male\n",
              "0        1\n",
              "1        1\n",
              "2        1\n",
              "3        0\n",
              "4        0\n",
              "...    ...\n",
              "1017     0\n",
              "1018     0\n",
              "1019     1\n",
              "1020     1\n",
              "1021     0\n",
              "\n",
              "[1022 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7686fe37-fd8d-47cc-b729-04f2f116c9d2\">\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",
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              "      <th>Male</th>\n",
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              "  <tbody>\n",
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          "metadata": {},
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      ],
      "source": [
        "gender = pd.get_dummies(testdata['gender'],drop_first=True)\n",
        "gender"
      ]
    },
    {
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        {
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              "      Yes\n",
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              "1       0\n",
              "2       1\n",
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              "...   ...\n",
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              "1021    1\n",
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              "[1022 rows x 1 columns]"
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              "                                                     [key], {});\n",
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          "metadata": {},
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      ],
      "source": [
        "ever_married= pd.get_dummies(testdata['ever_married'],drop_first=True) \n",
        "ever_married"
      ]
    },
    {
      "cell_type": "code",
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      "outputs": [
        {
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            "text/plain": [
              "      Govt_job  Never_worked  Private  Self-employed  children\n",
              "0            0             0        1              0         0\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": 37
        }
      ],
      "source": [
        "work_type  = pd.get_dummies(testdata['work_type'])\n",
        "work_type "
      ]
    },
    {
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        {
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            "text/plain": [
              "      Rural  Urban\n",
              "0         0      1\n",
              "1         1      0\n",
              "2         0      1\n",
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              "4         1      0\n",
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              "1021      1      0\n",
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              "[1022 rows x 2 columns]"
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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      ],
      "source": [
        "Residence_type = pd.get_dummies(testdata['Residence_type'])\n",
        "Residence_type"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
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              "      Unknown  formerly smoked  never smoked  smokes\n",
              "0           0                0             0       1\n",
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              "                                                     [key], {});\n",
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              "\n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "smoking_status = pd.get_dummies(testdata['smoking_status'])\n",
        "smoking_status"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
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        "outputId": "d15d9de0-ed95-43c2-c8c8-ad7946033df2"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Male   age  hypertension  heart_disease  Yes  Govt_job  Never_worked  \\\n",
              "0        1  82.0             0              1    1         0             0   \n",
              "1        1   4.0             0              0    0         0             0   \n",
              "2        1  58.0             0              0    1         0             0   \n",
              "3        0  20.0             0              0    0         0             0   \n",
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              "...    ...   ...           ...            ...  ...       ...           ...   \n",
              "1017     0  43.0             0              0    1         0             0   \n",
              "1018     0  42.0             0              0    1         0             0   \n",
              "1019     1  52.0             0              0    0         0             0   \n",
              "1020     1  78.0             0              0    1         0             0   \n",
              "1021     0  35.0             0              0    1         0             0   \n",
              "\n",
              "      Private  Self-employed  children  Rural  Urban  avg_glucose_level  \\\n",
              "0           1              0         0      0      1             144.90   \n",
              "1           0              0         1      1      0             106.22   \n",
              "2           1              0         0      0      1              79.95   \n",
              "3           1              0         0      1      0              96.57   \n",
              "4           0              0         1      1      0              69.84   \n",
              "...       ...            ...       ...    ...    ...                ...   \n",
              "1017        1              0         0      1      0              75.05   \n",
              "1018        1              0         0      0      1             191.94   \n",
              "1019        1              0         0      1      0              69.37   \n",
              "1020        1              0         0      0      1             237.75   \n",
              "1021        1              0         0      1      0              83.27   \n",
              "\n",
              "            bmi  Unknown  formerly smoked  never smoked  smokes  \n",
              "0     26.400000        0                0             0       1  \n",
              "1     16.700000        1                0             0       0  \n",
              "2     25.900000        0                0             1       0  \n",
              "3     34.100000        0                0             1       0  \n",
              "4     13.700000        1                0             0       0  \n",
              "...         ...      ...              ...           ...     ...  \n",
              "1017  22.900000        0                0             0       1  \n",
              "1018  27.900000        0                0             1       0  \n",
              "1019  36.200000        1                0             0       0  \n",
              "1020  28.845056        0                1             0       0  \n",
              "1021  19.800000        0                1             0       0  \n",
              "\n",
              "[1022 rows x 18 columns]"
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>96.57</td>\n",
              "      <td>34.100000</td>\n",
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              "      <td>1</td>\n",
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              "<p>1022 rows × 18 columns</p>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "testdata=pd.concat([gender,testdata['age'],testdata['hypertension'],testdata['heart_disease'],ever_married,work_type,Residence_type,testdata['avg_glucose_level'],testdata['bmi'],smoking_status],axis=1)\n",
        "testdata"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "oBI0GvBtD_Jx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "outputId": "c09efa75-f973-4c45-d3a9-a15d9802adce"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Gender   age  hypertension  heart_disease  ever_married  Govt_job  \\\n",
              "0          1  82.0             0              1             1         0   \n",
              "1          1   4.0             0              0             0         0   \n",
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              "3          0  20.0             0              0             0         0   \n",
              "4          0  10.0             0              0             0         0   \n",
              "...      ...   ...           ...            ...           ...       ...   \n",
              "1017       0  43.0             0              0             1         0   \n",
              "1018       0  42.0             0              0             1         0   \n",
              "1019       1  52.0             0              0             0         0   \n",
              "1020       1  78.0             0              0             1         0   \n",
              "1021       0  35.0             0              0             1         0   \n",
              "\n",
              "      Never_worked  Private  Self-employed  children  Rural  Urban  \\\n",
              "0                0        1              0         0      0      1   \n",
              "1                0        0              0         1      1      0   \n",
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              "4                0        0              0         1      1      0   \n",
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              "1017             0        1              0         0      1      0   \n",
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              "1019             0        1              0         0      1      0   \n",
              "1020             0        1              0         0      0      1   \n",
              "1021             0        1              0         0      1      0   \n",
              "\n",
              "      avg_glucose_level        bmi  Unknown_Smoking_Status  formerly smoked  \\\n",
              "0                144.90  26.400000                       0                0   \n",
              "1                106.22  16.700000                       1                0   \n",
              "2                 79.95  25.900000                       0                0   \n",
              "3                 96.57  34.100000                       0                0   \n",
              "4                 69.84  13.700000                       1                0   \n",
              "...                 ...        ...                     ...              ...   \n",
              "1017              75.05  22.900000                       0                0   \n",
              "1018             191.94  27.900000                       0                0   \n",
              "1019              69.37  36.200000                       1                0   \n",
              "1020             237.75  28.845056                       0                1   \n",
              "1021              83.27  19.800000                       0                1   \n",
              "\n",
              "      never smoked  smokes  \n",
              "0                0       1  \n",
              "1                0       0  \n",
              "2                1       0  \n",
              "3                1       0  \n",
              "4                0       0  \n",
              "...            ...     ...  \n",
              "1017             0       1  \n",
              "1018             1       0  \n",
              "1019             0       0  \n",
              "1020             0       0  \n",
              "1021             0       0  \n",
              "\n",
              "[1022 rows x 18 columns]"
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              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>96.57</td>\n",
              "      <td>34.100000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>69.84</td>\n",
              "      <td>13.700000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1017</th>\n",
              "      <td>0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>75.05</td>\n",
              "      <td>22.900000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1018</th>\n",
              "      <td>0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>191.94</td>\n",
              "      <td>27.900000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1019</th>\n",
              "      <td>1</td>\n",
              "      <td>52.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>69.37</td>\n",
              "      <td>36.200000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1020</th>\n",
              "      <td>1</td>\n",
              "      <td>78.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>237.75</td>\n",
              "      <td>28.845056</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1021</th>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>83.27</td>\n",
              "      <td>19.800000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1022 rows × 18 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-900f8468-8e8f-4ae0-9e81-ac29441ff464')\"\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "\n",
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              "\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "testdata.rename(columns={'Male':'Gender','Yes':'ever_married','Unknown':'Unknown_Smoking_Status'},inplace=True)\n",
        "testdata"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "e8vNFnykEMsg"
      },
      "outputs": [],
      "source": [
        "y_predict2 =model.predict(testdata)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "PFascCpcOlrX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8759041-7863-4438-e2a2-f274b2221c2b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "type(y_predict2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "NVftJErJEVNP"
      },
      "outputs": [],
      "source": [
        "id=np.arange(len(y_predict2))\n",
        "datatest=pd.DataFrame({'id':id2 , 'Stroke':y_predict2})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
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        "colab": {
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          "height": 423
        },
        "outputId": "2eafad58-efee-4e5e-cdb7-82d14a398cfe"
      },
      "outputs": [
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              "         id  Stroke\n",
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              "4     25391       0\n",
              "...     ...     ...\n",
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              "1018  47799       1\n",
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              "1020  66400       1\n",
              "1021   9013       1\n",
              "\n",
              "[1022 rows x 2 columns]"
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              "          const element = document.querySelector('#df-43d31588-df16-4152-9023-51e7512a9300');\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",
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              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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      "execution_count": 46,
      "metadata": {
        "id": "RNo_0VOqEwOi"
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      "outputs": [],
      "source": [
        "datatest.to_csv('predictied.csv',index=False)"
      ]
    },
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      "cell_type": "code",
      "execution_count": 47,
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              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_08173624-1e18-453d-82b2-e6ba9261c7cd\", \"predictied.csv\", 8031)"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from google.colab import files\n",
        "files.download('predictied.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "nQDCrdRzFEol",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca33e0bf-a193-435e-ed1d-98340c619b6b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1022"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "len(y_predict2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "ueF5vmiPGbgr"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}