bagging.ipynb

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        "<a href=\"https://colab.research.google.com/github/Mathurshab2210/Ml_with_Mathur/blob/main/bagging.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d28Zpxjde_E-"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv('/content/drive/MyDrive/ML_practtice/diabetes.csv')"
      ],
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        "id": "sHs0L73cfIl3"
      },
      "execution_count": 6,
      "outputs": []
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        "data.head()"
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      },
      "execution_count": 7,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
              "1            1       85             66             29        0  26.6   \n",
              "2            8      183             64              0        0  23.3   \n",
              "3            1       89             66             23       94  28.1   \n",
              "4            0      137             40             35      168  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
              "3                     0.167   21        0  \n",
              "4                     2.288   33        1  "
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                 0\n",
              "Glucose                     0\n",
              "BloodPressure               0\n",
              "SkinThickness               0\n",
              "Insulin                     0\n",
              "BMI                         0\n",
              "DiabetesPedigreeFunction    0\n",
              "Age                         0\n",
              "Outcome                     0\n",
              "dtype: int64"
            ]
          },
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          "execution_count": 8
        }
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      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
              "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
              "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
              "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
              "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
              "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
              "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
              "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
              "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
              "\n",
              "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
              "count  768.000000                768.000000  768.000000  768.000000  \n",
              "mean    31.992578                  0.471876   33.240885    0.348958  \n",
              "std      7.884160                  0.331329   11.760232    0.476951  \n",
              "min      0.000000                  0.078000   21.000000    0.000000  \n",
              "25%     27.300000                  0.243750   24.000000    0.000000  \n",
              "50%     32.000000                  0.372500   29.000000    0.000000  \n",
              "75%     36.600000                  0.626250   41.000000    1.000000  \n",
              "max     67.100000                  2.420000   81.000000    1.000000  "
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.300000</td>\n",
              "      <td>0.243750</td>\n",
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              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>32.000000</td>\n",
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              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.250000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>0.626250</td>\n",
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              "          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": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qChBnGg8gPOy",
        "outputId": "2bd70bc2-f85a-450b-9a93-ae0dbdf90968"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Pregnancies               768 non-null    int64  \n",
            " 1   Glucose                   768 non-null    int64  \n",
            " 2   BloodPressure             768 non-null    int64  \n",
            " 3   SkinThickness             768 non-null    int64  \n",
            " 4   Insulin                   768 non-null    int64  \n",
            " 5   BMI                       768 non-null    float64\n",
            " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
            " 7   Age                       768 non-null    int64  \n",
            " 8   Outcome                   768 non-null    int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xIeDs0-UgQeN",
        "outputId": "a2d3a00b-3855-4b7b-b1b2-c5dccde84841"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(768, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.Outcome.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H2XBmEGBgR_m",
        "outputId": "e0d9edcc-1fe6-41f6-e015-137b36375839"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    500\n",
              "1    268\n",
              "Name: Outcome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=data.drop('Outcome', axis=1)\n",
        "y=data['Outcome']"
      ],
      "metadata": {
        "id": "q71thT8dgb4a"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "MczGuLHhgs6i"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MP0yqBZHg0zi",
        "outputId": "cad3878f-69c5-47b1-d4f6-29171476baea"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "1      0\n",
              "2      1\n",
              "3      0\n",
              "4      1\n",
              "      ..\n",
              "763    0\n",
              "764    0\n",
              "765    0\n",
              "766    1\n",
              "767    0\n",
              "Name: Outcome, Length: 768, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "b4SVpAN0g4cv"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler=StandardScaler()"
      ],
      "metadata": {
        "id": "Qa8ktXjdhB2S"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_scaler=scaler.fit_transform(x)"
      ],
      "metadata": {
        "id": "0-H0QjcjhFRO"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x_scaler,y,stratify=y,test_size=0.2,random_state=42)"
      ],
      "metadata": {
        "id": "xfKR1ucthL9V"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "scores = cross_val_score(DecisionTreeClassifier(), x_scaler, y, cv=5)\n",
        "scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Ya8P_srhhXT",
        "outputId": "6ba9f7a8-c385-4452-fd78-d613f9c44abb"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.68181818, 0.68181818, 0.69480519, 0.79084967, 0.71895425])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Om70mqjDiJ1E",
        "outputId": "a483a4b1-4f82-4be7-85c9-03c7548af2a7"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7136490960020371"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import BaggingClassifier\n",
        "\n",
        "bag_model = BaggingClassifier(\n",
        "    base_estimator=DecisionTreeClassifier(), \n",
        "    n_estimators=75, \n",
        "    max_samples=0.8, \n",
        "    oob_score=True,\n",
        "    random_state=42\n",
        ")\n",
        "bag_model.fit(x_train, y_train)\n",
        "bag_model.oob_score_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EavchE7KiNHi",
        "outputId": "8769868b-f850-4296-e874-8e7cd189247f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.750814332247557"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bag_model.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yQGyFmpbjdB6",
        "outputId": "db10c468-8b89-4783-f8f2-c129976b3c85"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7792207792207793"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores = cross_val_score(bag_model, x, y, cv=5)\n",
        "scores.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RncwpD3Ujqoi",
        "outputId": "42542e42-87ba-4b2f-b7f9-a1f7e6b125ab"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7709107885578474"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/content/drive/MyDrive/ML_practtice/heart.csv')"
      ],
      "metadata": {
        "id": "4bNj_ty1j2CG"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xa8Cp_bYkY3K",
        "outputId": "6b2ed6bd-683d-4e1d-944c-4ecf2f5daba5"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
              "0   40   M           ATA        140          289          0     Normal    172   \n",
              "1   49   F           NAP        160          180          0     Normal    156   \n",
              "2   37   M           ATA        130          283          0         ST     98   \n",
              "3   48   F           ASY        138          214          0     Normal    108   \n",
              "4   54   M           NAP        150          195          0     Normal    122   \n",
              "\n",
              "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
              "0              N      0.0       Up             0  \n",
              "1              N      1.0     Flat             1  \n",
              "2              N      0.0       Up             0  \n",
              "3              Y      1.5     Flat             1  \n",
              "4              N      0.0       Up             0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-84310d43-dae5-4f02-a96a-f17adb6f1eaf\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>ChestPainType</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
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              "      <th>MaxHR</th>\n",
              "      <th>ExerciseAngina</th>\n",
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              "      <th>0</th>\n",
              "      <td>40</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
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              "      <td>ATA</td>\n",
              "      <td>130</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>ST</td>\n",
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              "      <td>N</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "      <td>F</td>\n",
              "      <td>ASY</td>\n",
              "      <td>138</td>\n",
              "      <td>214</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>108</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54</td>\n",
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              "      <td>150</td>\n",
              "      <td>195</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>122</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84310d43-dae5-4f02-a96a-f17adb6f1eaf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          document.querySelector('#df-84310d43-dae5-4f02-a96a-f17adb6f1eaf button.colab-df-convert');\n",
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              "          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",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "GGX5XFeqkaP6",
        "outputId": "9790b107-a5ad-4336-9ef1-e85e9fa226db"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Age   RestingBP  Cholesterol   FastingBS       MaxHR  \\\n",
              "count  918.000000  918.000000   918.000000  918.000000  918.000000   \n",
              "mean    53.510893  132.396514   198.799564    0.233115  136.809368   \n",
              "std      9.432617   18.514154   109.384145    0.423046   25.460334   \n",
              "min     28.000000    0.000000     0.000000    0.000000   60.000000   \n",
              "25%     47.000000  120.000000   173.250000    0.000000  120.000000   \n",
              "50%     54.000000  130.000000   223.000000    0.000000  138.000000   \n",
              "75%     60.000000  140.000000   267.000000    0.000000  156.000000   \n",
              "max     77.000000  200.000000   603.000000    1.000000  202.000000   \n",
              "\n",
              "          Oldpeak  HeartDisease  \n",
              "count  918.000000    918.000000  \n",
              "mean     0.887364      0.553377  \n",
              "std      1.066570      0.497414  \n",
              "min     -2.600000      0.000000  \n",
              "25%      0.000000      0.000000  \n",
              "50%      0.600000      1.000000  \n",
              "75%      1.500000      1.000000  \n",
              "max      6.200000      1.000000  "
            ],
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>HeartDisease</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>53.510893</td>\n",
              "      <td>132.396514</td>\n",
              "      <td>198.799564</td>\n",
              "      <td>0.233115</td>\n",
              "      <td>136.809368</td>\n",
              "      <td>0.887364</td>\n",
              "      <td>0.553377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.432617</td>\n",
              "      <td>18.514154</td>\n",
              "      <td>109.384145</td>\n",
              "      <td>0.423046</td>\n",
              "      <td>25.460334</td>\n",
              "      <td>1.066570</td>\n",
              "      <td>0.497414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>-2.600000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>173.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>54.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>223.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>267.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>603.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a1e689b6-bf21-4369-819d-c0b1f755489f')\"\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",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-a1e689b6-bf21-4369-819d-c0b1f755489f 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-a1e689b6-bf21-4369-819d-c0b1f755489f');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RNIRvEwFkh1M",
        "outputId": "5c37fd25-78d5-42b2-d523-619b3aa81d70"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 918 entries, 0 to 917\n",
            "Data columns (total 12 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Age             918 non-null    int64  \n",
            " 1   Sex             918 non-null    object \n",
            " 2   ChestPainType   918 non-null    object \n",
            " 3   RestingBP       918 non-null    int64  \n",
            " 4   Cholesterol     918 non-null    int64  \n",
            " 5   FastingBS       918 non-null    int64  \n",
            " 6   RestingECG      918 non-null    object \n",
            " 7   MaxHR           918 non-null    int64  \n",
            " 8   ExerciseAngina  918 non-null    object \n",
            " 9   Oldpeak         918 non-null    float64\n",
            " 10  ST_Slope        918 non-null    object \n",
            " 11  HeartDisease    918 non-null    int64  \n",
            "dtypes: float64(1), int64(6), object(5)\n",
            "memory usage: 86.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-3jAGfnyki0X",
        "outputId": "394e47f4-c669-4ed8-dcfe-cf5e5dc174d2"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age               0\n",
              "Sex               0\n",
              "ChestPainType     0\n",
              "RestingBP         0\n",
              "Cholesterol       0\n",
              "FastingBS         0\n",
              "RestingECG        0\n",
              "MaxHR             0\n",
              "ExerciseAngina    0\n",
              "Oldpeak           0\n",
              "ST_Slope          0\n",
              "HeartDisease      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        "df['Sex']= label_encoder.fit_transform(df['Sex'])\n",
        "\n",
        "df['ChestPainType']= label_encoder.fit_transform(df['ChestPainType'])\n",
        "\n",
        "df['RestingECG']= label_encoder.fit_transform(df['RestingECG'])\n",
        "\n",
        "df['ExerciseAngina']= label_encoder.fit_transform(df['ExerciseAngina'])\n",
        "df['ST_Slope']= label_encoder.fit_transform(df['ST_Slope'])\n",
        "df=df.drop('ST_Slope',axis=1)\n"
      ],
      "metadata": {
        "id": "EqDHoZWxkoQq"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xh3Tpf4jl8Ae",
        "outputId": "eb9b0f12-d59e-4778-a344-271927e600cf"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  Sex  ChestPainType  RestingBP  Cholesterol  FastingBS  RestingECG  \\\n",
              "0   40    1              1        140          289          0           1   \n",
              "1   49    0              2        160          180          0           1   \n",
              "2   37    1              1        130          283          0           2   \n",
              "3   48    0              0        138          214          0           1   \n",
              "4   54    1              2        150          195          0           1   \n",
              "\n",
              "   MaxHR  ExerciseAngina  Oldpeak  HeartDisease  ST_Slope   \n",
              "0    172               0      0.0             0          2  \n",
              "1    156               0      1.0             1          1  \n",
              "2     98               0      0.0             0          2  \n",
              "3    108               1      1.5             1          1  \n",
              "4    122               0      0.0             0          2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0ac10087-30c6-40fd-ba27-3d8425967873\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "      <th>0</th>\n",
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              "    <tr>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-convert:hover {\n",
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              "\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-0ac10087-30c6-40fd-ba27-3d8425967873 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-0ac10087-30c6-40fd-ba27-3d8425967873');\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": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=df.drop('HeartDisease',axis=1)\n",
        "y=df.HeartDisease"
      ],
      "metadata": {
        "id": "AcUZ6JRel-dx"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_scaler=scaler.fit_transform(x)"
      ],
      "metadata": {
        "id": "jgYZM2tHmZ1k"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x_scaler,y,stratify=y,test_size=0.2,random_state=42)"
      ],
      "metadata": {
        "id": "9t511IrVmktm"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "scores = cross_val_score(DecisionTreeClassifier(), x_scaler, y, cv=5)\n",
        "scores.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eqoTyIVtmlHk",
        "outputId": "325de72f-32f7-4e3e-bf52-5d856c7d3001"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7352280826799714"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import BaggingClassifier\n",
        "\n",
        "bag_model = BaggingClassifier(\n",
        "    base_estimator=DecisionTreeClassifier(), \n",
        "    n_estimators=75, \n",
        "    max_samples=0.8, \n",
        "    oob_score=True,\n",
        "    random_state=42\n",
        ")\n",
        "bag_model.fit(x_train, y_train)\n",
        "bag_model.oob_score_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bkcGjKbZmqts",
        "outputId": "4cbcab1d-0344-46cd-c748-7e23068f8f4c"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.837874659400545"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bag_model.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6eQQ0eP6mxhD",
        "outputId": "442aaa5d-f49b-484b-fbba-ecd069a6f711"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.875"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores = cross_val_score(bag_model, x_scaler, y, cv=8)\n",
        "scores.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "62dG10xem39V",
        "outputId": "969b4f17-a7c0-4792-bd19-bd37a640af2f"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.850629290617849"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vskqch07m9ws"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}