yhills_project1.ipynb

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        "<a href=\"https://colab.research.google.com/github/oshinrathor/datSci/blob/main/yhills_project1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "pO6fHEVbocDz"
      },
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"h1n1_vaccine_prediction.csv\")\n",
        "df"
      ],
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        "id": "R4tbm-jbqXAJ",
        "outputId": "dd7350ec-f70b-4fa7-91f5-985b633519ad"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "       unique_id  h1n1_worry  h1n1_awareness  antiviral_medication  \\\n",
              "0              0         1.0             0.0                   0.0   \n",
              "1              1         3.0             2.0                   0.0   \n",
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              "...          ...         ...             ...                   ...   \n",
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              "26705      26705         1.0             1.0                   0.0   \n",
              "26706      26706         0.0             0.0                   0.0   \n",
              "\n",
              "       contact_avoidance  bought_face_mask  wash_hands_frequently  \\\n",
              "0                    0.0               0.0                    0.0   \n",
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              "26706                1.0               0.0                    0.0   \n",
              "\n",
              "       avoid_large_gatherings  reduced_outside_home_cont  avoid_touch_face  \\\n",
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              "26705                     0.0                        0.0               NaN   \n",
              "26706                     0.0                        0.0               0.0   \n",
              "\n",
              "       ...      race     sex               income_level  marital_status  \\\n",
              "0      ...     White  Female              Below Poverty     Not Married   \n",
              "1      ...     White    Male              Below Poverty     Not Married   \n",
              "2      ...     White    Male  <= $75,000, Above Poverty     Not Married   \n",
              "3      ...     White  Female              Below Poverty     Not Married   \n",
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              "26702  ...     White  Female  <= $75,000, Above Poverty     Not Married   \n",
              "26703  ...     White    Male  <= $75,000, Above Poverty     Not Married   \n",
              "26704  ...     White  Female                        NaN     Not Married   \n",
              "26705  ...  Hispanic  Female  <= $75,000, Above Poverty         Married   \n",
              "26706  ...     White    Male  <= $75,000, Above Poverty         Married   \n",
              "\n",
              "       housing_status          employment                census_msa  \\\n",
              "0                 Own  Not in Labor Force                   Non-MSA   \n",
              "1                Rent            Employed  MSA, Not Principle  City   \n",
              "2                 Own            Employed  MSA, Not Principle  City   \n",
              "3                Rent  Not in Labor Force       MSA, Principle City   \n",
              "4                 Own            Employed  MSA, Not Principle  City   \n",
              "...               ...                 ...                       ...   \n",
              "26702             Own  Not in Labor Force                   Non-MSA   \n",
              "26703            Rent            Employed       MSA, Principle City   \n",
              "26704             Own                 NaN  MSA, Not Principle  City   \n",
              "26705            Rent            Employed                   Non-MSA   \n",
              "26706             Own  Not in Labor Force       MSA, Principle City   \n",
              "\n",
              "       no_of_adults  no_of_children  h1n1_vaccine  \n",
              "0               0.0             0.0             0  \n",
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              "2               2.0             0.0             0  \n",
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              "26705           1.0             0.0             0  \n",
              "26706           1.0             0.0             0  \n",
              "\n",
              "[26707 rows x 34 columns]"
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              "    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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-3a90fed8-ee77-46eb-b9ff-86763dd12021 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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-a0079d24-dce3-4cdc-8447-1f6ab1f52783 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-a0079d24-dce3-4cdc-8447-1f6ab1f52783');\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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LW_nwCJeqbHs",
        "outputId": "ae8724f9-e307-4d68-abd7-69a3a263fa27"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "unique_id                        0\n",
              "h1n1_worry                      92\n",
              "h1n1_awareness                 116\n",
              "antiviral_medication            71\n",
              "contact_avoidance              208\n",
              "bought_face_mask                19\n",
              "wash_hands_frequently           42\n",
              "avoid_large_gatherings          87\n",
              "reduced_outside_home_cont       82\n",
              "avoid_touch_face               128\n",
              "dr_recc_h1n1_vacc             2160\n",
              "dr_recc_seasonal_vacc         2160\n",
              "chronic_medic_condition        971\n",
              "cont_child_undr_6_mnths        820\n",
              "is_health_worker               804\n",
              "has_health_insur             12274\n",
              "is_h1n1_vacc_effective         391\n",
              "is_h1n1_risky                  388\n",
              "sick_from_h1n1_vacc            395\n",
              "is_seas_vacc_effective         462\n",
              "is_seas_risky                  514\n",
              "sick_from_seas_vacc            537\n",
              "age_bracket                      0\n",
              "qualification                 1407\n",
              "race                             0\n",
              "sex                              0\n",
              "income_level                  4423\n",
              "marital_status                1408\n",
              "housing_status                2042\n",
              "employment                    1463\n",
              "census_msa                       0\n",
              "no_of_adults                   249\n",
              "no_of_children                 249\n",
              "h1n1_vaccine                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Handling missing values"
      ],
      "metadata": {
        "id": "h4X89qiXrvet"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.isnull(), yticklabels=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "id": "sIGamNCOr3Sv",
        "outputId": "0c016f64-b22c-419f-d043-93b70b9e2059"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(how=\"all\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        },
        "id": "Jyn4Sah3tdk9",
        "outputId": "1f82b805-868f-4feb-e1b4-c5f9cfedcbcf"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       unique_id  h1n1_worry  h1n1_awareness  antiviral_medication  \\\n",
              "0              0         1.0             0.0                   0.0   \n",
              "1              1         3.0             2.0                   0.0   \n",
              "2              2         1.0             1.0                   0.0   \n",
              "3              3         1.0             1.0                   0.0   \n",
              "4              4         2.0             1.0                   0.0   \n",
              "...          ...         ...             ...                   ...   \n",
              "26702      26702         2.0             0.0                   0.0   \n",
              "26703      26703         1.0             2.0                   0.0   \n",
              "26704      26704         2.0             2.0                   0.0   \n",
              "26705      26705         1.0             1.0                   0.0   \n",
              "26706      26706         0.0             0.0                   0.0   \n",
              "\n",
              "       contact_avoidance  bought_face_mask  wash_hands_frequently  \\\n",
              "0                    0.0               0.0                    0.0   \n",
              "1                    1.0               0.0                    1.0   \n",
              "2                    1.0               0.0                    0.0   \n",
              "3                    1.0               0.0                    1.0   \n",
              "4                    1.0               0.0                    1.0   \n",
              "...                  ...               ...                    ...   \n",
              "26702                1.0               0.0                    0.0   \n",
              "26703                1.0               0.0                    1.0   \n",
              "26704                1.0               1.0                    1.0   \n",
              "26705                0.0               0.0                    0.0   \n",
              "26706                1.0               0.0                    0.0   \n",
              "\n",
              "       avoid_large_gatherings  reduced_outside_home_cont  avoid_touch_face  \\\n",
              "0                         0.0                        1.0               1.0   \n",
              "1                         0.0                        1.0               1.0   \n",
              "2                         0.0                        0.0               0.0   \n",
              "3                         1.0                        0.0               0.0   \n",
              "4                         1.0                        0.0               1.0   \n",
              "...                       ...                        ...               ...   \n",
              "26702                     0.0                        1.0               0.0   \n",
              "26703                     0.0                        0.0               0.0   \n",
              "26704                     1.0                        0.0               1.0   \n",
              "26705                     0.0                        0.0               NaN   \n",
              "26706                     0.0                        0.0               0.0   \n",
              "\n",
              "       ...      race     sex               income_level  marital_status  \\\n",
              "0      ...     White  Female              Below Poverty     Not Married   \n",
              "1      ...     White    Male              Below Poverty     Not Married   \n",
              "2      ...     White    Male  <= $75,000, Above Poverty     Not Married   \n",
              "3      ...     White  Female              Below Poverty     Not Married   \n",
              "4      ...     White  Female  <= $75,000, Above Poverty         Married   \n",
              "...    ...       ...     ...                        ...             ...   \n",
              "26702  ...     White  Female  <= $75,000, Above Poverty     Not Married   \n",
              "26703  ...     White    Male  <= $75,000, Above Poverty     Not Married   \n",
              "26704  ...     White  Female                        NaN     Not Married   \n",
              "26705  ...  Hispanic  Female  <= $75,000, Above Poverty         Married   \n",
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['no_of_children'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wKzaYyBvuBm_",
        "outputId": "579715f9-7380-42a5-c819-1a142535ed34"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    18672\n",
              "1.0     3175\n",
              "2.0     2864\n",
              "3.0     1747\n",
              "Name: no_of_children, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D6R0-6T8y6ZC",
        "outputId": "816a0cd7-d93e-4296-db2f-d9aef7d55c19"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(26707, 34)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "SyBXvUtyy_9C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OVuZ7g7vzBdq",
        "outputId": "00bc1614-90ae-4050-dab4-293b1b4e12f2"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "float64    23\n",
              "object      9\n",
              "int64       2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WwBxmWH4zBv-",
        "outputId": "21b41ff9-027d-4785-c97d-caaf100a7216"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['unique_id', 'h1n1_worry', 'h1n1_awareness', 'antiviral_medication',\n",
              "       'contact_avoidance', 'bought_face_mask', 'wash_hands_frequently',\n",
              "       'avoid_large_gatherings', 'reduced_outside_home_cont',\n",
              "       'avoid_touch_face', 'dr_recc_h1n1_vacc', 'dr_recc_seasonal_vacc',\n",
              "       'chronic_medic_condition', 'cont_child_undr_6_mnths',\n",
              "       'is_health_worker', 'has_health_insur', 'is_h1n1_vacc_effective',\n",
              "       'is_h1n1_risky', 'sick_from_h1n1_vacc', 'is_seas_vacc_effective',\n",
              "       'is_seas_risky', 'sick_from_seas_vacc', 'age_bracket', 'qualification',\n",
              "       'race', 'sex', 'income_level', 'marital_status', 'housing_status',\n",
              "       'employment', 'census_msa', 'no_of_adults', 'no_of_children',\n",
              "       'h1n1_vaccine'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['h1n1_worry'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kdp0Hk6PzB_Z",
        "outputId": "bbc1bdf8-b1b9-466e-f348-aa92b1084be2"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.,  3.,  2.,  0., nan])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['unique_id'], inplace = True, axis = 1)"
      ],
      "metadata": {
        "id": "hsW0mlLYzCb-"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NI_l9qtvzCn3",
        "outputId": "1bb8b659-c97c-4a15-e600-d35a390388de"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "h1n1_worry                      92\n",
              "h1n1_awareness                 116\n",
              "antiviral_medication            71\n",
              "contact_avoidance              208\n",
              "bought_face_mask                19\n",
              "wash_hands_frequently           42\n",
              "avoid_large_gatherings          87\n",
              "reduced_outside_home_cont       82\n",
              "avoid_touch_face               128\n",
              "dr_recc_h1n1_vacc             2160\n",
              "dr_recc_seasonal_vacc         2160\n",
              "chronic_medic_condition        971\n",
              "cont_child_undr_6_mnths        820\n",
              "is_health_worker               804\n",
              "has_health_insur             12274\n",
              "is_h1n1_vacc_effective         391\n",
              "is_h1n1_risky                  388\n",
              "sick_from_h1n1_vacc            395\n",
              "is_seas_vacc_effective         462\n",
              "is_seas_risky                  514\n",
              "sick_from_seas_vacc            537\n",
              "age_bracket                      0\n",
              "qualification                 1407\n",
              "race                             0\n",
              "sex                              0\n",
              "income_level                  4423\n",
              "marital_status                1408\n",
              "housing_status                2042\n",
              "employment                    1463\n",
              "census_msa                       0\n",
              "no_of_adults                   249\n",
              "no_of_children                 249\n",
              "h1n1_vaccine                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['h1n1_worry'].fillna(df['h1n1_worry'].mode()[0], inplace = True)\n",
        "df['h1n1_awareness'].fillna(df['h1n1_awareness'].mode()[0], inplace = True)\n",
        "df['antiviral_medication'].fillna(df['antiviral_medication'].mode()[0], inplace = True)\n",
        "df['contact_avoidance'].fillna(df['contact_avoidance'].mode()[0], inplace = True)\n",
        "df['bought_face_mask'].fillna(df['bought_face_mask'].mode()[0], inplace = True)\n",
        "df['wash_hands_frequently'].fillna(df['wash_hands_frequently'].mode()[0], inplace = True)\n",
        "df['avoid_large_gatherings'].fillna(df['avoid_large_gatherings'].mode()[0], inplace = True)\n",
        "df['reduced_outside_home_cont'].fillna(df['reduced_outside_home_cont'].mode()[0], inplace = True)\n",
        "df['avoid_touch_face'].fillna(df['avoid_touch_face'].mode()[0], inplace = True)\n",
        "df['dr_recc_h1n1_vacc'].fillna(df['dr_recc_h1n1_vacc'].mode()[0], inplace = True)\n",
        "df['dr_recc_seasonal_vacc'].fillna(df['dr_recc_seasonal_vacc'].mode()[0], inplace = True)\n",
        "df['chronic_medic_condition'].fillna(df['chronic_medic_condition'].mode()[0], inplace = True)\n",
        "df['cont_child_undr_6_mnths'].fillna(df['cont_child_undr_6_mnths'].mode()[0], inplace = True)\n",
        "df['is_health_worker'].fillna(df['is_health_worker'].mode()[0], inplace = True)\n",
        "df['has_health_insur'].fillna(2.0, inplace = True)\n",
        "df['is_h1n1_vacc_effective'].fillna(df['is_h1n1_vacc_effective'].mode()[0], inplace = True)\n",
        "df['is_seas_vacc_effective'].fillna(df['is_seas_vacc_effective'].mode()[0], inplace = True)\n",
        "df['is_h1n1_risky'].fillna(df['is_h1n1_risky'].mode()[0], inplace = True)\n",
        "df['is_seas_risky'].fillna(df['is_seas_risky'].mode()[0], inplace = True)\n",
        "df['sick_from_h1n1_vacc'].fillna(df['sick_from_h1n1_vacc'].mode()[0], inplace = True)\n",
        "df['sick_from_seas_vacc'].fillna(df['sick_from_seas_vacc'].mode()[0], inplace = True)\n",
        "df['qualification'].fillna(df['qualification'].mode()[0], inplace = True)\n",
        "df['income_level'].fillna('Unknown', inplace = True)\n",
        "df['marital_status'].fillna(df['marital_status'].mode()[0], inplace = True)"
      ],
      "metadata": {
        "id": "O6JvssvKzDXD"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['housing_status'].fillna(df['housing_status'].mode()[0], inplace = True)\n",
        "df['employment'].fillna(df['employment'].mode()[0], inplace = True)\n",
        "df['no_of_adults'].fillna(df['no_of_adults'].mode()[0], inplace = True)\n",
        "df['no_of_children'].fillna(df['no_of_children'].mode()[0], inplace = True)"
      ],
      "metadata": {
        "id": "qWi93vIWzDyM"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3uX_41aczD__",
        "outputId": "35a5de31-a48c-4bce-c17a-42206500a097"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "h1n1_worry                   0\n",
              "h1n1_awareness               0\n",
              "antiviral_medication         0\n",
              "contact_avoidance            0\n",
              "bought_face_mask             0\n",
              "wash_hands_frequently        0\n",
              "avoid_large_gatherings       0\n",
              "reduced_outside_home_cont    0\n",
              "avoid_touch_face             0\n",
              "dr_recc_h1n1_vacc            0\n",
              "dr_recc_seasonal_vacc        0\n",
              "chronic_medic_condition      0\n",
              "cont_child_undr_6_mnths      0\n",
              "is_health_worker             0\n",
              "has_health_insur             0\n",
              "is_h1n1_vacc_effective       0\n",
              "is_h1n1_risky                0\n",
              "sick_from_h1n1_vacc          0\n",
              "is_seas_vacc_effective       0\n",
              "is_seas_risky                0\n",
              "sick_from_seas_vacc          0\n",
              "age_bracket                  0\n",
              "qualification                0\n",
              "race                         0\n",
              "sex                          0\n",
              "income_level                 0\n",
              "marital_status               0\n",
              "housing_status               0\n",
              "employment                   0\n",
              "census_msa                   0\n",
              "no_of_adults                 0\n",
              "no_of_children               0\n",
              "h1n1_vaccine                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# No outliers as data is categorical"
      ],
      "metadata": {
        "id": "ZgSgmRhozEM4"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df.astype({'h1n1_worry' : str, 'h1n1_awareness': str, 'antiviral_medication': str,\n",
        "       'contact_avoidance': str, 'bought_face_mask': str, 'wash_hands_frequently': str,\n",
        "       'avoid_large_gatherings': str, 'reduced_outside_home_cont': str,\n",
        "       'avoid_touch_face': str, 'dr_recc_h1n1_vacc': str, 'dr_recc_seasonal_vacc': str,\n",
        "       'chronic_medic_condition': str, 'cont_child_undr_6_mnths': str,\n",
        "       'is_health_worker': str, 'has_health_insur': str, 'is_h1n1_vacc_effective': str,\n",
        "       'is_h1n1_risky': str, 'sick_from_h1n1_vacc': str, 'is_seas_vacc_effective': str, 'age_bracket' : str,\n",
        "       'is_seas_risky': str, 'sick_from_seas_vacc': str, 'no_of_adults' : str, 'no_of_children' : str,\n",
        "        'h1n1_vaccine' : str})\n",
        "df1.dtypes.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Of337-YzEZq",
        "outputId": "f8b93d6f-b1be-465f-efb0-c3a44ab0f8a0"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "object    33\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_dummy = pd.get_dummies(df1, drop_first= True )"
      ],
      "metadata": {
        "id": "H3cL0DVd6l2z"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_dummy.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0xa4oOdq6mTj",
        "outputId": "6816f3c1-5715-432d-8bf3-b154f0535336"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(26707, 70)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_dummy['h1n1_vaccine_1'].value_counts(normalize = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yTVe7Gjp6mhZ",
        "outputId": "d2caa51a-f124-416a-cb25-3156d7d0132a"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.787546\n",
              "1    0.212454\n",
              "Name: h1n1_vaccine_1, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=df_dummy['h1n1_vaccine_1']\n",
        "x=df_dummy.drop(['h1n1_vaccine_1'], axis=1)"
      ],
      "metadata": {
        "id": "sSrNA9kh6ms_"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "LF4Yl9oY7bov"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train , y_test=train_test_split(x,y, test_size=.25, random_state=88)"
      ],
      "metadata": {
        "id": "p9jKk_8D6m5R"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Logistic Regression**"
      ],
      "metadata": {
        "id": "YFNmvofR8DpS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()\n",
        "\n",
        "model.fit(x_train,y_train)\n",
        "\n",
        "print(f\"Training Score : {model.score(x_train, y_train)}\")\n",
        "print(f\"Testing Score : {model.score(x_test, y_test)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kRXpWuCM6nSS",
        "outputId": "5edce929-b473-4eef-9398-67847ad9ecc5"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Score : 0.8558162755866201\n",
            "Testing Score : 0.8442414257900255\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Evaluation"
      ],
      "metadata": {
        "id": "8y_gkM4o6neG"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred_train=model.predict(x_train)\n",
        "pred_test=model.predict(x_test)"
      ],
      "metadata": {
        "id": "v93mVaMl6nov"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnfTrain=pd.DataFrame(metrics.confusion_matrix(y_train , pred_train), columns=[\"Pred_0\", \"Pred_1\"],\n",
        "                index=[\"Act_0\", \"Act_1\"])\n",
        "cnfTrain"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "UtUvFSIS9EQm",
        "outputId": "ebbc8e50-5671-4a2a-ee2f-3ef8990efe88"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Pred_0  Pred_1\n",
              "Act_0   15031     773\n",
              "Act_1    2115    2111"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-3e74f2b0-7fc0-4dd4-b0fc-738985d53406\">\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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              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-a3eb484d-3b8a-43a9-9f35-0470dc707119 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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-3e74f2b0-7fc0-4dd4-b0fc-738985d53406 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-3e74f2b0-7fc0-4dd4-b0fc-738985d53406');\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": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(y_train, pred_train))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l3rynqf99EeO",
        "outputId": "9b9b091f-aa8e-4d19-ab7f-00d4543c9972"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.95      0.91     15804\n",
            "           1       0.73      0.50      0.59      4226\n",
            "\n",
            "    accuracy                           0.86     20030\n",
            "   macro avg       0.80      0.73      0.75     20030\n",
            "weighted avg       0.85      0.86      0.85     20030\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Decision Tree Classifier**"
      ],
      "metadata": {
        "id": "DPRF6ILC-qEK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "model_2=DecisionTreeClassifier(max_depth=3)\n",
        "model_2.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "6xyYFKio9Eov",
        "outputId": "76e987b0-36ba-41ab-d239-e59bbbb8b324"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=3)"
            ],
            "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>DecisionTreeClassifier(max_depth=3)</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\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=3)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2.score(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xIeS0-wF9Ex1",
        "outputId": "c49d1312-24f8-439f-f90a-7cb61ca7b3d5"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8256115826260609"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2.score(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rf8QciKD9FZo",
        "outputId": "2d0cf2d1-0557-4b57-a4ed-1ff1c5e8498d"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8126404073685787"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We can see that there is no overfitting in this model\n",
        "#The difference b/w train & test should be <lesser than =or equal to 5%"
      ],
      "metadata": {
        "id": "UyPtpSVy9Fh1"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Bagging Classification Model**"
      ],
      "metadata": {
        "id": "IOv_BqZ6_eXR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import BaggingClassifier\n",
        "model_3=BaggingClassifier(n_estimators=90,base_estimator=model_2)\n",
        "model_3.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        },
        "id": "cqu2IZCx9Gfj",
        "outputId": "42ba7083-43d3-4c4f-9712-80c9408c7664"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BaggingClassifier(base_estimator=DecisionTreeClassifier(max_depth=3),\n",
              "                  n_estimators=90)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BaggingClassifier(base_estimator=DecisionTreeClassifier(max_depth=3),\n",
              "                  n_estimators=90)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(base_estimator=DecisionTreeClassifier(max_depth=3),\n",
              "                  n_estimators=90)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=3)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=3)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_3.score(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BsheBs_k_pwp",
        "outputId": "1f201569-7821-486f-f237-7891e6d18edf"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8274588117823265"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_3.score(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-rSmXWLq_qA1",
        "outputId": "dc39bcae-3981-421d-f71f-12620a3f379e"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8139883181069343"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **AdaBoost Classification Model**"
      ],
      "metadata": {
        "id": "P8_sPeN-__E_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "model_4=AdaBoostClassifier(n_estimators=25)\n",
        "model_4.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "8jxurhhh_qMM",
        "outputId": "ddf77659-0681-4978-b613-f26a537c8945"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier(n_estimators=25)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(n_estimators=25)</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(n_estimators=25)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_4.score(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ST9ERxbp_qXi",
        "outputId": "34779eb3-9f4f-4df3-b8fe-359246806439"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8498252621068397"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_4.score(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w6EIgiN0_qjC",
        "outputId": "8f0dd871-1a77-46cf-89e4-04be49e21c99"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8381009435375169"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **GradientBoosting Classification Model**"
      ],
      "metadata": {
        "id": "AgU2EXzjAkZC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "model_5=GradientBoostingClassifier()\n",
        "model_5.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "qWHSV2v7_quk",
        "outputId": "a91b044d-a412-4648-e898-87ee7bb73cdf"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier()</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_5.score(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e7-5OZ2K_q4-",
        "outputId": "eea2a4ea-9342-412e-dce1-196590a2ddec"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8616575137294059"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_5.score(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nxD4PxM9_rEN",
        "outputId": "5d9a22ab-165c-416a-dab6-a5efc6594a9f"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8440916579302081"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **RandomForest Classification Model**"
      ],
      "metadata": {
        "id": "cDuFnGVtA1j-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "model_6=RandomForestClassifier(n_estimators=1,max_features=1)\n",
        "model_6.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "qLn574Jl_rOY",
        "outputId": "8e8507c2-56e8-41dc-be31-d5fd23612881"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_features=1, n_estimators=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 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-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 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-11 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-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 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-11 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-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 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-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 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-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 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-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_features=1, n_estimators=1)</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-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_features=1, n_estimators=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_6.score(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2sVKPYh2_rWk",
        "outputId": "bbe235eb-5c8e-42f1-c7b6-e69f35ab09a2"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8992511233150274"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_5.score(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g_ktYvad_r78",
        "outputId": "1694209b-6126-4154-ff42-1ea1fa6994c5"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8440916579302081"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    }
  ]
}