Team_notebook.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/rutujahingankar/Health-insurance-cross-sell-prediction/blob/main/Team_notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LGcl5Bv9ed6u"
      },
      "source": [
        "# **Problem Statement**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jJAmGx75jJk1"
      },
      "source": [
        "Our client is an Insurance company that has provided Health Insurance to its customers now they need your help in building a model to predict whether the policyholders (customers) from past year will also be interested in Vehicle Insurance provided by the company.\n",
        "\n",
        "An insurance policy is an arrangement by which a company undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for the payment of a specified premium. A premium is a sum of money that the customer needs to pay regularly to an insurance company for this guarantee.\n",
        "\n",
        "For example, you may pay a premium of Rs. 5000 each year for a health insurance cover of Rs. 200,000/- so that if, God forbid, you fall ill and need to be hospitalised in that year, the insurance provider company will bear the cost of hospitalisation etc. for upto Rs. 200,000. Now if you are wondering how can company bear such high hospitalisation cost when it charges a premium of only Rs. 5000/-, that is where the concept of probabilities comes in picture. For example, like you, there may be 100 customers who would be paying a premium of Rs. 5000 every year, but only a few of them (say 2-3) would get hospitalised that year and not everyone. This way everyone shares the risk of everyone else.\n",
        "\n",
        "Just like medical insurance, there is vehicle insurance where every year customer needs to pay a premium of certain amount to insurance provider company so that in case of unfortunate accident by the vehicle, the insurance provider company will provide a compensation (called ‘sum assured’) to the customer.\n",
        "\n",
        "Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimise its business model and revenue.\n",
        "\n",
        "Now, in order to predict, whether the customer would be interested in Vehicle insurance, you have information about demographics (gender, age, region code type), Vehicles (Vehicle Age, Damage), Policy (Premium, sourcing channel) etc."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AzGDqdC4fZ-b"
      },
      "source": [
        "# **Attribute Information**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oEJxl68MjYbs"
      },
      "source": [
        "1. id :\tUnique ID for the customer\n",
        "\n",
        "2. Gender\t: Gender of the customer\n",
        "\n",
        "3. Age :\tAge of the customer\n",
        "\n",
        "4. Driving_License\t0 : Customer does not have DL, 1 : Customer already has DL\n",
        "\n",
        "5. Region_Code :\tUnique code for the region of the customer\n",
        "\n",
        "6. Previously_Insured\t: 1 : Customer already has Vehicle Insurance, 0 : Customer doesn't have Vehicle Insurance\n",
        "\n",
        "7. Vehicle_Age :\tAge of the Vehicle\n",
        "\n",
        "8. Vehicle_Damage\t :1 : Customer got his/her vehicle damaged in the past. 0 : Customer didn't get his/her vehicle damaged in the past.\n",
        "\n",
        "9. Annual_Premium\t: The amount customer needs to pay as premium in the year\n",
        "\n",
        "10. PolicySalesChannel :\tAnonymized Code for the channel of outreaching to the customer ie. Different Agents, Over Mail, Over Phone, In Person, etc.\n",
        "\n",
        "11. Vintage :\tNumber of Days, Customer has been associated with the company\n",
        "\n",
        "12. Response :\t1 : Customer is interested, 0 : Customer is not interested"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading Dataset"
      ],
      "metadata": {
        "id": "ps0GjhVBdR_N"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7W7Rl8yEDDtY"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from collections import Counter\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from xgboost import XGBClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5SVRlkqeDemP",
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        },
        "outputId": "13a65dd9-0439-412d-e731-9cefe9a35c7d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NGDq6hP7DDvY",
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          "height": 206
        },
        "outputId": "d61ef030-590b-4f0b-ddcd-03e55b45f1e9"
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
              "0   1    Male   44                1         28.0                   0   \n",
              "1   2    Male   76                1          3.0                   0   \n",
              "2   3    Male   47                1         28.0                   0   \n",
              "3   4    Male   21                1         11.0                   1   \n",
              "4   5  Female   29                1         41.0                   1   \n",
              "\n",
              "  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n",
              "0   > 2 Years            Yes         40454.0                  26.0      217   \n",
              "1    1-2 Year             No         33536.0                  26.0      183   \n",
              "2   > 2 Years            Yes         38294.0                  26.0       27   \n",
              "3    < 1 Year             No         28619.0                 152.0      203   \n",
              "4    < 1 Year             No         27496.0                 152.0       39   \n",
              "\n",
              "   Response  \n",
              "0         1  \n",
              "1         0  \n",
              "2         1  \n",
              "3         0  \n",
              "4         0  "
            ],
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              "      <td>5</td>\n",
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              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>41.0</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt; 1 Year</td>\n",
              "      <td>No</td>\n",
              "      <td>27496.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>39</td>\n",
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          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "#reading dataset\n",
        "df=pd.read_csv('/content/drive/MyDrive/TRAIN-HEALTH INSURANCE CROSS SELL PREDICTION.csv')\n",
        "df.head()"
      ]
    },
    {
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          "height": 300
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        "outputId": "f671392d-be17-4f8b-d971-952fee19d4a8"
      },
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  id            Age  Driving_License    Region_Code  \\\n",
              "count  381109.000000  381109.000000    381109.000000  381109.000000   \n",
              "mean   190555.000000      38.822584         0.997869      26.388807   \n",
              "std    110016.836208      15.511611         0.046110      13.229888   \n",
              "min         1.000000      20.000000         0.000000       0.000000   \n",
              "25%     95278.000000      25.000000         1.000000      15.000000   \n",
              "50%    190555.000000      36.000000         1.000000      28.000000   \n",
              "75%    285832.000000      49.000000         1.000000      35.000000   \n",
              "max    381109.000000      85.000000         1.000000      52.000000   \n",
              "\n",
              "       Previously_Insured  Annual_Premium  Policy_Sales_Channel  \\\n",
              "count       381109.000000   381109.000000         381109.000000   \n",
              "mean             0.458210    30564.389581            112.034295   \n",
              "std              0.498251    17213.155057             54.203995   \n",
              "min              0.000000     2630.000000              1.000000   \n",
              "25%              0.000000    24405.000000             29.000000   \n",
              "50%              0.000000    31669.000000            133.000000   \n",
              "75%              1.000000    39400.000000            152.000000   \n",
              "max              1.000000   540165.000000            163.000000   \n",
              "\n",
              "             Vintage       Response  \n",
              "count  381109.000000  381109.000000  \n",
              "mean      154.347397       0.122563  \n",
              "std        83.671304       0.327936  \n",
              "min        10.000000       0.000000  \n",
              "25%        82.000000       0.000000  \n",
              "50%       154.000000       0.000000  \n",
              "75%       227.000000       0.000000  \n",
              "max       299.000000       1.000000  "
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              "      <th>Age</th>\n",
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              "      <th>count</th>\n",
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              "      <td>381109.000000</td>\n",
              "      <td>381109.000000</td>\n",
              "      <td>381109.000000</td>\n",
              "      <td>381109.000000</td>\n",
              "      <td>381109.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>190555.000000</td>\n",
              "      <td>38.822584</td>\n",
              "      <td>0.997869</td>\n",
              "      <td>26.388807</td>\n",
              "      <td>0.458210</td>\n",
              "      <td>30564.389581</td>\n",
              "      <td>112.034295</td>\n",
              "      <td>154.347397</td>\n",
              "      <td>0.122563</td>\n",
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              "      <th>std</th>\n",
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              "      <td>13.229888</td>\n",
              "      <td>0.498251</td>\n",
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              "      <td>54.203995</td>\n",
              "      <td>83.671304</td>\n",
              "      <td>0.327936</td>\n",
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              "      <th>25%</th>\n",
              "      <td>95278.000000</td>\n",
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              "      <td>15.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>24405.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>82.000000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>190555.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31669.000000</td>\n",
              "      <td>133.000000</td>\n",
              "      <td>154.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>285832.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>39400.000000</td>\n",
              "      <td>152.000000</td>\n",
              "      <td>227.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>381109.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>540165.000000</td>\n",
              "      <td>163.000000</td>\n",
              "      <td>299.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29e058a5-2406-457b-90db-293137f9beb0')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-29e058a5-2406-457b-90db-293137f9beb0 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-29e058a5-2406-457b-90db-293137f9beb0');\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": 4
        }
      ],
      "source": [
        "#Descriptive statistics\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NZHpMad6DD2W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd9caee2-992d-4868-d2cd-f57da7b309b4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(381109, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "#Shape of data\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d_VMYhiCDD5T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d120bc5-2618-4f69-89ec-ad7b17842e92"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'Gender', 'Age', 'Driving_License', 'Region_Code',\n",
              "       'Previously_Insured', 'Vehicle_Age', 'Vehicle_Damage', 'Annual_Premium',\n",
              "       'Policy_Sales_Channel', 'Vintage', 'Response'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FLDUF7GaDD7v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a203a7e-5d78-4bc4-bd3c-37b64360e1f6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                      0\n",
              "Gender                  0\n",
              "Age                     0\n",
              "Driving_License         0\n",
              "Region_Code             0\n",
              "Previously_Insured      0\n",
              "Vehicle_Age             0\n",
              "Vehicle_Damage          0\n",
              "Annual_Premium          0\n",
              "Policy_Sales_Channel    0\n",
              "Vintage                 0\n",
              "Response                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "#checking missing values\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6TjVmkV_I-wC"
      },
      "source": [
        "* Our dataset have\n",
        " no null value."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1Mze77kDJTlG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "3c6bca48-65e1-42e8-c83e-13b22d3f2f41"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "            id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
              "0            1    Male   44                1         28.0                   0   \n",
              "1            2    Male   76                1          3.0                   0   \n",
              "2            3    Male   47                1         28.0                   0   \n",
              "3            4    Male   21                1         11.0                   1   \n",
              "4            5  Female   29                1         41.0                   1   \n",
              "...        ...     ...  ...              ...          ...                 ...   \n",
              "381104  381105    Male   74                1         26.0                   1   \n",
              "381105  381106    Male   30                1         37.0                   1   \n",
              "381106  381107    Male   21                1         30.0                   1   \n",
              "381107  381108  Female   68                1         14.0                   0   \n",
              "381108  381109    Male   46                1         29.0                   0   \n",
              "\n",
              "       Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  \\\n",
              "0        > 2 Years            Yes         40454.0                  26.0   \n",
              "1         1-2 Year             No         33536.0                  26.0   \n",
              "2        > 2 Years            Yes         38294.0                  26.0   \n",
              "3         < 1 Year             No         28619.0                 152.0   \n",
              "4         < 1 Year             No         27496.0                 152.0   \n",
              "...            ...            ...             ...                   ...   \n",
              "381104    1-2 Year             No         30170.0                  26.0   \n",
              "381105    < 1 Year             No         40016.0                 152.0   \n",
              "381106    < 1 Year             No         35118.0                 160.0   \n",
              "381107   > 2 Years            Yes         44617.0                 124.0   \n",
              "381108    1-2 Year             No         41777.0                  26.0   \n",
              "\n",
              "        Vintage  Response  \n",
              "0           217         1  \n",
              "1           183         0  \n",
              "2            27         1  \n",
              "3           203         0  \n",
              "4            39         0  \n",
              "...         ...       ...  \n",
              "381104       88         0  \n",
              "381105      131         0  \n",
              "381106      161         0  \n",
              "381107       74         0  \n",
              "381108      237         0  \n",
              "\n",
              "[381109 rows x 12 columns]"
            ],
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              "\n",
              "  <div id=\"df-b1eb5a96-cb8b-4d38-803b-b79c8d8a5bf4\">\n",
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              "      <th>id</th>\n",
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              "      <th>Driving_License</th>\n",
              "      <th>Region_Code</th>\n",
              "      <th>Previously_Insured</th>\n",
              "      <th>Vehicle_Age</th>\n",
              "      <th>Vehicle_Damage</th>\n",
              "      <th>Annual_Premium</th>\n",
              "      <th>Policy_Sales_Channel</th>\n",
              "      <th>Vintage</th>\n",
              "      <th>Response</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>&gt; 2 Years</td>\n",
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              "      <td>40454.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>76</td>\n",
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              "      <th>2</th>\n",
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              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>38294.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt; 1 Year</td>\n",
              "      <td>No</td>\n",
              "      <td>28619.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>203</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>41.0</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt; 1 Year</td>\n",
              "      <td>No</td>\n",
              "      <td>27496.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381104</th>\n",
              "      <td>381105</td>\n",
              "      <td>Male</td>\n",
              "      <td>74</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1-2 Year</td>\n",
              "      <td>No</td>\n",
              "      <td>30170.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>88</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381105</th>\n",
              "      <td>381106</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>37.0</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt; 1 Year</td>\n",
              "      <td>No</td>\n",
              "      <td>40016.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>131</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381106</th>\n",
              "      <td>381107</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt; 1 Year</td>\n",
              "      <td>No</td>\n",
              "      <td>35118.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>161</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381107</th>\n",
              "      <td>381108</td>\n",
              "      <td>Female</td>\n",
              "      <td>68</td>\n",
              "      <td>1</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>&gt; 2 Years</td>\n",
              "      <td>Yes</td>\n",
              "      <td>44617.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381108</th>\n",
              "      <td>381109</td>\n",
              "      <td>Male</td>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1-2 Year</td>\n",
              "      <td>No</td>\n",
              "      <td>41777.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>237</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>381109 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1eb5a96-cb8b-4d38-803b-b79c8d8a5bf4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      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",
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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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b1eb5a96-cb8b-4d38-803b-b79c8d8a5bf4 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-b1eb5a96-cb8b-4d38-803b-b79c8d8a5bf4');\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": {}
        }
      ],
      "source": [
        "#Check for duplicated values\n",
        "display(df.drop_duplicates())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8kqrd008JTnA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43f1bb38-7cf8-4024-e9ca-6726218224d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 381109 entries, 0 to 381108\n",
            "Data columns (total 12 columns):\n",
            " #   Column                Non-Null Count   Dtype  \n",
            "---  ------                --------------   -----  \n",
            " 0   id                    381109 non-null  int64  \n",
            " 1   Gender                381109 non-null  object \n",
            " 2   Age                   381109 non-null  int64  \n",
            " 3   Driving_License       381109 non-null  int64  \n",
            " 4   Region_Code           381109 non-null  float64\n",
            " 5   Previously_Insured    381109 non-null  int64  \n",
            " 6   Vehicle_Age           381109 non-null  object \n",
            " 7   Vehicle_Damage        381109 non-null  object \n",
            " 8   Annual_Premium        381109 non-null  float64\n",
            " 9   Policy_Sales_Channel  381109 non-null  float64\n",
            " 10  Vintage               381109 non-null  int64  \n",
            " 11  Response              381109 non-null  int64  \n",
            "dtypes: float64(3), int64(6), object(3)\n",
            "memory usage: 34.9+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W1jkdo37rNej"
      },
      "source": [
        "#Data Visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Univariate Analysis"
      ],
      "metadata": {
        "id": "f4PamjbV0-X8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D6RHZvkGJTph",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "outputId": "5217b94d-2114-46c7-d87a-57ce7f5da3c3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faacc9da650>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Dependent variable 'Response'\n",
        "plt.figure(figsize=(8,7))\n",
        "sns.set_theme(style='whitegrid')\n",
        "sns.countplot(x=df['Response'],data=df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aisfwXRM5o5C"
      },
      "source": [
        "* From above fig we can see that the data is highly imbalanced."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tokK-zFLJTso",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "outputId": "6dfcd809-2fbc-4d3e-d61b-30ee0f0469f0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faacc493750>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Distribution of Age \n",
        "plt.figure(figsize=(15,8))\n",
        "sns.countplot(x=df['Age'],data=df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SlDtkxcD5pl7"
      },
      "source": [
        "* From the above distribution of age we can see that most of the customers age is between 21 to 25 years.There are few Customers above the age of 60 years."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SPcPEuuWJTwE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "5e6d938e-6606-4b2f-b37e-bbf5d60d63b1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "plt.figure(figsize=(7,9))\n",
        "plt.pie(df['Previously_Insured'].value_counts(), autopct='%.0f%%', shadow=True, startangle=200, explode=[0.01,0])\n",
        "plt.legend(labels=['Insured','Not insured'])\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KE4kcB2rRGZs"
      },
      "source": [
        "* 54% customer are previously insured ahe 46% customer are are not insured yet.\n",
        "* Customer who are not perviosly insured are likely to be inetrested."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vZ7EiUDDMmC8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "outputId": "5ad536e2-6392-4c3c-af21-fcb6ceb5d82c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faacc267950>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(15,9))\n",
        "a=df['Annual_Premium']\n",
        "sns.distplot(a, color='purple')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* From the distribution plot we can infer that the annual premimum variable is right skewed"
      ],
      "metadata": {
        "id": "zhTqJ-Ir1Pdl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h_Lw2hKDMmJC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "e9418183-89b7-409a-a907-0cc4c10c1577"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faacc13fad0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.boxplot(df['Annual_Premium'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OYecW7Zc39fC"
      },
      "source": [
        "*  For the boxplot above we can see that there's a lot of outliers in the annual premium."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZmfWVhcFwbUg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "outputId": "ab63005b-6243-4604-b8cf-b7835a01ccb4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faac98b2890>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(5,7))\n",
        "sns.countplot(x=df['Vehicle_Damage'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Customers with Vehicle_Damage are likely to buy insurance"
      ],
      "metadata": {
        "id": "Iyz45Zch1yBW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qTZCMa_xwbX5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "b40a8ece-0068-495a-9ec2-f6b868c6f4a2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df['Vehicle_Age'].hist();"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* From the above plot we can see that most of the people are having vehicle age between 1 or 2 years and very few people are having vehicle age more than 2 years. "
      ],
      "metadata": {
        "id": "k2PX1SwepRBr"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4vEshKR8GClh"
      },
      "source": [
        "#Bivariate analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8qfdFp51F9w9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "outputId": "d0e3d318-3c33-4e72-c191-4a9ec4faa194"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Age VS Response\n",
        "plt.figure(figsize=(16,8))\n",
        "sns.countplot(data=df, x='Age',hue='Response', palette='CMRmap_r')\n",
        "plt.xlabel('Age response')\n",
        "plt.ylabel('count')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vlCRh6HFquJL"
      },
      "source": [
        "* People ages between from 31 to 50 are more likely to respond. \n",
        "*  while Young people below 30 are not interested in vehicle insurance. \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6vC4W-IMF93F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "f5c3f8ee-6be4-4b63-908b-54f6e2721d25"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faac97f8f50>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Gender vs Response\n",
        "df.groupby(['Gender', 'Response']).size().unstack().plot(kind = 'bar', stacked = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Male category is slightly greater than that of female and chances of buying the insurance is also little high"
      ],
      "metadata": {
        "id": "rZKyC-rJf_ia"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (10,6) )\n",
        "sns.countplot(data = df, x = 'Vehicle_Age', hue = 'Response', palette='Dark2_r')\n",
        "plt.xlabel('Vehicle Age', fontsize = 15)\n",
        "plt.ylabel('Count', fontsize = 15)\n",
        "plt.title('Vehicle Age and Customer Response analysis', fontsize = 19)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "SwQtaCg19h8X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "4972faf5-c8ba-44f7-95e7-b268ad5fa259"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Customers with vechicle age 1-2 years are more likely to interested as compared to the other two\n",
        "\n",
        "* Customers with with Vehicle_Age <1 years have very less chance of buying Insurance"
      ],
      "metadata": {
        "id": "WMKVrr2Co8h-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x = 'Response', y ='Annual_Premium', data = df)"
      ],
      "metadata": {
        "id": "1JankwCh9iAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "e49f3fe0-b55f-474d-933e-32e2e47fefb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faac9416810>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*  People who response have slightly higher annual premium "
      ],
      "metadata": {
        "id": "u8kSEwPuqoqy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sNcsHxMbF-DI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "outputId": "48fbd25e-b6d0-47ad-da9d-2f5bfd54180b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faad6021550>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (20, 8))\n",
        "sns.heatmap(df.corr(), annot = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Target variable is not much affected by Vintage variable. we can drop least correlated variable."
      ],
      "metadata": {
        "id": "i6h_e4LBqDVU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Encoding Object columns\n",
        "* changing categorical value to numerical values"
      ],
      "metadata": {
        "id": "EUd_mZDcqgma"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Gender'] = df['Gender'].map({'Female':1, 'Male':0})\n",
        "df.head()"
      ],
      "metadata": {
        "id": "06wp_gsyqfwp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "c85b381d-76f7-4c07-f096-c6f8d69e67de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
              "0   1       0   44                1         28.0                   0   \n",
              "1   2       0   76                1          3.0                   0   \n",
              "2   3       0   47                1         28.0                   0   \n",
              "3   4       0   21                1         11.0                   1   \n",
              "4   5       1   29                1         41.0                   1   \n",
              "\n",
              "  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n",
              "0   > 2 Years            Yes         40454.0                  26.0      217   \n",
              "1    1-2 Year             No         33536.0                  26.0      183   \n",
              "2   > 2 Years            Yes         38294.0                  26.0       27   \n",
              "3    < 1 Year             No         28619.0                 152.0      203   \n",
              "4    < 1 Year             No         27496.0                 152.0       39   \n",
              "\n",
              "   Response  \n",
              "0         1  \n",
              "1         0  \n",
              "2         1  \n",
              "3         0  \n",
              "4         0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a3fc6b0f-5c82-4bb5-af7c-97ee772d4ff8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
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              "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
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              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>33536.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>183</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>38294.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>28619.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>203</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>41.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>27496.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f2acc706-12c9-4218-be22-1c1ab7d5eb8d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f2acc706-12c9-4218-be22-1c1ab7d5eb8d 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-f2acc706-12c9-4218-be22-1c1ab7d5eb8d');\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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Feature Engineering and Feature Selection"
      ],
      "metadata": {
        "id": "IVDbR9xkmu2X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "correlation = df.corr()\n",
        "correlation['Response'].sort_values(ascending = False)[1:]"
      ],
      "metadata": {
        "id": "ij1OeM93yz9V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "97ac27eb-94c6-4eee-b2f1-755f7dfe56af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Vehicle_Damage          0.354400\n",
              "Vehicle_Age             0.221874\n",
              "Age                     0.111147\n",
              "Annual_Premium          0.022575\n",
              "Region_Code             0.010570\n",
              "Driving_License         0.010155\n",
              "Vintage                -0.001050\n",
              "id                     -0.001368\n",
              "Gender                 -0.052440\n",
              "Policy_Sales_Channel   -0.139042\n",
              "Previously_Insured     -0.341170\n",
              "Name: Response, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.drop(columns=['id','Driving_License','Policy_Sales_Channel','Vintage','Response'])# independent variable\n",
        "y = df['Response']# dependent variable"
      ],
      "metadata": {
        "id": "70sDv5zojTaw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fill any numerical NaNs with mode()\n",
        "\n",
        "fill_mode = lambda col: col.fillna(col.mode())\n",
        "X = X.apply(fill_mode, axis=0)\n",
        "df = df.apply(fill_mode, axis=0)"
      ],
      "metadata": {
        "id": "ZP3ilC7r8hZS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model Building"
      ],
      "metadata": {
        "id": "yNSb8zTs1Pr-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check for imbalance in data \n",
        "df['Response'].value_counts()"
      ],
      "metadata": {
        "id": "k4iAuPyljTf7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "746eeb09-898a-4e51-ca14-c42b9b760bc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    334399\n",
              "1     46710\n",
              "Name: Response, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "+* We can clearly see that there is a huge difference between the data set. \n",
        "* Standard ML techniques such as Decision Tree and Logistic Regression have a bias towards the majority class, and they tend to ignore the minority class. So solving this issue we use resampling technique.\n"
      ],
      "metadata": {
        "id": "cj_NpsbapGqf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Resampling\n",
        "ros = RandomOverSampler(random_state=0)\n",
        "X_new,y_new= ros.fit_resample(X, y)\n",
        "\n",
        "print(\"After Random Over Sampling Of Minor Class Total Samples are :\", len(y_new))\n",
        "print('Original dataset shape {}'.format(Counter(y)))\n",
        "print('Resampled dataset shape {}'.format(Counter(y_new)))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CfVYkVvKPePk",
        "outputId": "416ba825-01da-4ea6-af41-4fb0b5f34739"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "After Random Over Sampling Of Minor Class Total Samples are : 668798\n",
            "Original dataset shape Counter({0: 334399, 1: 46710})\n",
            "Resampled dataset shape Counter({1: 334399, 0: 334399})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Splitting the data in train and test sets"
      ],
      "metadata": {
        "id": "hxZSkqHTvSCb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test ,y_train, y_test=  train_test_split(X_new, y_new, random_state=42, test_size=0.3)\n",
        "X_train.shape, X_test.shape , y_train.shape, y_test.shape"
      ],
      "metadata": {
        "id": "LqfFmzFkxUoo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "187dee76-80a1-4765-ba75-ebf013c4dd1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((468158, 7), (200640, 7), (468158,), (200640,))"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalizing the Dataset using Standard Scaling Technique.\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler()\n",
        "X_train=scaler.fit_transform(X_train)\n",
        "X_test=scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "N4JMuxtAQr9Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Logistic Regression"
      ],
      "metadata": {
        "id": "enxfxI_PAkPR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Importing Logistic Regression\n",
        "model= LogisticRegression(random_state=42)\n",
        "model=model.fit(X_train, y_train)\n",
        "#Making prediction\n",
        "pred = model.predict(X_test)\n",
        "prob = model.predict_proba(X_test)[:,1]"
      ],
      "metadata": {
        "id": "G_kYObX8xUsS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model** **Evaluation**"
      ],
      "metadata": {
        "id": "wlkDPz3FwXKB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation\n",
        "r_lgt= recall_score(y_test, pred)\n",
        "print(\"recall_score : \", r_lgt)\n",
        "\n",
        "p_lgt= precision_score(y_test, pred)\n",
        "print(\"precision_score :\",p_lgt)\n",
        "\n",
        "f1_lgt= f1_score(y_test, pred)\n",
        "print(\"f1_score :\", f1_lgt)\n",
        "\n",
        "A_lgt= accuracy_score(pred, y_test)\n",
        "print(\"accuracy_score :\",A_lgt)\n",
        "\n",
        "acu_lgt = roc_auc_score(pred, y_test)\n",
        "print(\"ROC_AUC Score:\",acu_lgt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-JPolsOmc3Qx",
        "outputId": "4f9fcf12-b716-4d08-b2ab-3c32a4a5868e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall_score :  0.976100453713285\n",
            "precision_score : 0.7071892503658422\n",
            "f1_score : 0.8201651165221027\n",
            "accuracy_score : 0.7855811403508772\n",
            "ROC_AUC Score: 0.8341983171030103\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "fpr, tpr, _ = roc_curve(y_test, prob)\n",
        "\n",
        "plt.title('Logistic Regression ROC curve')\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.plot(fpr,tpr)\n",
        "plt.plot((0,1), linestyle=\"--\",color='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "pp55Vpp_r4R6",
        "outputId": "9765cc3d-55a1-4204-b64d-bfd76cb39133"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**confusion_matrix**"
      ],
      "metadata": {
        "id": "Db8u8ZG7rHgl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matrix= confusion_matrix(y_test, pred)\n",
        "print(matrix)\n",
        "sns.heatmap(matrix ,annot=True, fmt='g')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "f0QQ1je0xUvP",
        "outputId": "437d3bf7-cf67-4e97-8e2d-fad64acc401f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[59517 40619]\n",
            " [ 2402 98102]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faacca3fd90>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* From the confusion matrix we see that the model is predicting positive responses but also predicting negative response too."
      ],
      "metadata": {
        "id": "yeDxFIxbGuMx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(pred, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fUBJ9Un8xUyo",
        "outputId": "5966229f-5312-4e8b-e134-9c38f3eeef42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.59      0.96      0.73     61919\n",
            "           1       0.98      0.71      0.82    138721\n",
            "\n",
            "    accuracy                           0.79    200640\n",
            "   macro avg       0.79      0.83      0.78    200640\n",
            "weighted avg       0.86      0.79      0.79    200640\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#RandomForest Classifier"
      ],
      "metadata": {
        "id": "S8vUr5C4inMg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "RF_model= RandomForestClassifier()\n",
        "RF_model= RF_model.fit(X_train, y_train)\n",
        "#Making prediction\n",
        "rf_pred= RF_model.predict(X_test)\n",
        "rf_proba= RF_model.predict_proba(X_test)[:,1]"
      ],
      "metadata": {
        "id": "Ukoc7CQkxU40"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Evaluation**"
      ],
      "metadata": {
        "id": "i_FvcFN1q0_W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation\n",
        "r_rf=  recall_score(y_test, rf_pred)\n",
        "print(\"recall_score : \", r_rf)\n",
        "\n",
        "p_rf= precision_score(y_test, rf_pred)\n",
        "print(\"precision_score :\",p_rf)\n",
        "\n",
        "f1_rf= f1_score(y_test, rf_pred)\n",
        "print(\"f1_score :\", f1_rf)\n",
        "\n",
        "A_rf= accuracy_score(y_test, rf_pred)\n",
        "print(\"accuracy_score :\",A_rf)\n",
        "\n",
        "acu_rf = roc_auc_score(rf_pred, y_test)\n",
        "print(\"ROC_AUC Score:\",acu_rf)"
      ],
      "metadata": {
        "id": "uZSBq2BCxU8R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96617f7c-f94b-4450-9890-c4f8fd80ca85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall_score :  0.9852742179415744\n",
            "precision_score : 0.8639631465065959\n",
            "f1_score : 0.9206396429899592\n",
            "accuracy_score : 0.9149122807017543\n",
            "ROC_AUC Score: 0.9233793227185634\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fpr, tpr, _ = roc_curve(y_test, rf_proba)\n",
        "\n",
        "plt.title('Random Forest ROC curve')\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.plot(fpr,tpr)\n",
        "plt.plot((0,1), linestyle=\"--\",color='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "Dnn6FqmGvOK_",
        "outputId": "79f52c89-54a9-4e03-cb06-72203d3350f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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FixaxeM5MHBwcWL16NQMHDuT06dN5anNaWtoTv1xbTZFo06YNW7ZsoXXr1iQkJLB//36+/vprtWNZrfQMPYdCb3Lw9E3O/hkDQAlHO6q4lSKoSRUqVSiJc6liPONoh6ODLaWeccCpePbJDwvzH5IQhZWNjQ0HDhwgMDCQFStW5OnLdG5YpEjMmzePffv2ERMTwxtvvEHp0qX54YcfCA4OZvTo0bz00kt06tSJs2fP0rp1awBGjBhBxYoVLRGvQMnQ6fnht+ts3n+JxOQMnitXgq4tqtHcpyKVXUuqHU8IYQZpaWm89957DBgwgEqVKrFt2zYcHR0tssdukSIxffp0pk+f/sjjn3/+ufG2Vqtlzpw5lohTYB0Kvcn6Hy9wJzaZujXK8WrzatSpXg6Nxjq7doQQT+/YsWMMGjSI8PBwSpYsybhx4yhevLjF1m813U0iZ4qisGbnH2z/9QpV3EoxZ8gr1HN3UTuWEMKMkpKSmDFjBp988gnPP/88u3fvpm3bthbPIUXCymXoDCzdHMovITcJavwigzu/hFb2HIQo9ObNm8fHH3/M8OHDWbBgASVLqtOdLEXCij1IyWDemuP8cTWWzs2q8kZQLelaEqIQS0hI4O7du1SvXp0pU6bQrl07/Pz8VM0kE/xZqdQ0HbNW/c7F63GM7enNoI61pUAIUYjt2LEDT09PevbsiaIolCpVSvUCAVIkrJKiKHy08TSXbyQwqa8vLV9+Qe1IQggziYqKokePHnTu3BkXFxdWrVplVecZSXeTFVr+7VmOhkXyRpAnjeuYdwy0EEI9586do0WLFjx48ID58+czadKkHC8jqhYpElZm8/7L7D32N0FNXqRL82pqxxFCmIFer0er1eLh4UGXLl2YMGECHh4easd6LOlusiI/n7rB+h8v0LB2BQZ3esmqdjmFEE/PYDCwYsUKateuTUJCAnZ2dqxevdpqCwRIkbAakTFJfPZdGB6VnXmr38syzFWIQuby5cs0b97cOJtEcnKy2pFMIkXCCsTdT2XWqqPodAaGdHkJO1v5tQhRWOj1ehYuXIiXlxdhYWGsXbuWvXv3mn3OpfwixyRUpigKH24I4W5CMrMGN6Ta86XVjiSEyEcajYaDBw/Svn17li1b9p8XYLM2UiRU9tXuC4RdiWF4Vy/q1pCpNoQoDNLS0nj33XcZOHBgtgn5CiLp11DRvuN/8+3Pf+LvW5E2r1RWO44QIh/8/vvv1K1bl3feeYfvvvsOoMAWCJAioZrYeyl88f15PCo7M+xVLxnJJEQB9+DBA0aPHk2TJk1ITk5mz549jB07Vu1YT02KhEpW7zhPeoaBwZ1q4+ggvX5CFHTz5s1j2bJljBw5kvPnzxMYGKh2pHwhWycV/HzqBkfO3qabf3VqvFBG7ThCiDyKj4/n7t271KhRg7fffpuOHTvSqFEjtWPlK9mTsLDYeyms2HqOyq4l6dXaXe04Qog82rp1Kx4eHtkm5CtsBQKkSFiUwaCwcts50jP09Gvrgb2dVu1IQohcunPnDt26daNbt264ubmxZs2aQn1MUbqbLOinEzc4dv4OnZtVpX6tCmrHEULk0tmzZ2nRogXJycksWLCACRMmWN2EfPlNioSF3IlNYuW2s7xQwYk3gmqpHUcIkQs6nQ5bW1s8PT3p1q0bEyZMwN29aHQXS3eTBSiKwtpdf6A3KEx/o4FcPEiIAsJgMLB06VI8PT2NE/KtWrWqyBQIkCJhEWf/vMvv5yLp6FcV12dLqB1HCGGCixcv0rRpU0aPHk2VKlVISUlRO5IqpEiYmd6gsHTzGcqWKkbfNjXVjiOE+A96vZ53332XOnXqEB4ezrp16/jxxx8L3JxL+UWKhJltP/gX0fEpvBFUi2Jy0pwQVk+j0XD48GE6duzIhQsX6N+/f6EevfRfZKtlRvcepLHlwGXqubvQpO5zascRQuQgJSWF+fPnM3jwYCpXrlygJ+TLb7InYUarvz9Pus7AgCBPuYiQEFbqyJEj1K1bl/nz5/P9998DBXtCvvwmRcJMwq/Fcij0FoENK/GiWym14wgh/iUxMZGRI0fi5+dHeno6P/30E6NHj1Y7ltWRImEGeoPCV7svULKEPX3bWO+1a4UoyubPn8+KFSsYO3Ys58+fJyAgQO1IVkmOSZjBD79d5Y+rsYx8rS4lHAv32ZhCFCSxsbHExMTg7u7O22+/TefOnWnYsKHasaya7Enks+TUDL498Ce1qpQlsGElteMIIcg8ofXbb7/F09OTXr16GSfkkwLx36RI5LMDJyOIT0xjQHtPtaMIIYDIyEi6du3Ka6+9RsWKFVm7dm2RHtKaWxbrbrp27RpTpkwhISGB0qVLs3DhQipXrpxtmdjYWN5++20iIyPR6XQ0aNCA6dOnY2tbMHrFklMz+O7Xv6hUwYmalZ3VjiNEkXf27FmaN29OamoqCxcuZPz48QVme2ItLLYnMWvWLHr37s3evXvp3bs3M2fOfGSZlStXUrVqVXbu3Mn333/PH3/8wb59+ywV8antPHyVu/EpjOhWV+0oQhRpOp0OAE9PT3r06MHZs2d56623pEDkgUWKRGxsLOHh4QQFBQEQFBREeHg4cXFx2ZazsbEhKSkJg8FAeno6GRkZlC9f3hIRn9q9B2lsO/gX9T0r4PGi7EUIoQa9Xs/ixYvp1q0b8fHx2NnZsXLlSmrUqKF2tALLImU1MjKS8uXLo9VmXmRHq9Xi4uJCZGQkzs7/26AOHz6cUaNG0aRJE1JSUujTpw8+Pj65Wtf58+fznDMkJCTPrz38x32SU3XUq2R4qvextIKUNb9Imwunq1evMnfuXMLCwmjcuDEnTpzg2WefVTuWRZnj92xV+1579uzB3d2ddevWkZSURHBwMHv27KFNmzYmv0ft2rVxcHDI9bpDQkJyXZCyRMclc3jLz/jUdKF9wCt5eg81PE2bCyppc+GTNSHfvHnzcHJyYsOGDbi7u+Pr66t2NIvK6+85LS3tiV+uLdLd5OrqSlRUFHq9Hsj8pUZHRz8yq+KGDRvo2LEjGo0GJycn/P39OX78uCUiPpVvf/kTvd7A8K511I4iRJGj0Wg4duwYr776KuHh4fTp00dGL+UjixSJsmXL4uHhwa5duwDYtWsXHh4e2bqaAJ5//nkOHToEQHp6OkePHqV69eqWiJhnEVGJ7D32N21fqYyLc3G14whRJKSkpDB16lSuXbuGjY0NW7duZePGjbi4uKgdrdCx2Oim2bNns2HDBgIDA9mwYQNz5swBIDg4mLCwMACmTp1KSEgIHTp0oHPnzlSuXJnu3btbKmKebNp3CXtbDT1bF50rVQmhpl9//RUvLy8WLFhg/OJZrFgxlVMVXhY7JlG1alW2bNnyyOOff/658fYLL7zA2rVrLRXpqf195z6Hz96im391Sj2T++MgQgjT3b9/n8mTJ7Ny5UqqVKnCgQMH8Pf3VztWoSdnXD+FnYevYqvV0KlpVbWjCFHovfvuu6xatYrx48dz7tw5KRAWYlWjmwqSew/S+OVUBP6+FWUvQggziYmJ4e7du3h4eDB16lS6dOlCgwYN1I5VpJi8J/Hbb78xdepUhg0bBkBYWBhHjx41WzBrt/v366TrDLIXIYQZKIrCpk2b8PDwoE+fPiiKQsmSJaVAqMCkIrF+/Xpmz55N5cqVOXnyJJB5oGjx4sVmDWetFEXhl1MR1K1ejorlndSOI0ShcuvWLTp37kyvXr2oUqUKX331lQxpVZFJRWLdunWsXbuWIUOGoNFkvqRKlSpcu3bNrOGs1ZVb94iMTcLPW65bLUR+OnPmDJ6envz0008sWrSI33//ndq1a6sdq0gz6ZhEUlKS8cS3rIqu0+mwsyuaF9Q5cuYWWo0Nr7zk+t8LCyH+U0ZGBnZ2dtSqVYs+ffowYcIEqlaVrlxrYNKexMsvv8yqVauyPfbVV18Vyf5BRVE4fPY2dWuUw6m4vdpxhCjQ9Ho9H330ETVr1jROyLdixQopEFbEpCIxffp0fvrpJ/z9/UlKSiIwMJAff/yRKVOmmDuf1fkzIoHouGSa1JGuJiGexvnz52nUqBETJkzA09OT9PR0tSOJxzCpu8nFxYWtW7cSFhbGrVu3cHV1xcvLy3h8oig5fOYWtlobGtauoHYUIQokvV7PvHnzmD9/PqVKlWLjxo306NFDDk5bKZO28m+++SY2NjZ4eXnRtm1b6tati0ajYeTIkebOZ1UMBoUjZ2/j7e7CM9LVJESeaDQaTp48yWuvvUZ4eDg9e/aUAmHFTCoSOc3EeuLEiXwNY+0u34gnJiFFupqEyKXk5GTeeustrl69apyQ7+uvv6ZcuXJqRxP/4YndTVnnQWRkZDxyTkRERARubm7mS2aFTl2MQmMDDWpJV5MQpvrll18YPHgwV69epVKlSowYMSJP13wR6nhikbhz5w6QOaIn63YWV1dXRo0aZb5kVujS9Xgqu5aihGPRHPorRG7cu3ePSZMm8fnnn1OtWjUOHjxIs2bN1I4lcumJRWLBggUAeHt7W/2U3eamNyhcuhFP83rPqx1FiAJhwYIFfPHFF0yaNInZs2dTvLhcb6UgMml0U1aBePDgAfHx8dmeq1ixYv6nskI3oxJJSdNRs3IZtaMIYbXu3r1LTEyMcUK+bt26FbnLiBY2JhWJK1euMGHCBC5evIiNjQ2KohhHI1y4cMGsAa3Fxb8zi2PNSs7/saQQRY+iKGzcuJHRo0fzwgsvEBISQsmSJaVAFAImjW6aPXs2DRo04MSJEzzzzDOcPHmSHj168N5775k7n9W49HccTsXtcX22hNpRhLAqERERdOjQgT59+lCtWjXWr18vQ1oLEZP2JC5evMiaNWuws7NDURScnJx46623CAoKolOnTubOaBUu/h2Pe6Uy8o9fiH8IDQ2lWbNm6PV6Pv74Y0aNGoVWq1U7lshHJu1JODg4oNPpAChTpgy3b9/GYDCQkJBg1nDWIjVdx83oRGpULK12FCGsQtYUGrVr16Z///6EhYUxduxYKRCFkElFwsfHhx9//BGAwMBAgoOD6devHw0bNjRrOGsREZWIokAl15JqRxFCVTqdjg8++AB3d3fi4uKws7Nj2bJlVKlSRe1owkxM6m7654l048ePp1q1aiQnJ9OlSxezBbMmf0feB6CyFAlRhJ07d45BgwZx6tQpOnfubOxdEIVbrmfo02g0dO7cmW7durFt2zZzZLI6f99JxN5OS/myctBaFD16vZ6ZM2fi4+PDjRs32Lx5M9u2bcPFxUXtaMIC/rNIHD16lDVr1rB//34gc3fzq6++omXLlmzatMnsAa3B9cj7vFD+GbQaOWgtih6NRkNoaCi9e/cmPDyc1157TQZwFCFP7G5atWoVn376KdWqVeOvv/6iV69enDhxAnt7e+bOnUvz5s0tFFNdN+7cp24N+dYkio6kpCRmzZrF8OHDqVKlClu3bsXeXmY+LoqeWCS++eYb1q9fT+3atTlz5gy9evVi8uTJDBgwwELx1Hc/KZ24+2lyPEIUGfv372fIkCFcu3aNKlWqMHz4cCkQRdgTu5vi4+ONFyGvW7cu9vb2vP766xYJZi2yDlpXqiBFQhRuCQkJDBo0iFatWmFra8uhQ4cYPny42rGEyv7zmISiKBgMBvR6vXF6X4PBYPwp7P6+87BIuDqpnEQI83rvvfdYt24dU6ZM4ezZs/j5+akdSViBJ3Y3JScn4+npabyvKIrxftb8TYV97qbrkfd5xtEO55LF1I4iRL6LiooiJiaGWrVqMXXqVLp37069evXUjiWsyBOLxIEDByyVw2rduJPICxWcZDSHKFQURWH9+vWMHTuWypUrGyfkkwIh/u2JReK55+QynXfjk/GqLpdYFIXHjRs3GDp0KHv27KFRo0Z88cUX8iVI5MikM67zw7Vr15gyZQoJCQmULl2ahQsXUrly5UeW2717N59++qmxO2vt2rU8++yzloqZjd6gEJeYRtlS0tUkCofTp0/TrFkzFEVhyZIljBgxAo0m1+fUiiLEYkVi1qxZ9O7dm06dOrFjxw5mzpzJV199lW2ZsLAwli1bxrp16yhXrhyJiYmqDr1LSEzFYFB4trSjahmEyA9paWk4ODjg5eXFwIEDGdLzrmcAACAASURBVDdu3GO/pAnxbxb5ChEbG0t4eDhBQUEABAUFER4eTlxcXLblvvzySwYOHEi5cpndO05OTqpeMD32XioAz5aSIiEKJp1Ox5dffmmckM/W1pbFixdLgRAmy1WRiIyM5MyZM7leSWRkJOXLlzdOI6zVanFxcSEyMjLbcleuXCEiIoI+ffrQpUsXVqxYgaIouV5ffolJSAGQ7iZRIJ05c4YGDRqwbNkyfHx80Ov1akcSBZBJ3U23b99m/PjxxsuXhoaGsmfPHg4fPsz8+fPzLYxer+fSpUusXbuW9PR0Bg8ejJubG507dzb5Pc6fP5/n9YeEhGS7H3opEYBbf18mIapwzpP/7zYXBYW9zXq9ns8++4x169YZj/+1bNmSGzducOPGDbXjWUxh/z0/jjnabFKRmDlzJs2bN+f//u//aNCgAQCNGzdm4cKFJq3E1dWVqKgo9Ho9Wq0WvV5PdHQ0rq6u2ZZzc3OjTZs22NvbY29vT8uWLTl37lyuikTt2rXz1EUVEhKCj49PtsfCIv/AVpuIX6OXC+Xoj8e1ubArCm1WFIV58+bRt29fPvroI65du1bo2/xvReH3/G95bXNaWtoTv1yb1N0UFhbGkCFD0Gg0xo2lk5MTiYmJJoUoW7YsHh4e7Nq1C4Bdu3bh4eGBs7NztuWCgoI4cuQIiqKQkZHBsWPHqFmzpknrMIeYhFTKlipWKAuEKFwePHjAuHHjuHLlCjY2NmzdupUvv/zykb8xIXLLpCJRtmxZ/v7772yP/fXXX4/sCTzJ7Nmz2bBhA4GBgWzYsIE5c+YAEBwcTFhYGADt27enbNmytGvXjs6dO1OtWjW6detm8jryW8y9FBnZJKze3r17qVWrFosXL+ann34CwM7OTuVUorAwqbtp4MCBDBs2jCFDhqDT6di1axefffYZwcHBJq+oatWqbNmy5ZHHP//8c+NtjUbD22+/zdtvv23y+5pT7L0UarxQRu0YQjxWXFwc48ePZ926ddSsWZPDhw/TuHFjtWOJQsakItGtWzdKly7NN998g6urK9u3b2fMmDEEBASYO59qFEUh9l6qDH8VVuv9999nw4YNTJs2jenTp1OsmIzCE/nPpCKh1+sJCAgo1EXh3+4npZOhM1C2tPzhCetx584dYmJiqF27NtOmTaNnz57UrVtX7ViiEDPpmETjxo2ZPXt2kRpSJifSCWuiKApffvklnp6e9O/fH0VRcHJykgIhzM6kIrFmzRqKFy/OhAkT8Pf3Z9GiRVy6dMnc2VQVc09OpBPW4fr167Rp04Y33niDWrVqsXHjRhlxJyzGpO4mT09PPD09eeuttzhx4gS7du3i9ddfp1y5cuzcudPcGVUR+/BsaxndJNR0+vRpmjZtio2NDcuXL2fYsGEyIZ+wqFz/a6tSpQpVq1bFzc2NW7dumSOTVYi5l4pGY0NpJ9mTEJaXmprZ3enl5UVwcDDnz59n+PDhUiCExZn0L+7+/fts2bKF119/nYCAAE6cOMHgwYM5evSoufOpJvZeCs5ODmg1slsvLCcjI4P58+dTo0YNYmNjsbW15eOPP6ZSpUpqRxNFlEndTX5+fnh7exMUFMTSpUspWbKkuXOpLjYhlbLS1SQs6PTp0wwcOJCzZ8/SvXt3VSe3FCKLSUXip59+wsXFxdxZrErMvRQqVSj8xVCoT6/XM23aND788ENcXFz47rvvcjVfmRDmlGOROHnyJC+//DKQOYX3lStXHrvcK6+8Yp5kKlIUhZiEFOrVLFqFUahDo9Fw4cIFBgwYwIcffkjp0qXVjiSEUY5FYs6cOcYJ+aZNm/bYZWxsbDhw4IB5kqkoOVVHarqesiWlu0mYR2JiItOnT2fUqFFUq1aNb7/9VuZbElYpxyKRVSAAfv75Z4uEsRax97KGv8rIJpH/fvzxR4YOHcrNmzfx9PSkWrVqUiCE1TJpdNObb7752MdHjhyZr2GsRdbZ1s4lpUiI/BMbG0v//v1p164dTk5O/PbbbwwdOlTtWEI8kUlF4vjx4499/MSJE/kaxlqkpOkAKOEo3+5E/vnggw/YuHEjM2bM4PTp04XyeJ4ofJ44umnx4sVA5tjtrNtZIiIicHNzM18yFaWmZ14L2MG+cF6yVFjO7du3iY2N5aWXXmLatGn07t0bLy8vtWMJYbInFok7d+4AmaN9sm5ncXV1ZdSoUeZLpqK0jIdFwk6KhMgbRVFYs2YNEyZMoEqVKoSEhODk5CQFQhQ4TywSCxYsAMDb25vu3btbJJA1SEvP7G4qZm/SaSRCZHP16lWGDBnCgQMHaNasGatXr5YJ+USBleNW8ObNmzz//PNA5rkQERERj12uYsWK5kmmojTpbhJ5dPr0afz8/NBqtaxcuZLg4GCZb0kUaDkWiQ4dOhAaGgpAq1atsLGxeWSaABsbGy5cuGDehCpITddjq7XBVit/3MI0qampFCtWDC8vL958803GjBlTKL9AiaInxyKRVSAALl68aJEw1iI1XYeDdDUJE6Snp7Nw4UJWrVrFmTNnKFu2LB9++KHasYTIN3naEkZERGBjY2Psjips0tL1ctBa/KeTJ08yaNAgwsLC6Nmzp9pxhDALk/pTxo8fz+nTpwHYunUr7du3JygoiC1btpg1nFrS0vUUk+MRIgd6vZ633nqLhg0bEhsby44dO9i4cSNly5ZVO5oQ+c6kInH06FFq164NwJdffsnatWvZsmULn3/+uVnDqSUtQy8jm0SONBoNf/31F4MGDSI8PJyOHTuqHUkIszFpS5iRkYG9vT1RUVEkJCTg4+MDQExMjFnDqSXzmITsSYj/uXfvHtOmTWPMmDFUr16dzZs3Y2srXyRE4WfSv3IPDw8+++wzbt26RfPmzQGIiorimWeeMWc21aSm63F0kA2AyPTDDz8wdOhQIiMj8fLyonr16lIgRJFhUnfT/PnzuXz5MmlpaYwZMwbIHP3UoUMHs4ZTixy4FgB3796lT58+BAUFUaZMGY4ePcqQIUPUjiWERZn0deiFF15g0aJF2R5r06YNbdq0MUsotWUeuJZvikXdokWL2LJlC7Nnz+btt9/G3t5e7UhCWJzJW8KtW7eyY8cOoqKiKF++PJ06daJr167mzKaatAwdxRxkT6IounXrFrGxsXh5eTF9+nT69u1rHLQhRFFkUpH49NNP2b59OwMHDsTNzY3bt2+zevVqoqOjc7zWREGWKt1NRY6iKKxevZqJEydStWpVQkJCeOaZZ6RAiCLPpCKxZcsW1q9fz3PPPWd8rEmTJvTt27fQFQlFUTKLhIxuKjKuXLlCcHAwv/zyCy1atODzzz+XCfmEeMikIpGSkoKzs3O2x0qXLk1qaqpZQqlJp1cwGBQpEkVESEgIfn5+2NnZsWrVKgYPHiwFQoh/MGl0k5+fHxMnTuTq1aukpqZy5coVpkyZQpMmTUxe0bVr1+jRoweBgYH06NGD69ev57js1atXqVOnDgsXLjT5/fOLTBNeNKSkZF7HvE6dOowcOZLw8HCCg4OlQAjxLyYViZkzZ1KiRAk6duyIt7c3nTt3xtHRkRkzZpi8olmzZtG7d2/27t1L7969mTlz5mOX0+v1zJo1i4CAAJPfOz9lXZVOpuUonNLT05k9ezbVq1cnJiYGW1tb3n///WxdqUKI//nPr8uJiYncuHGDmTNn8t577xEfH0+ZMmVyNUd+bGws4eHhrF27FoCgoCDmzp1LXFzcI91Yq1atonnz5iQnJ5OcnJzL5jw9uSpd4XX8+HH69u3L1atX6du3r1znQQgTPLFIHDx4kLFjx5KamkqJEiVYvnw5DRs2zPVKIiMjKV++PFpt5oZXq9Xi4uJCZGRktiJx8eJFjhw5wldffcWKFStyvR6A8+fP5+l1kNk/HRmXDsDNiL8JITrP71VQhISEqB3B7HQ6HUuWLGHjxo24uLjwySef0KRJE65du8a1a9fUjmcRReH3/G/S5vzxxCKxePFiJk6cSNeuXdm8eTOffPIJmzZtyvcQkDk/1IwZM1iwYIGxmORF7dq1cXBwyPXrQkJC8PHxIfxaLBCNp0cN6rm75DlHQZDV5sJOURRSU1MZNmwYPXr0oFmzZmpHsqii8nv+J2mz6dLS0p745fqJRSIiIoK+ffsC0KdPH1auXJnrAACurq5ERUWh1+vRarXo9Xqio6NxdXU1LnP37l1u3LhhnPbg/v37KIrCgwcPmDt3bp7WmxdyTKJwSEhIYOrUqYwbN844IZ9Wqy2S3y6FeBpPLBIGg+F/C9raotfr87SSsmXL4uHhwa5du+jUqRO7du3Cw8MjW1eTm5sbx48fN95funQpycnJTJ48OU/rzCsZ3VTwff/997z55pvcuXOHevXqUb169afaOxWiKHviljA1NZU+ffoY7yclJWW7D/D111+btKLZs2czZcoUVqxYQcmSJY3DW4ODgxk9ejQvvfRSbrObRdrDPQk5T6LgiY6OZvTo0XzzzTe89NJL7NixA19fX7VjCVGgPbFIzJ8/P9v9bt265XlFVatWfeyV7HK6cNGoUaPyvK6nId1NBddHH33Ed999x9y5c3nrrbdkQj4h8sETi0SXLl0slcNqGIfASndTgRAREUFsbCx169ZlxowZ9O/fH09PT7VjCVFoyEDxf0l9eExCzpOwbgaDgU8//ZRatWoxaNAgFEWhRIkSUiCEyGdSJP4lLV2PVmODna18NNbqzz//pEWLFgwfPpwGDRrw7bffynQaQpiJ9Kn8S5rMAGvVTp06hZ+fHw4ODnzxxRe88cYbUiCEMCP5uvwvqel6OWhthbKmaPH29mbs2LGEh4czcOBAKRBCmJlJRSI9PZ2PP/6Yli1bGs/oO3LkCBs2bDBrODVk7knIDpa1SEtLY8aMGcYJ+bRaLQsWLMDNzU3taEIUCSYViXfffZfLly/z4YcfGr+5Va9enY0bN5o1nBpS03Vy0NpKHD16FG9vb+bNm0dAQIBMyCeECkz6yrx//3727dtH8eLFjX+o5cuXJyoqyqzh1JAm3U2q0+l0TJw4kSVLllCxYkV+/PFH2rRpo3YsIYokk76a2dnZPTIlR1xcHKVLlzZLKDWlZciBa7XZ2tpy8+ZNhg8fzvnz56VACKEik4pEmzZtmDx5MhEREUDm9AfvvPMO7du3N2s4NaSm62TeJhXEx8czdOhQLl26BMA333zDsmXLcHJyUjmZEEWbSUVi3LhxPP/883Ts2JH79+8TGBiIi4sLI0aMMHc+i0uVIbAW99133+Hp6ckXX3zB4cOHAWRCPiGshElfme3t7Zk6dSpTp04lLi6OMmXKFNqhh2npejlwbSF37txh1KhRfPvtt9StW5cffviBevXqqR1LCPEPJhWJrG6mLElJScbbFStWzN9EKktL11HMQbqbLOGTTz5h586dvPvuu0ycOBE7Ozu1Iwkh/sWkrWGrVq2wsbFBURTjY1l7EhcuXDBPMpWkZciehDnduHGD2NhYvL29mTFjBgMGDKBmzZpqxxJC5MCkInHx4sVs9+/evcuyZcsK3Vz9Or0BnV6RIbBmkDUh35QpU6hRowanTp2iRIkSUiCEsHJ5OjupXLlyTJs2jY8++ii/86gqNV2mCTeHS5cu0axZM0aOHEmjRo3YunVroT2mJURhk+et4dWrV0lJScnPLKrLunSpjG7KPydPnsTPz4/ixYvz5Zdf0r9/fykQQhQgJhWJ3r17Z/vDTklJ4a+//ip0Q2DT5Kp0+SYpKYkSJUpQr149JkyYwKhRo6hQoYLasYQQuWRSkXjttdey3Xd0dKRmzZpUrlzZHJlUY7wqnRy4zrPU1FTmzp3L2rVrOXv2LOXKlXvkMrhCiILjP4uEXq/n2LFjzJ07t9BfMzg1LWtPQo5J5MVvv/3GoEGDuHTpEgMGDMDWVj5HIQq6/zxwrdVq+e2334pEP3KqHJPIE51Ox6hRo/Dz8yM1NZW9e/eydu1aypQpo3Y0IcRTMml00+uvv87SpUvJyMgwdx5VGbubpEjkiq2tLVFRUYwaNYrz58/TunVrtSMJIfLJE/sDdu3aRVBQEBs2bCAmJoa1a9fi7Oycba/i4MGD5s5oMaly4NpkcXFxTJ48mYkTJ+Lu7s6mTZvkeg9CFEJPLBIzZ84kKCiIDz74wFJ5VJU1usnBTvrSn2Tr1q2MGDGC2NhYGjVqhLu7uxQIIQqpJ24Ns6bhqF+/vkXCqC3rPIliDrIn8TiRkZGMHDmSbdu2Ua9ePfbs2UPdunXVjiWEMKMnFgmDwcCxY8eyzdn0b6+88kq+h1KLdDc92eLFi/nhhx947733mDBhgoxeEqIIeOJfeXp6OtOmTcuxSNjY2HDgwAGzBFNDWoYejQ3YaqXrJMv169eJj483Tsg3cOBAatSooXYsIYSFPLFIODo6Fqoi8F9S03U42NsWieG+/0Wv17N8+XKmTp1KzZo1OXnyJCVKlJACIUQRI1+Z/yFNrkoHZE7/3rRpU8aMGYOfnx/btm2TwilEEWXSgeuiIi1dX+SPR5w8eZImTZrwzDPPsH79evr06SMFQogi7IlFIjQ0NN9WdO3aNaZMmUJCQgKlS5dm4cKFj8z9tHz5cnbv3o1Go8HOzo5x48bh5+eXbxn+S2q6rshOyZGYmIiTkxP16tVj8uTJjBw5EhcXF7VjCSFUZrHuplmzZtG7d2/27t1L7969mTlz5iPLeHl58e233xovaTlu3DhSU1MtFbFIXt86JSWFKVOmUL16daKjo9FqtbzzzjtSIIQQgIWKRGxsLOHh4QQFBQEQFBREeHg4cXFx2Zbz8/PD0dERAHd3dxRFISEhwRIRgcwhsEXpmMTp06epU6cOCxcuJCgoqNBP4CiEyD2LFInIyEjKly+PVpu5AdZqtbi4uBAZGZnja7Zv384LL7xg0WsQFJUD1zqdjhEjRjBkyBB0Oh379+9n9erVlC5dWu1oQggrY5Ud8CdOnGDx4sWsWbMm1689f/58ntd7LzEJR9t0QkJC8vweBcWff/5Jr169GD58OI6OjkWizVmKUluzSJuLBnO02SJFwtXVlaioKPR6PVqtFr1eT3R0NK6uro8sGxoayqRJk1ixYgVVqlTJ9bpq166Ng4NDrl8XEhICGlvcKpTDx8c716+3drGxsUyaNIm33nqLmjVrsmfPHkJDQ/Hx8VE7mkWFhIRIm4sAabPp0tLSnvjl2iLdTWXLlsXDw4Ndu3YBmbPLenh44OzsnG25c+fOMW7cOJYsWUKtWrUsES2bwnjgWlEUNm/ejIeHB+vXr+fYsWMAMiGfEMIkFttSzJ49mw0bNhAYGMiGDRuYM2cOAMHBwYSFhQEwZ84cUlNTmTlzJp06daJTp05cunTJUhEL3YHr27dv06VLF3r06EGlSpUICQlhwIABascSQhQgFjsmUbVqVbZs2fLI459//rnx9tatWy0V5xF6g4JOb6CYg1UepsmTpUuXsnfvXj744APGjh0rE/IJIXJNthoPZegzzy4v6N1NV69eJT4+Hh8fH2bMmMGgQYOoVq2a2rGEEAWUdEw/lKHLLBIFdVoOvV7PJ598wksvvcTQoUNRFIXixYtLgRBCPBUpEg9lFYmCeEzijz/+oHHjxowbN44WLVqwfft2mW9JCJEvpLvpoXSdAQCHAjZ304kTJ2jSpAmlSpXi//7v/+jZs6cUCCFEvpE9iYcKWndTYmIiAD4+PkydOpXw8HB69eolBUIIka+kSDxUUA5cJycnM2nSJKpVq2ackG/27NmUK1dO7WhCiEKoYPWtmFG6cU/Cej+SgwcPMnjwYK5cuUJwcHCeziwXQojckD2Jh6z5wLVOp2Po0KG0aNECRVE4cOAAq1atolSpUmpHE0IUclIkHjJ2N1lhkbC1teX+/fuMHz+esLAw/P391Y4khCgipEg8lDW6yVq6m+7evcvrr7/OxYsXAfj6669ZtGgRxYsXVzmZEKIokSLxkLV0NymKwsaNG/H09GTjxo2cOHECkAn5hBDqkC3PQxk6BRsbsLdV7yO5efMmHTt2pHfv3lSpUoXTp0/Tv39/1fIIIYQUiYfSdQoOdlpVzzNYsWIFBw4c4KOPPuL333+ndu3aqmURQgiQIbBGGXpFleMRf/31FwkJCfj6+jJ9+nQGDRpE1apVLZ5DCCEeR/YkHsrQKRY9HqHX61m0aBFeXl4MGzbMOCGfFAghhDWRIvFQht5yReL8+fO88sorTJw4kYCAAHbs2CHTaQghrJJ0Nz2UrjNYZN6mrAn5SpcuzaZNm+jevbsUCCGE1ZI9iYcydAoOduarmffu3QMwHnsIDw+nR48eUiCEEFZNisRD5jomkZSUxPjx46levTpRUVFoNBpmzpzJs88+m+/rEkKI/CbdTQ+l65V87246cOAAwcHBXLt2jTfffBNHR8d8fX8hhDA32ZN4KD/3JHQ6HcHBwQQEBGBra8vBgwdZsWIFJUuWzJf3F0IIS5Ei8VCGLv/Ok7C1tSUpKYm33nqLs2fP0qxZs3x5XyGEsDQpEg+l6wxPdcGh6Oho+vXrx4ULF4DMCfkWLlwoXUxCiAJNigSgNyjoDXm7dKmiKHz99dd4enqyefNmTp06BSCjloQQhYIUCSAtXQeAQy67myIiIggKCqJv375Ur16d0NBQ+vXrZ46IQgihCikSQFq6Hsj9NOErVqzg4MGDfPLJJxw5cgRPT09zxBNCCNXIEFggLSOzSJjS3XT58mUSEhKoX78+M2bMYMiQIbz44ovmjiiEEKqQPQkg1YQ9CZ1Ox/vvv0+dOnUYPny4cUI+KRBCiMJMigT/OyaR0xDYs2fP0qBBAyZPnkzbtm3ZuXOnHJgWQhQJ0t3Ek/ckjh8/TpMmTXB2dmbLli107dpVCoQQosiQPQn+ceD6H+dJJCQkAPDyyy8ze/ZsLly4QLdu3aRACCGKFIsViWvXrtGjRw8CAwPp0aMH169ff2QZvV7PnDlzCAgIoFWrVmzZssUi2bKKRDF7LQ8ePGDs2LHZJuSbNm0azs7OFskihBDWxGJFYtasWfTu3Zu9e/fSu3dvZs6c+cgyO3fu5MaNG+zbt49vvvmGpUuXcvPmTbNnS314TOL4b7/y0ksvsXjxYnr06EHx4sXNvm4hhLBmFikSsbGxhIeHExQUBEBQUBDh4eHExcVlW2737t289tpraDQanJ2dCQgIYM+ePWbPl5ySxtm9S+nerSP29vYcOnSIZcuW4eTkZPZ1CyGENbPIgevIyEjKly+PVpvZ56/VanFxcSEyMjJbN05kZCRubm7G+66urty5cydX6zp//nyu88XGJGGDjv79X2fo0CE4ODgQEhKS6/cpiIpKO/9J2lw0SJvzR6Eb3VS7dm0cHBxy9RpvbwX3596n8Sv1zZTKOoWEhODj46N2DIuSNhcN0mbTpaWlPfHLtUW6m1xdXYmKikKvzzxArNfriY6OxtXV9ZHlbt++bbwfGRlJhQoVzJ5Po7GxyPWthRCioLFIkShbtiweHh7s2rULgF27duHh4fHIiKE2bdqwZcsWDAYDcXFx7N+/n8DAQEtEFEII8RgWG900e/ZsNmzYQGBgIBs2bGDOnDkABAcHExYWBkCnTp14/vnnad26Nd27d2fEiBFUrFjRUhGFEEL8i8WOSVStWvWx5z18/vnnxttardZYPIQQQqhPzrgWQgiRIykSQgghciRFQgghRI4KzXkSiqIAkJ6enuf3SEtLy684BYa0uWiQNhcNeWlz1jYzaxv6bzZKTs8UMImJiVy+fFntGEIIUSDVqFHjsVMRFZoiYTAYSEpKws7OTqbzFkIIEymKQkZGBiVKlECjefQIRKEpEkIIIfKfHLgWQgiRIykSQgghciRFQgghRI6kSAghhMiRFAkhhBA5kiIhhBAiR1IkhBBC5KhIFYlr167Ro0cPAgMD6dGjB9evX39kGb1ez5w5cwgICKBVq1aPnd68IDGlzcuXL6d9+/Z06NCBV199lcOHD1s+aD4ypc1Zrl69Sp06dVi4cKHlApqBqW3evXs3HTp0ICgoiA4dOhATE2PZoPnIlDbHxsYyZMgQOnToQNu2bZk9ezY6nc7yYfPBwoUL8ff3x93dPcfZJcyy/VKKkH79+inbt29XFEVRtm/frvTr1++RZb777jtl4MCBil6vV2JjYxU/Pz8lIiLC0lHzjSltPnTokJKcnKwoiqJcuHBB8fHxUVJSUiyaMz+Z0mZFURSdTqf07dtXGT9+vPLee+9ZMmK+M6XN586dU9q2batER0criqIo9+/fV1JTUy2aMz+Z0uZ58+YZf7fp6elKt27dlB9++MGiOfPLyZMnldu3bystWrRQLl269NhlzLH9KjJ7ErGxsYSHhxMUFARAUFAQ4eHhxMXFZVtu9+7dvPbaa2g0GpydnQkICGDPnj1qRH5qprbZz88PR0dHANzd3VEUhYSEBIvnzQ+mthlg1apVNG/enMqVK1s4Zf4ytc1ffvklAwcOpFy5cgA4OTnh4OBg8bz5wdQ229jYkJSUhMFgID09nYyMDMqXL69G5Kfm6+uLq6vrE5cxx/aryBSJyMhIypcvj1arBTKvgufi4kJkZOQjy7m5uRnvu7q6cufOHYtmzS+mtvmftm/fzgsvvECFChUsFTNfmdrmixcvcuTIEQYMGKBCyvxlapuvXLlCREQEffr0oUuXLqxYsSLHmT+tnaltHj58ONeuXaNJkybGHx8fHzUiW4Q5tl9FpkiI/3bixAkWL17MokWL1I5iVhkZGcyYMYM5c+YYNzJFgV6v59KlS6xdu5b169dz6NAhduzYoXYss9qzZw/u7u4cOXKEQ4cOcerUqQLbM6CWIlMkXF1diYqKQq/XA5l/MNHR0Y/svrm6unL79m3j/cjIyAL7rdrUNgOEhoYyadIkli9fTpUqVSwdNd+Y0ua7d+9y48YNhgwZgr+/P+vWrWPz5s3MmDFDrdhPxdTfs5ubG23atMHe64ut+AAACXNJREFU3p5nnnmGli1bcu7cOTUiPzVT27xhwwY6duyIRqPByckJf39/jh8/rkZkizDH9qvIFImyZcvi4eHBrl27ANi1axceHh44OztnW65NmzZs2bIFg8FAXFwc+/fvJzAwUI3IT83UNp87d45x48axZMkSatWqpUbUfGNKm93c3Dh+/Dg///wzP//8M6+//jrdu3dn7ty5asV+Kqb+noOCgjhy5Ihxauhjx45Rs2ZNNSI/NVPb/Pzzz3Po0CEg8+I6R48epXr16hbPaylm2X491WHvAuavv/5SunXrprRu3Vrp1q2bcuXKFUVRFGXw4MHKuXPnFEXJHPEyc+ZMpWXLlkrLli2VTZs2qRn5qZnS5ldffVVp0KCB0rFjR+PPxYsX1Yz9VExp8z8tWbKkwI9uMqXNer1eeffdd5U2bdoo7dq1U959911Fr9erGfupmNLmv//+WxkwYIASFBSktG3bVpk9e7aSkZGhZuw8mzt3ruLn56d4eHgojRo1Utq1a6coivm3X3I9CSGEEDkqMt1NQgghck+KhBBCiBxJkRBCCJEjKRJCCCFyJEVCCCFEjqRIiAKtX79+Vj9T7/fff8/AgQNzfP7UqVMF9lwcUfhJkRBWw9/fHy8vL7y9vY0/UVFRFs/Rr18/XnrpJby9vWnQoAEjR44kOjo6z+/XsWNH1qxZY7zv7u7O33//bbzv6+vL3r17nyrz4yxdupRatWrh7e2Nr68vPXv2JDQ01OTX/zunKJqkSAirsnLlSkJDQ40/as3YOXPmTEJDQ9m7dy/3799nwYIFquR4Wm3btiU0NJRjx47RoEEDxowZo3YkUcBIkRBW7d69ewwdOpSGDRvy8ssvM3To0Bxntfz777/p27cvPj4+NGjQgLFjxxqfu3LlCm+88Qb169cnMDCQ3bt3m7T+0qVLExgYyJ9//gnA6dOn6dq1Kz4+PnTt2pXTp08bl922bRstW7bE29sbf39/vv/+e+PjvXr1AqBPnz4AdOrUCW9vb3bv3s3x48dp2rQpkDl9+ejRo7NlmDdvHvPmzQMgMTGRqVOn0qRJE/z8/Pj444+N8xc9ia2tLR06dCAqKso4nfa5c+fo0aMHvr6+NGnShHfeeYf09PQccwL88ssvdOrUybhncvHiRZM+R1GAPfU520LkkxYtWii//fZbtsfi4uKUPXv2KMnJyUpiYqIyatQo5c033zQ+37dvX2Xz5s2KoijKuHHjlBUrVih6vV5JTU1VTp48qSiKoiQlJSlNmzZVvv32WyUjI0P5448/lPr16yt//vnnY3P88z1jY2OVfv36KRMnTlTi4+MVX19f5bvvvlMyMjKUnTt3Kr6+vkpcXJySlJSkeHt7G6eGiIqKUi5fvqwoiqJs3bpV6dmzp/H9a9SooVy/ft14/9ixY4qfn5+iKIpy8+ZNxcvLS0lMTFQUJXOahcaNGyuhoaGKoijK8OHDlRkzZihJSUlKTEyM0rVrV2Xjxo2PbceSJUuUCRMmKIqiKGlpacoHH3yg1K9f3zgtRVhYmBIaGqpkZGQoERERSps2bZS1a9fmmPOPP/5QGjZsqJw5c0bR6XTKtm3blBYtWihpaWmPXb8oHGRPQliVESNG4Ovri6+vL8P/v727CYlqDQM4/m86fo1D4uQMiYZguyHRZEalPOgqSwad0GGWKm6aEHdiZoKYoInRBwYuTF1EpUEIjh+Q0EQQki7UEhclidHgV2HgJ6PjXcgdblyP6G1xFZ7fbg5nzvucA8Mz73tenufGDaKjo8nJySEiIgKDwYDb7WZkZGTP7yqKgs/nY2FhgbCwMKxWKwBer5e4uDgKCgpQFAWLxUJOTs6+JaPr6+uxWq3k5+djMpmoqqrC6/WSkJCAw+FAURTsdjuJiYm8efMGAJ1Ox+fPn9nY2MBsNv+nQnJxcXFYLBaGhoYAGB4eJjw8nJSUFJaWlnj79i23bt1Cr9dz+vRpiouL6evr07ze4OAgVquV5ORkXr58yaNHj1AUBYDz58+TkpKCoijEx8fjcrk0ny1AV1cXLpeL5ORkTp48ybVr1wgJCWFsbOzQ9ymOD+X/DkCIf3r8+DEXL14Mfl5fX6ehoYF3797x69cvAFZXV9ne3v5XL4iKigoePnxIYWEhUVFRlJSUUFhYyPfv35mYmAgmDdgtLZ2Xl6cZx+3bt3E6nb8dW1hY+K2hC+xWlJ2fn0ev13P//n3a29uprq4mNTWVyspKzp07d+hnYLfb8Xg8OBwOPB5PsPuaz+dja2uLzMzM4LmBQGDfbmVXrlyhubmZnz9/Ul5ezuTkJOnp6cBuj+jGxkY+ffrE+vo629vb+1YB9vl89PT08PTp0+Axv9//Ry/1xdEnSUIcae3t7Xz9+pXu7m5MJhNTU1M4HI49O6qZTKbg2v3o6CglJSXYbDZiY2Ox2Wx0dHT8USxms/m3Wv2wW69fVVVgtw2sqqpsbGzw4MEDampqePbs2aHHuXr1Knfv3mVubo7Xr1/T1dUFwJkzZwgNDWV4eDg4Gzgoo9FIXV0dBQUF2O12zGYztbW1WCwW7t27h8FgoLOzc99dVrGxsVy/fh23233oexLHlyw3iSNtdXWVsLAwTp06xfLyMi0tLZrnDgwMBF9qR0VFceLECXQ6HdnZ2czMzNDT04Pf78fv9zMxMcH09PShYsnKymJmZobe3l62trbo7+/ny5cvZGdns7S0xNDQEGtra4SGhqLX69Hp9v55xcTE8O3bN81xjEYjaWlpVFVVER8fH5yNmM1mLl26RGNjIysrKwQCAWZnZ/nw4cOB4k9MTERVVdra2oDdZxsZGUlkZCTT09M8f/583zidTicvXrxgfHycnZ0d1tbW8Hq9rKysHGh8cTxJkhBHWlFREZubm2RkZOByuYL/2vfy8eNHnE4nFy5cwO12U11dzdmzZzEYDDx58oT+/n5UVSUzM5Pm5ubgTp6Dio6OprW1lY6ODtLT02lra6O1tRWj0UggEKCzsxNVVUlLS2NkZITa2to9r1NWVsbNmzexWq2au6zsdjvv378PLjX9rampCb/fT25uLjabjfLychYXFw98D6WlpXR3d/Pjxw8qKyvxeDykpqZSU1NDbm7uvnEmJSVx584d6urqsNlsXL58mVevXh14bHE8ST8JIYQQmmQmIYQQQpMkCSGEEJokSQghhNAkSUIIIYQmSRJCCCE0SZIQQgihSZKEEEIITZIkhBBCaJIkIYQQQtNfj+/P135M2rQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**confusion_matrix**"
      ],
      "metadata": {
        "id": "xnOTl7agq6Pa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matrix= confusion_matrix(y_test,rf_pred)\n",
        "print(matrix)\n",
        "sns.heatmap(matrix ,annot=True, fmt='g')"
      ],
      "metadata": {
        "id": "wf5hLuozbBwo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "6ad9ab16-a012-4422-b4af-86cc0ab03167"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[84544 15592]\n",
            " [ 1480 99024]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faac9325c50>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The confusion matrix now shows that the model now is much better with predicting positive responses.\n",
        "\n"
      ],
      "metadata": {
        "id": "YsPdPJ5EN6o8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(rf_pred, y_test))"
      ],
      "metadata": {
        "id": "4LTWhMhkbByN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c51f6e0f-855f-4656-ed75-39c8ed261006"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.98      0.91     86024\n",
            "           1       0.99      0.86      0.92    114616\n",
            "\n",
            "    accuracy                           0.91    200640\n",
            "   macro avg       0.91      0.92      0.91    200640\n",
            "weighted avg       0.92      0.91      0.92    200640\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The model performs very well, so we can use it to predict unknown data."
      ],
      "metadata": {
        "id": "B3TMrdj3N_La"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#XGBoost"
      ],
      "metadata": {
        "id": "u2xkZKrAqFw4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "XG_model= XGBClassifier()\n",
        "XG_model= XG_model.fit(X_train, y_train)\n",
        "#Making prediction\n",
        "XG_pred = XG_model.predict(X_test)\n",
        "XG_prob = XG_model.predict_proba(X_test)[:,1]"
      ],
      "metadata": {
        "id": "nUPXIaN-bB0f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model** **Evaluation**"
      ],
      "metadata": {
        "id": "DBKtqu2yq99P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation\n",
        "r_XG= recall_score(y_test, XG_pred)\n",
        "print(\"recall_score : \", r_XG)\n",
        "\n",
        "p_XG= precision_score(y_test, XG_pred)\n",
        "print(\"precision_score :\",p_XG)\n",
        "\n",
        "f1_XG= f1_score(y_test, XG_pred)\n",
        "print(\"f1_score :\", f1_XG)\n",
        "\n",
        "A_XG= accuracy_score( y_test, XG_pred)\n",
        "print(\"accuracy_score :\",A_XG)\n",
        "\n",
        "acu_XG = roc_auc_score(XG_pred, y_test)\n",
        "print(\"ROC_AUC Score:\",acu_XG)"
      ],
      "metadata": {
        "id": "rkSS4_robB3G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "abd2fac9-34d7-4bde-d875-8931e7df6da5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall_score :  0.9335847329459523\n",
            "precision_score : 0.7339507669683435\n",
            "f1_score : 0.8218178633208523\n",
            "accuracy_score : 0.7972139154704945\n",
            "ROC_AUC Score: 0.8211299735197491\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fpr, tpr, _ = roc_curve(y_test, XG_prob)\n",
        "\n",
        "plt.title('XGBoost ROC curve')\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.plot(fpr,tpr)\n",
        "plt.plot((0,1), linestyle=\"--\",color='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "d1850321-b0c8-4790-9818-4bf20c159ca4",
        "id": "lhDpZV46vp-K"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**confusion_matrix**"
      ],
      "metadata": {
        "id": "5UhMliX-rNgb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matrix= confusion_matrix(y_test,XG_pred)\n",
        "print(matrix)\n",
        "sns.heatmap(matrix ,annot=True, fmt='g')"
      ],
      "metadata": {
        "id": "y7cPsD81bB5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "760f8785-1c90-4055-8f04-cb7456cbe9c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[66124 34012]\n",
            " [ 6675 93829]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faac94b5b50>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the confusion matrix we see that the model is a bit better with predicting positive responses."
      ],
      "metadata": {
        "id": "5xmpJPxTPCyO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(XG_pred, y_test))"
      ],
      "metadata": {
        "id": "eDcDaKxsbB9C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04e4390e-c929-484a-ba76-562372cc2a9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.66      0.91      0.76     72799\n",
            "           1       0.93      0.73      0.82    127841\n",
            "\n",
            "    accuracy                           0.80    200640\n",
            "   macro avg       0.80      0.82      0.79    200640\n",
            "weighted avg       0.83      0.80      0.80    200640\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Comparing  the Model\n",
        "\n"
      ],
      "metadata": {
        "id": "-AIAbTVu2WYS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "com= ['Logistic Regression','Randomforest','XGBClassifier']\n",
        "data={'Accuracy':[A_lgt,A_rf,A_XG],'Recall':[r_lgt,r_rf, r_XG],'Precision':[p_lgt, p_rf, p_XG], 'f1_score':[f1_lgt, f1_rf, f1_XG],'ROC_AUC':[acu_lgt, acu_rf, acu_XG]}\n",
        "result=pd.DataFrame(data=data, index=com)\n",
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "rZd5qFQe2Um8",
        "outputId": "b18345ab-f1c1-4a91-d832-79516998a232"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Accuracy    Recall  Precision  f1_score   ROC_AUC\n",
              "Logistic Regression  0.785581  0.976100   0.707189  0.820165  0.834198\n",
              "Randomforest         0.914912  0.985274   0.863963  0.920640  0.923379\n",
              "XGBClassifier        0.797214  0.933585   0.733951  0.821818  0.821130"
            ],
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              "      <th>Accuracy</th>\n",
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              "      <td>0.785581</td>\n",
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              "      <td>0.914912</td>\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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-d57c0911-e456-46de-9282-5bea8f74fc30 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-d57c0911-e456-46de-9282-5bea8f74fc30');\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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Conclusion"
      ],
      "metadata": {
        "id": "PgnI-SVNZLVK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Starting from loading our dataset, we initially checked for null values and duplicates. There were no null values and duplicates so treatment of such was not required.\n",
        "* Through Exploratory Data Analysis,we observed that customers belonging to youngAge are more interested in vehicle response.while Young people below 30 are not interested in vehicle insurance. We observed that customers having vehicles older than 2 years are more likely to be interested in vehicle insurance. Similarly, customers having damaged vehicles are more likely to be interested in vehicle insurance. \n",
        "* The variable such as Age, Previously_insured,Annual_premium are more afecting the target variable.\n",
        "* For Feature Selection, we applied the Mutual Information technique. Here we observed that Previously_Insured is the most important feature and has the highest impact on the dependent feature and there is no correlation between the two.\n",
        "* We observed that the target variable was highly imbalanced.So this issue was solved by using Random Over Sample resampling technique. \n",
        "* we applied feature scaling techniques to normalize our data to bring all features on the same scale and make it easier to process by ML algorithms.\n",
        "* Further, we applied Machine Learning Algorithms to determine whether a customer would be interested in Vehicle Insurance.For the logistic regression we got an accuracy of 78% and for the XGBClassifier we got the aacuracy of 79% whereas,.We are getting the highest accuracy of about 91% and ROC_AUC score of 92% with random forest So, From this we can conclude that random forest is the best models as compare to the other models.  "
      ],
      "metadata": {
        "id": "NYK6KTthZOQL"
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