Insurance_Prediction_(Team_Zeal).ipynb

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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mariruwanjiru/Insurance-Prediction/blob/main/Insurance_Prediction_(Team_Zeal).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 207,
      "id": "7a9cbcaf-a7c5-4121-ab3e-c7c35c913f24",
      "metadata": {
        "id": "7a9cbcaf-a7c5-4121-ab3e-c7c35c913f24"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 208,
      "id": "210c3dd1-9ab2-4b6d-a235-fedf3b13346d",
      "metadata": {
        "id": "210c3dd1-9ab2-4b6d-a235-fedf3b13346d"
      },
      "outputs": [],
      "source": [
        "#load the dataset\n",
        "train_data = pd.read_csv(\"train_data.csv\")\n",
        "test_data = pd.read_csv(\"test_data.csv\")\n",
        "variables = pd.read_csv(\"VariableDescription.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 209,
      "id": "747ba3da-400b-46ce-afb4-76be0389f87c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "747ba3da-400b-46ce-afb4-76be0389f87c",
        "outputId": "4c62a589-b8c2-4de0-9543-191865637a72"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train datashape : (7160, 14)\n",
            "test datashape : (3069, 13)\n"
          ]
        }
      ],
      "source": [
        "#Observe the shape of our dataset\n",
        "print('train datashape :',train_data.shape)\n",
        "print('test datashape :', test_data.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 210,
      "id": "500c953d-ebae-4705-b366-d687e9b63934",
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        "outputId": "52b4e498-0370-4ea0-f4e6-9ee503cbfdb7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Customer Id  YearOfObservation  Insured_Period  Residential  \\\n",
              "0      H14663               2013             1.0            0   \n",
              "1       H2037               2015             1.0            0   \n",
              "2       H3802               2014             1.0            0   \n",
              "3       H3834               2013             1.0            0   \n",
              "4       H5053               2014             1.0            0   \n",
              "\n",
              "  Building_Painted Building_Fenced Garden Settlement  Building Dimension  \\\n",
              "0                N               V      V          U               290.0   \n",
              "1                V               N      O          R               490.0   \n",
              "2                N               V      V          U               595.0   \n",
              "3                V               V      V          U              2840.0   \n",
              "4                V               N      O          R               680.0   \n",
              "\n",
              "   Building_Type  Date_of_Occupancy NumberOfWindows Geo_Code  Claim  \n",
              "0              1             1960.0               .     1053      0  \n",
              "1              1             1850.0               4     1053      0  \n",
              "2              1             1960.0               .     1053      0  \n",
              "3              1             1960.0               .     1053      0  \n",
              "4              1             1800.0               3     1053      0  "
            ],
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              "summary": "{\n  \"name\": \"train_data\",\n  \"rows\": 7160,\n  \"fields\": [\n    {\n      \"column\": \"Customer Id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7160,\n        \"samples\": [\n          \"H14373\",\n          \"H5917\",\n          \"H1367\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearOfObservation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2012,\n        \"max\": 2016,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2015,\n          2016,\n          2014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insured_Period\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23975565166111013,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 401,\n        \"samples\": [\n          0.052054795,\n          0.2,\n          0.081967213\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Residential\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Building_Painted\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"V\",\n          \"N\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Building_Fenced\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"N\",\n          \"V\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Garden\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"O\",\n          \"V\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Settlement\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"R\",\n          \"U\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Building Dimension\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2278.157745028028,\n        \"min\": 1.0,\n        \"max\": 20940.0,\n        \"num_unique_values\": 2043,\n        \"samples\": [\n          783.0,\n          3124.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Building_Type\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date_of_Occupancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36.00201375360148,\n        \"min\": 1545.0,\n        \"max\": 2016.0,\n        \"num_unique_values\": 134,\n        \"samples\": [\n          1913.0,\n          2015.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumberOfWindows\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \">=10\",\n          \"   .\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Geo_Code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1307,\n        \"samples\": [\n          \"91589\",\n          \"82112\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Claim\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 210
        }
      ],
      "source": [
        "#inspect the train data\n",
        "train_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 211,
      "id": "dd3f66f6-6105-4193-94d8-06f8cf2763e6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dd3f66f6-6105-4193-94d8-06f8cf2763e6",
        "outputId": "57535258-2c3d-4353-95d2-17a032480106"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "missing values: Customer Id             0\n",
            "YearOfObservation       0\n",
            "Insured_Period          0\n",
            "Residential             0\n",
            "Building_Painted        0\n",
            "Building_Fenced         0\n",
            "Garden                  7\n",
            "Settlement              0\n",
            "Building Dimension    106\n",
            "Building_Type           0\n",
            "Date_of_Occupancy     508\n",
            "NumberOfWindows         0\n",
            "Geo_Code              102\n",
            "Claim                   0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "#check for missing values\n",
        "print('missing values:', train_data.isnull().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 212,
      "id": "bfac1d67-d2df-4307-b7f2-2e20051bea58",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bfac1d67-d2df-4307-b7f2-2e20051bea58",
        "outputId": "e5c53d92-e8ae-44c9-a9a8-8292500f6231"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Customer Id  YearOfObservation  Insured_Period  Residential  \\\n",
            "0         H11920               2013        1.000000            0   \n",
            "1         H11921               2016        0.997268            0   \n",
            "2          H9805               2013        0.369863            0   \n",
            "3          H7493               2014        1.000000            0   \n",
            "4          H7494               2016        1.000000            0   \n",
            "...          ...                ...             ...          ...   \n",
            "3064      H11583               2015        1.000000            0   \n",
            "3065      H11720               2012        1.000000            0   \n",
            "3066      H11721               2012        1.000000            0   \n",
            "3067      H12408               2013        1.000000            0   \n",
            "3068       H9021               2012        1.000000            0   \n",
            "\n",
            "     Building_Painted Building_Fenced Garden Settlement  Building Dimension  \\\n",
            "0                   V               N      O          R               300.0   \n",
            "1                   V               N      O          R               300.0   \n",
            "2                   V               V      V          U               790.0   \n",
            "3                   V               N      O          R              1405.0   \n",
            "4                   V               N      O          R              1405.0   \n",
            "...               ...             ...    ...        ...                 ...   \n",
            "3064                V               V      V          U                 0.0   \n",
            "3065                V               V      V          U                 0.0   \n",
            "3066                V               V      V          U                 0.0   \n",
            "3067                V               V      V          U                 0.0   \n",
            "3068                V               V      V          U                 0.0   \n",
            "\n",
            "      Building_Type  Date_of_Occupancy NumberOfWindows Geo_Code  \n",
            "0                 1             1960.0               3     3310  \n",
            "1                 1             1960.0               3     3310  \n",
            "2                 1             1960.0               .     3310  \n",
            "3                 1             2004.0               3     3321  \n",
            "4                 1             2004.0               3     3321  \n",
            "...             ...                ...             ...      ...  \n",
            "3064              4             1900.0               .        0  \n",
            "3065              2             1948.0               .        0  \n",
            "3066              2             1993.0               .        0  \n",
            "3067              1             1800.0               .        0  \n",
            "3068              2             1950.0               .        0  \n",
            "\n",
            "[3069 rows x 13 columns]\n"
          ]
        }
      ],
      "source": [
        "test_new =test_data.fillna(0)\n",
        "print(test_new)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 213,
      "id": "3bc38173-6807-4db8-8a15-fe9e776a30b7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3bc38173-6807-4db8-8a15-fe9e776a30b7",
        "outputId": "a1c1d2c3-7b46-4f93-e475-46c1377d1747"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Customer Id  YearOfObservation  Insured_Period  Residential  \\\n",
            "0         H14663               2013        1.000000            0   \n",
            "1          H2037               2015        1.000000            0   \n",
            "2          H3802               2014        1.000000            0   \n",
            "3          H3834               2013        1.000000            0   \n",
            "4          H5053               2014        1.000000            0   \n",
            "...          ...                ...             ...          ...   \n",
            "7155       H5290               2012        1.000000            1   \n",
            "7156       H5926               2013        1.000000            0   \n",
            "7157       H6204               2016        0.038251            0   \n",
            "7158       H6537               2013        1.000000            0   \n",
            "7159       H7470               2014        1.000000            0   \n",
            "\n",
            "     Building_Painted Building_Fenced Garden Settlement  Building Dimension  \\\n",
            "0                   N               V      V          U               290.0   \n",
            "1                   V               N      O          R               490.0   \n",
            "2                   N               V      V          U               595.0   \n",
            "3                   V               V      V          U              2840.0   \n",
            "4                   V               N      O          R               680.0   \n",
            "...               ...             ...    ...        ...                 ...   \n",
            "7155                V               V      V          U                 0.0   \n",
            "7156                V               V      V          U                 0.0   \n",
            "7157                V               V      V          U                 0.0   \n",
            "7158                V               V      V          U                 0.0   \n",
            "7159                V               V      V          U                 0.0   \n",
            "\n",
            "      Building_Type  Date_of_Occupancy NumberOfWindows Geo_Code  Claim  \n",
            "0                 1             1960.0               .     1053      0  \n",
            "1                 1             1850.0               4     1053      0  \n",
            "2                 1             1960.0               .     1053      0  \n",
            "3                 1             1960.0               .     1053      0  \n",
            "4                 1             1800.0               3     1053      0  \n",
            "...             ...                ...             ...      ...    ...  \n",
            "7155              1             2001.0               .        0      0  \n",
            "7156              2             1980.0               .        0      1  \n",
            "7157              1             1992.0               .        0      0  \n",
            "7158              1             1972.0               .        0      0  \n",
            "7159              1             2004.0               .        0      0  \n",
            "\n",
            "[7160 rows x 14 columns]\n"
          ]
        }
      ],
      "source": [
        "# lets deal with the missing values\n",
        "train_new =train_data.fillna(0)\n",
        "print(train_new)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 214,
      "id": "9874b302-8b62-4c10-897f-81af61acbd68",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9874b302-8b62-4c10-897f-81af61acbd68",
        "outputId": "75c91315-dbbd-4c6a-ceb9-5e4d583dadf5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "missing values: Customer Id           0\n",
            "YearOfObservation     0\n",
            "Insured_Period        0\n",
            "Residential           0\n",
            "Building_Painted      0\n",
            "Building_Fenced       0\n",
            "Garden                0\n",
            "Settlement            0\n",
            "Building Dimension    0\n",
            "Building_Type         0\n",
            "Date_of_Occupancy     0\n",
            "NumberOfWindows       0\n",
            "Geo_Code              0\n",
            "Claim                 0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print('missing values:', train_new.isnull().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 215,
      "id": "1fe6b54a-20c0-431c-8613-31acac84de8c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "1fe6b54a-20c0-431c-8613-31acac84de8c",
        "outputId": "7910e193-32b6-4d44-887c-b50f9dcebc27"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Customer Id  YearOfObservation  Insured_Period  Residential  \\\n",
              "0      H14663               2013             1.0            0   \n",
              "1       H2037               2015             1.0            0   \n",
              "2       H3802               2014             1.0            0   \n",
              "3       H3834               2013             1.0            0   \n",
              "4       H5053               2014             1.0            0   \n",
              "\n",
              "  Building_Painted Building_Fenced Garden Settlement  Building Dimension  \\\n",
              "0                N               V      V          U               290.0   \n",
              "1                V               N      O          R               490.0   \n",
              "2                N               V      V          U               595.0   \n",
              "3                V               V      V          U              2840.0   \n",
              "4                V               N      O          R               680.0   \n",
              "\n",
              "   Building_Type  Date_of_Occupancy NumberOfWindows Geo_Code  Claim  \n",
              "0              1             1960.0               .     1053      0  \n",
              "1              1             1850.0               4     1053      0  \n",
              "2              1             1960.0               .     1053      0  \n",
              "3              1             1960.0               .     1053      0  \n",
              "4              1             1800.0               3     1053      0  "
            ],
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              "      <th>Customer Id</th>\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
              "\n",
              "<div id=\"df-9396b89f-7237-4342-910f-4993d42346ec\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9396b89f-7237-4342-910f-4993d42346ec')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9396b89f-7237-4342-910f-4993d42346ec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_new",
              "summary": "{\n  \"name\": \"train_new\",\n  \"rows\": 7160,\n  \"fields\": [\n    {\n      \"column\": \"Customer Id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7160,\n        \"samples\": [\n          \"H14373\",\n          \"H5917\",\n          \"H1367\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearOfObservation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2012,\n        \"max\": 2016,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2015,\n          2016,\n          2014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insured_Period\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23975565166111013,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 401,\n        \"samples\": [\n          0.052054795,\n          0.2,\n          0.081967213\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Residential\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Building_Painted\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"V\",\n          \"N\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Building_Fenced\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"N\",\n          \"V\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Garden\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"V\",\n          \"O\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Settlement\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"R\",\n          \"U\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Building Dimension\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2272.645786064534,\n        \"min\": 0.0,\n        \"max\": 20940.0,\n        \"num_unique_values\": 2044,\n        \"samples\": [\n          5657.0,\n          864.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Building_Type\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date_of_Occupancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 505.58368509149943,\n        \"min\": 0.0,\n        \"max\": 2016.0,\n        \"num_unique_values\": 135,\n        \"samples\": [\n          1600.0,\n          2015.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumberOfWindows\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \">=10\",\n          \"   .\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Geo_Code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1308,\n        \"samples\": [\n          \"78327\",\n          \"82121\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Claim\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 215
        }
      ],
      "source": [
        "train_new.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 216,
      "id": "e9a1aa34-8f03-40a3-b756-0c60c710a695",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "e9a1aa34-8f03-40a3-b756-0c60c710a695",
        "outputId": "0dcb36fc-0e77-4fd4-bd30-93546a67b5e4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Variable                                        Description\n",
              "0          Customer Id        Identification number for the Policy holder\n",
              "1    YearOfObservation         year of observation for the insured policy\n",
              "2       Insured_Period  duration of insurance policy in Olusola Insura...\n",
              "3          Residential      is the building a residential building or not\n",
              "4     Building_Painted  is the building painted or not (N-Painted, V-N...\n",
              "5      Building_Fenced  is the building fence or not (N-Fenced, V-Not ...\n",
              "6               Garden  building has garden or not (V-has garden; O-no...\n",
              "7           Settlement  Area where the building is located. (R- rural ...\n",
              "8   Building Dimension                 Size of the insured building in m2\n",
              "9        Building_Type             The type of building (Type 1, 2, 3, 4)\n",
              "10   Date_of_Occupancy                   date building was first occupied\n",
              "11     NumberOfWindows                  number of windows in the building\n",
              "12            Geo Code          Geographical Code of the Insured building\n",
              "13               Claim  target variable. (0: no claim, 1: at least one..."
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e3ab14f9-e1dd-4ca1-8b2b-40e38da46c47\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Variable</th>\n",
              "      <th>Description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Customer Id</td>\n",
              "      <td>Identification number for the Policy holder</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>YearOfObservation</td>\n",
              "      <td>year of observation for the insured policy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Insured_Period</td>\n",
              "      <td>duration of insurance policy in Olusola Insura...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Residential</td>\n",
              "      <td>is the building a residential building or not</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Building_Painted</td>\n",
              "      <td>is the building painted or not (N-Painted, V-N...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Building_Fenced</td>\n",
              "      <td>is the building fence or not (N-Fenced, V-Not ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Garden</td>\n",
              "      <td>building has garden or not (V-has garden; O-no...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Settlement</td>\n",
              "      <td>Area where the building is located. (R- rural ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Building Dimension</td>\n",
              "      <td>Size of the insured building in m2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Building_Type</td>\n",
              "      <td>The type of building (Type 1, 2, 3, 4)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Date_of_Occupancy</td>\n",
              "      <td>date building was first occupied</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>NumberOfWindows</td>\n",
              "      <td>number of windows in the building</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Geo Code</td>\n",
              "      <td>Geographical Code of the Insured building</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Claim</td>\n",
              "      <td>target variable. (0: no claim, 1: at least one...</td>\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "        document.querySelector('#df-e3ab14f9-e1dd-4ca1-8b2b-40e38da46c47 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-e3ab14f9-e1dd-4ca1-8b2b-40e38da46c47');\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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              "\n",
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              "            title=\"Suggest charts\"\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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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a75f80d9-41b5-402c-ad5b-2ca3332be453 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_2e73fafb-e54c-4cb4-a91c-a1b8ce204780\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('variables')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_2e73fafb-e54c-4cb4-a91c-a1b8ce204780 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('variables');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "variables",
              "summary": "{\n  \"name\": \"variables\",\n  \"rows\": 14,\n  \"fields\": [\n    {\n      \"column\": \"Variable\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"Building_Type\",\n          \"NumberOfWindows\",\n          \"Customer Id\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"The type of building (Type 1, 2, 3, 4)\",\n          \"number of windows in the building\",\n          \"Identification number for the Policy holder\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 216
        }
      ],
      "source": [
        "variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 217,
      "id": "eabe28ec-3176-48a3-88ce-7633bc48edee",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "eabe28ec-3176-48a3-88ce-7633bc48edee",
        "outputId": "09ef60e1-9cf8-4b17-b5da-7a10f273656b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x78810435fc40>"
            ]
          },
          "metadata": {},
          "execution_count": 217
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 511.111x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# explore target distribution\n",
        "sns.catplot(x=\"Claim\", kind= \"count\", data = train_new)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 218,
      "id": "77aa5399-c133-47f7-a411-e823574e7e0f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "77aa5399-c133-47f7-a411-e823574e7e0f",
        "outputId": "2269e549-1083-46fa-d913-ee9b3dc6ba88"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7160 entries, 0 to 7159\n",
            "Data columns (total 14 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   Customer Id         7160 non-null   object \n",
            " 1   YearOfObservation   7160 non-null   int64  \n",
            " 2   Insured_Period      7160 non-null   float64\n",
            " 3   Residential         7160 non-null   int64  \n",
            " 4   Building_Painted    7160 non-null   object \n",
            " 5   Building_Fenced     7160 non-null   object \n",
            " 6   Garden              7160 non-null   object \n",
            " 7   Settlement          7160 non-null   object \n",
            " 8   Building Dimension  7160 non-null   float64\n",
            " 9   Building_Type       7160 non-null   int64  \n",
            " 10  Date_of_Occupancy   7160 non-null   float64\n",
            " 11  NumberOfWindows     7160 non-null   object \n",
            " 12  Geo_Code            7160 non-null   object \n",
            " 13  Claim               7160 non-null   int64  \n",
            "dtypes: float64(3), int64(4), object(7)\n",
            "memory usage: 783.2+ KB\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "#understand the dataset\n",
        "#show some more information about the dataset\n",
        "print(train_new.info())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6bc61350-bb45-4489-8ec6-b484b95c9635",
      "metadata": {
        "id": "6bc61350-bb45-4489-8ec6-b484b95c9635"
      },
      "source": [
        "DATA PREPARATION FOR MACHINE LEARNING\n",
        "PERFORM DATA CLEANING AND PREPROCESSING\n",
        "Building training sets - 3%\n",
        "Cleaning and organizing - 60%\n",
        "Collecting data sets - 19%\n",
        "Mining data for patterns -9%\n",
        "Refining algorithms - 4%\n",
        "other 5%"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 219,
      "id": "e0a74c6c-41f4-4f99-b445-73ea54777e50",
      "metadata": {
        "id": "e0a74c6c-41f4-4f99-b445-73ea54777e50"
      },
      "outputs": [],
      "source": [
        "#seperate the independent variables and target from train data\n",
        "# import preprocessing module\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 220,
      "id": "a927aa8a-131f-4ec8-a9dd-891b15f7fbb2",
      "metadata": {
        "id": "a927aa8a-131f-4ec8-a9dd-891b15f7fbb2"
      },
      "outputs": [],
      "source": [
        "#convert target label to numerical Data\n",
        "#this part is not necessary since it is already in numerical form\n",
        "#used it for the purpose of learning\n",
        "#instantiate\n",
        "le = LabelEncoder()\n",
        "#fit the encoder to the data(learning the unique categories and mapping the no. to integers and after fitting it immediately transforms the original categories to their corresponding numerical labels)\n",
        "train_new.loc[:, \"Claim\"] = le.fit_transform(train_new[\"Claim\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 221,
      "id": "da0724d3-e9bc-46a2-b65f-7328d0ed5b50",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "da0724d3-e9bc-46a2-b65f-7328d0ed5b50",
        "outputId": "3ccf977d-0c91-430d-c841-ac74d91f499b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0       0\n",
            "1       0\n",
            "2       0\n",
            "3       0\n",
            "4       0\n",
            "       ..\n",
            "7155    0\n",
            "7156    1\n",
            "7157    0\n",
            "7158    0\n",
            "7159    0\n",
            "Name: Claim, Length: 7160, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "#seperate the training features from target\n",
        "x_train = train_new.drop([\"Claim\"], axis = 1)\n",
        "y_train = train_new[\"Claim\"]\n",
        "x_test = test_new\n",
        "print(y_train)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "# Identify categorical and numerical columns\n",
        "categorical_cols = x_train.select_dtypes(include=['object', 'category']).columns\n",
        "numerical_cols = x_train.select_dtypes(include=['number']).columns\n",
        "\n",
        "# Impute missing values\n",
        "numerical_imputer = SimpleImputer(strategy='median')\n",
        "categorical_imputer = SimpleImputer(strategy='most_frequent')\n",
        "\n",
        "x_train[numerical_cols] = numerical_imputer.fit_transform(x_train[numerical_cols])\n",
        "x_train[categorical_cols] = categorical_imputer.fit_transform(x_train[categorical_cols])\n",
        "x_test[numerical_cols] = numerical_imputer.transform(x_test[numerical_cols])\n",
        "x_test[categorical_cols] = categorical_imputer.transform(x_test[categorical_cols])\n",
        "\n",
        "# Convert all categorical columns to string type before one-hot encoding\n",
        "# This ensures uniformity of data types for the encoder\n",
        "x_train[categorical_cols] = x_train[categorical_cols].astype(str)\n",
        "x_test[categorical_cols] = x_test[categorical_cols].astype(str)\n",
        "\n",
        "# One-hot encode categorical columns\n",
        "encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
        "encoded_train = pd.DataFrame(encoder.fit_transform(x_train[categorical_cols]))\n",
        "encoded_test = pd.DataFrame(encoder.transform(x_test[categorical_cols]))\n",
        "\n",
        "\n",
        "# Align indices\n",
        "encoded_train.index = x_train.index\n",
        "encoded_test.index = x_test.index\n",
        "\n",
        "# Drop the original categorical columns and concatenate encoded columns\n",
        "x_train = x_train.drop(categorical_cols, axis=1)\n",
        "x_test = x_test.drop(categorical_cols, axis=1)\n",
        "\n",
        "x_train = pd.concat([x_train, encoded_train], axis=1)\n",
        "x_test = pd.concat([x_test, encoded_test], axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lwkS8QOMQ53E",
        "outputId": "29e546bb-fbd1-49fc-d875-34acf6bfb9ff"
      },
      "id": "lwkS8QOMQ53E",
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a9ea3ebf-3c34-4b5d-8900-8e9a28f85ae7",
      "metadata": {
        "id": "a9ea3ebf-3c34-4b5d-8900-8e9a28f85ae7"
      },
      "source": [
        "WE NEED TO SELECT ONLY IMPORTANT FEATURES THAT CAN INCREASE THE PERFORMANCE OF A MODEL"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3b416c0b-bfbd-4dec-bd23-a1861453144c",
      "metadata": {
        "id": "3b416c0b-bfbd-4dec-bd23-a1861453144c"
      },
      "source": [
        "MODEL BUILDING AND EXPERIMENTS.\n",
        "A portion of the train dataset will be used to evaluate our models and find the best one that performs well before using it in the test dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 223,
      "id": "a77c0a4b-1d95-4a21-9e65-35756d9fadd5",
      "metadata": {
        "id": "a77c0a4b-1d95-4a21-9e65-35756d9fadd5"
      },
      "outputs": [],
      "source": [
        "#split datasets into training and testing subsets that performing cross-validation and selecting hyperparameters for models\n",
        "import sklearn.model_selection"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocessing_data(data):\n",
        "  # Perform your preprocessing steps here\n",
        "  # For example:\n",
        "\n",
        "  # data = data.fillna(method='ffill')\n",
        "  return data\n",
        "\n",
        "# Preprocess the training data\n",
        "processed_train = preprocessing_data(x_train)\n",
        "processed_test = preprocessing_data(x_test)\n",
        "\n"
      ],
      "metadata": {
        "id": "MvlG0ZyKSeUB"
      },
      "id": "MvlG0ZyKSeUB",
      "execution_count": 225,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 226,
      "id": "4c799918-a5a2-4700-abbb-7a064bcef29c",
      "metadata": {
        "id": "4c799918-a5a2-4700-abbb-7a064bcef29c"
      },
      "outputs": [],
      "source": [
        "#split train_data\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_val,y_train,y_val =  train_test_split(processed_train,y_train,stratify = y_train,test_size =0.3, random_state = 42)\n",
        "#y_train = Target(dependent)variable\n",
        "#processed_train= The preprocessed feature,datset(independent variable)\n",
        "#stratify = useful in classification problems\n",
        "#test_size = 0.3(30% of data is used for validation and 70% for training\n",
        "#random_state = 42 (ensures reproducibility by fixing the random seed for shuffling the data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 227,
      "id": "adac4f14-60b9-44c6-8e27-e7534d2abb85",
      "metadata": {
        "id": "adac4f14-60b9-44c6-8e27-e7534d2abb85",
        "outputId": "c1b8b573-8eb3-4b65-bd66-a42ea9c50278",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC: 0.68\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# train the model\n",
        "# Train a binary classifier (RandomForest)\n",
        "# Convert all column names to strings\n",
        "x_train.columns = x_train.columns.astype(str)\n",
        "# Convert all column names to strings\n",
        "x_val.columns = x_val.columns.astype(str)\n",
        "\n",
        "# Now, try fitting the model again\n",
        "clf.fit(x_train, y_train)\n",
        "# Now, try fitting the model again\n",
        "clf.fit(x_train, y_train)\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf = RandomForestClassifier(random_state=42)\n",
        "clf.fit(x_train, y_train)\n",
        "\n",
        "# Predict probabilities for the positive class\n",
        "y_pred = clf.predict_proba(x_val)[:, 1]\n",
        "\n",
        "# ROC and AUC\n",
        "from sklearn.metrics import roc_curve, auc, RocCurveDisplay\n",
        "\n",
        "# Calculate ROC curve\n",
        "fpr, tpr, thresholds = roc_curve(y_val, y_pred)\n",
        "\n",
        "# Calculate AUC\n",
        "roc_auc = auc(fpr, tpr)\n",
        "print(f\"AUC: {roc_auc:.2f}\")\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "24ddb6b9-a25e-4398-9615-9041ddc9bd8e",
      "metadata": {
        "id": "24ddb6b9-a25e-4398-9615-9041ddc9bd8e"
      },
      "source": [
        "XGBoost model performs well on predicting class 0 and performs poorly on predicting class 1 , it may be caused by the imbalanced of data provided(the target variable has more 'No' values than 'Yes' values .You can learn the best way with imbalanced data here.\n",
        "One way to increase the model performance is by applying the grid search method as an approach to parameter tuning that will methodically build and evaluate a model for each combination of algorithm parameters specified in a grid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 228,
      "id": "88550704-fd5f-4387-aed9-ce6a79982f3f",
      "metadata": {
        "id": "88550704-fd5f-4387-aed9-ce6a79982f3f",
        "outputId": "c2995fcb-8e52-4051-bb51-5302d54e19e0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal threshold: 0.19\n"
          ]
        }
      ],
      "source": [
        "#determine the optimal threshold\n",
        "# Determine optimal threshold\n",
        "optimal_idx = np.argmax(tpr - fpr)\n",
        "optimal_threshold = thresholds[optimal_idx]\n",
        "print(f'Optimal threshold: {optimal_threshold:.2f}')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 229,
      "id": "24bccf27-eab9-4ece-ba2c-a1eb53dc6d13",
      "metadata": {
        "id": "24bccf27-eab9-4ece-ba2c-a1eb53dc6d13",
        "outputId": "6d84bdbe-46a9-4ebc-b42f-8b291a63692a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: 0.40\n",
            "Recall: 0.51\n",
            "F1 Score: 0.45\n",
            "Confusion Matrix:\n",
            "[[1274  384]\n",
            " [ 239  251]]\n"
          ]
        }
      ],
      "source": [
        "#evaluate the model performance\n",
        "from sklearn.metrics import precision_recall_fscore_support, confusion_matrix\n",
        "\n",
        "# Evaluate performance at the optimal threshold\n",
        "y_pred_optimal = [1 if prob >= optimal_threshold else 0 for prob in y_pred]\n",
        "precision, recall, f1, _ = precision_recall_fscore_support(y_val, y_pred_optimal, average='binary')\n",
        "\n",
        "print(f\"Precision: {precision:.2f}\")\n",
        "print(f\"Recall: {recall:.2f}\")\n",
        "print(f\"F1 Score: {f1:.2f}\")\n",
        "\n",
        "# Display the confusion matrix\n",
        "cm = confusion_matrix(y_val, y_pred_optimal)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(cm)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 230,
      "id": "5f5dcbb4-f133-4c28-9726-d315782370f6",
      "metadata": {
        "id": "5f5dcbb4-f133-4c28-9726-d315782370f6",
        "outputId": "edb5bc58-defd-4c4c-f574-ec5835dd9614",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7756052141527002"
            ]
          },
          "metadata": {},
          "execution_count": 230
        }
      ],
      "source": [
        "clf.score(x_val,y_val)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 231,
      "id": "79bbc760-f444-4ced-b035-0bc38b0276fe",
      "metadata": {
        "id": "79bbc760-f444-4ced-b035-0bc38b0276fe",
        "outputId": "47973390-e3c9-49ea-b2f0-aa719caee312",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters: {'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 150}\n",
            "Best model AUC: 0.68\n"
          ]
        }
      ],
      "source": [
        "# refine the model\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import roc_auc_score\n",
        "# Define the parameter grid\n",
        "param_grid = {\n",
        "    'n_estimators': [100, 150],  # Reduced the range to avoid long computation\n",
        "    'max_depth': [None, 10],\n",
        "    'min_samples_split': [2, 5],\n",
        "    'min_samples_leaf': [1, 2]\n",
        "}\n",
        "\n",
        "# Initialize the GridSearchCV with cross-validation\n",
        "grid_search = GridSearchCV(estimator=RandomForestClassifier(random_state=42),\n",
        "                           param_grid=param_grid,\n",
        "                           cv=5,\n",
        "                           scoring='roc_auc',\n",
        "                           n_jobs=-1)  # Use all processors\n",
        "\n",
        "# Fit the model\n",
        "grid_search.fit(x_train, y_train)\n",
        "\n",
        "# Extract the best model\n",
        "best_model = grid_search.best_estimator_\n",
        "print(f\"Best parameters: {grid_search.best_params_}\")\n",
        "\n",
        "# Evaluate the best model\n",
        "best_y_probs = best_model.predict_proba(x_val)[:, 1]\n",
        "best_auc_score = roc_auc_score(y_val, best_y_probs)\n",
        "print(f\"Best model AUC: {best_auc_score:.2f}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 232,
      "id": "494af375-56c1-44d3-8001-a8bab279aa65",
      "metadata": {
        "id": "494af375-56c1-44d3-8001-a8bab279aa65",
        "outputId": "96150354-14e7-452a-f3b3-567fd7ce514a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error rate of the XGB classifier:  0.22811918063314707\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "\n",
        "# fit by setting best parameters and Evaluate model\n",
        "best_y_probs = RandomForestClassifier(max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=150)\n",
        "\n",
        "best_y_probs.fit(x_train, y_train)\n",
        "y_pred = best_y_probs.predict(x_val)\n",
        "\n",
        "# Get error rate\n",
        "print(\"Error rate of the XGB classifier: \", 1 - accuracy_score(y_val, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 233,
      "id": "9f040c98-9a58-4a9b-8045-36c28cdf18a8",
      "metadata": {
        "id": "9f040c98-9a58-4a9b-8045-36c28cdf18a8",
        "outputId": "99143c69-80ec-4791-9a48-eca65cd04a3c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-validated AUC scores: [0.71270438 0.68372206 0.68696227 0.69470164 0.69606793]\n",
            "Mean AUC: 0.69\n"
          ]
        }
      ],
      "source": [
        "#validate the model\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Cross-validation\n",
        "cv_scores = cross_val_score(best_model, x_train, y_train, cv=5, scoring='roc_auc')\n",
        "print(f\"Cross-validated AUC scores: {cv_scores}\")\n",
        "print(f\"Mean AUC: {cv_scores.mean():.2f}\")\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#deploy the model\n",
        "import joblib\n",
        "\n",
        "# Save the model to a file\n",
        "joblib.dump(best_model, 'best_model.pkl')\n",
        "\n",
        "# Load the model from the file\n",
        "loaded_model = joblib.load('best_model.pkl')\n",
        "\n",
        "# Ensure all column names in x_test are strings\n",
        "processed_test.columns = processed_test.columns.astype(str)  # Convert column names to strings\n",
        "\n",
        "# Make predictions with the loaded model\n",
        "test_predictions = loaded_model.predict_proba(processed_test)\n",
        "print(f\"Predictions: {test_predictions}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TRl77oqVgoFs",
        "outputId": "24eeed6c-7506-40fe-90b8-8ad33d6aabf7"
      },
      "id": "TRl77oqVgoFs",
      "execution_count": 234,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predictions: [[0.97333333 0.02666667]\n",
            " [0.97333333 0.02666667]\n",
            " [0.98666667 0.01333333]\n",
            " ...\n",
            " [0.83333333 0.16666667]\n",
            " [0.83333333 0.16666667]\n",
            " [0.86666667 0.13333333]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 235,
      "id": "152e5605-3c7f-4b0c-a8db-fa76e7f32af4",
      "metadata": {
        "id": "152e5605-3c7f-4b0c-a8db-fa76e7f32af4",
        "outputId": "cd5fd9e9-6314-40b4-99d7-1a8ae6825592",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision-Recall AUC: 0.41\n"
          ]
        }
      ],
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "\n",
        "# Handle imbalanced data with SMOTE\n",
        "smote = SMOTE(random_state=42)\n",
        "x_resampled, y_resampled = smote.fit_resample(x_train, y_train)\n",
        "\n",
        "# Train model on resampled data\n",
        "best_model.fit(x_resampled, y_resampled)\n",
        "resampled_y_probs = best_model.predict_proba(x_val)[:, 1]\n",
        "\n",
        "# Calculate and plot the precision-recall curve\n",
        "precision, recall, _ = precision_recall_curve(y_val, resampled_y_probs)\n",
        "plt.figure()\n",
        "plt.plot(recall, precision, marker='.')\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('Precision-Recall Curve')\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "# Calculate Precision-Recall AUC\n",
        "from sklearn.metrics import auc\n",
        "pr_auc = auc(recall, precision)\n",
        "print(f\"Precision-Recall AUC: {pr_auc:.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "processed_test.shape\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3LnECMlL25jr",
        "outputId": "494683b5-2e2a-42c4-c967-b13bff4d2f62"
      },
      "id": "3LnECMlL25jr",
      "execution_count": 248,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3069, 8494)"
            ]
          },
          "metadata": {},
          "execution_count": 248
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# If you need to save the predictions\n",
        "output = pd.DataFrame({'Customer Id': test_data['Customer Id'],\n",
        "                       'Predicted': test_predictions[:, 1]}) # Select the probabilities for the positive class (index 1)\n",
        "output.to_csv('First Submission6.csv', index=False)"
      ],
      "metadata": {
        "id": "yA5xHwNi6ow2"
      },
      "id": "yA5xHwNi6ow2",
      "execution_count": 251,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.7"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}