Machine_Learning_Project_Group10.ipynb

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        "<a href=\"https://colab.research.google.com/github/nhatanhle20/Machine-learning-project-Predicting-life-insurance-cost/blob/main/Machine_Learning_Project_Group10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "#Machine Learning Project: Predicting Life Insurance in VietNam\n"
      ],
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        "id": "Hgf63hGulsp2"
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        "**1. Introduction**\n",
        "\n",
        "Life insurance plays a crucial role in providing financial security and peace of mind to individuals and their families. In Vietnam, understanding the factors that influence life insurance premiums is essential for both insurers and policyholders. Accurate prediction of life insurance costs allows insurers to set fair premiums, manage risk effectively, and ensure profitability. For policyholders, it helps in making informed decisions about purchasing policies that best suit their needs.\n",
        "\n",
        " **2. Problem definition**\n",
        "\n",
        "The goal is to develop a predictive model that estimates the annual cost of life insurance premiums for individuals in Vietnam based on various demographic, health, and lifestyle factors. This model should help insurance companies to set premiums that are reflective of individual risk profiles and provide insights into the key drivers of life insurance costs.\n",
        "\n",
        "**3. Data Overveiw**\n",
        "\n",
        "Overview: This dataset is curated to support the development of predictive models for estimating life insurance premiums. It encompasses various attributes that influence the pricing decisions in life insurance policies.\n",
        "\n",
        "\n",
        "Source: Collected from historical records of insurance applications and synthetic data to fill gaps.\n",
        "\n",
        "\n",
        "Dataset Structure:\n",
        "\n",
        "Rows: 1,000 policyholders.\n",
        "\n",
        "Columns: 5 attributes.\n",
        "\n",
        "Key Features:\n",
        "\n",
        "Age: Age of the policyholder (Numeric).\n",
        "\n",
        "Gender: Gender of the policyholder (Categorical).\n",
        "\n",
        "Health Conditions: Pre-existing health conditions (Categorical).\n",
        "\n",
        "Occupation: Job type or profession (Categorical).\n",
        "\n",
        "Insurance Cost: Income level (Numeric).\n",
        "\n",
        "**4. Methodology**"
      ],
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      "cell_type": "markdown",
      "source": [
        "##**Import Library**"
      ],
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        "id": "0BmDZROWl4pz"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "- Analyzed the dataset using following libraries in Python:"
      ],
      "metadata": {
        "id": "HWcgfoGuZ1gb"
      }
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from google.colab import files\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn import metrics\n"
      ],
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        "id": "zqfyKFuEmH3D"
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      "outputs": []
    },
    {
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      "source": [
        "##**Read Data**"
      ],
      "metadata": {
        "id": "tUodXKT9mr9A"
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      "cell_type": "markdown",
      "source": [
        "- Begin, must read the data form CSV file using read_csv()"
      ],
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        "# loading the data form csv file to pandas dataframe\n",
        "upload = files.upload()\n",
        "data = pd.read_csv(\"vietnam_insurance_costs.csv\")\n",
        "# inspecting all row of the dataframe\n",
        "data"
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          "text": [
            "Saving vietnam_insurance_costs.csv to vietnam_insurance_costs.csv\n"
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              "     Age     Sex Health Condition Occupation  Insurance Cost\n",
              "0     56    Male             Good    Group 2      7825131.82\n",
              "1     46  Female          Average    Group 4     14875093.63\n",
              "2     32  Female             Good    Group 1      5918225.80\n",
              "3     60  Female             Good    Group 3      9558453.32\n",
              "4     25    Male             Good    Group 2      6291935.48\n",
              "..   ...     ...              ...        ...             ...\n",
              "995   22    Male             Poor    Group 4     13903113.83\n",
              "996   40    Male             Good    Group 4     16175649.29\n",
              "997   27  Female        Excellent    Group 2      2253618.49\n",
              "998   61    Male             Poor    Group 2      8394113.02\n",
              "999   19    Male          Average    Group 2      6337036.48\n",
              "\n",
              "[1000 rows x 5 columns]"
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              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Health Condition</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>56</td>\n",
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              "      <td>Good</td>\n",
              "      <td>Group 2</td>\n",
              "      <td>7825131.82</td>\n",
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              "      <th>1</th>\n",
              "      <td>46</td>\n",
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              "      <td>Average</td>\n",
              "      <td>Group 4</td>\n",
              "      <td>14875093.63</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>32</td>\n",
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              "      <td>5918225.80</td>\n",
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              "      <td>60</td>\n",
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              "      <th>995</th>\n",
              "      <td>22</td>\n",
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              "      <td>13903113.83</td>\n",
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              "      <th>996</th>\n",
              "      <td>40</td>\n",
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              "      <th>997</th>\n",
              "      <td>27</td>\n",
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              "      <td>2253618.49</td>\n",
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              "      <th>998</th>\n",
              "      <td>61</td>\n",
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              "      <td>8394113.02</td>\n",
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              "      <th>999</th>\n",
              "      <td>19</td>\n",
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              "      <td>Group 2</td>\n",
              "      <td>6337036.48</td>\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-430d907f-cc74-45f9-9392-e7d0c8c0609c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_5ffa18ae-0aa0-4e22-a3f8-45997ebf1a6f\">\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('data')\"\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_5ffa18ae-0aa0-4e22-a3f8-45997ebf1a6f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 18,\n        \"max\": 64,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          24,\n          48,\n          54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Health Condition\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Average\",\n          \"Poor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupation\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Group 4\",\n          \"Group 3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insurance Cost\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4052020.738864036,\n        \"min\": 2253618.49,\n        \"max\": 19714219.43,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          15439443.46,\n          16894466.16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Data Infomation**"
      ],
      "metadata": {
        "id": "kpC4TJcNneMN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Dataset infomation\n",
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qQ7OOvC8njQP",
        "outputId": "3d4ed5c2-3d47-4f8a-8ed7-ecdc5fa86570"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 5 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   Age               1000 non-null   int64  \n",
            " 1   Sex               1000 non-null   object \n",
            " 2   Health Condition  1000 non-null   object \n",
            " 3   Occupation        1000 non-null   object \n",
            " 4   Insurance Cost    1000 non-null   float64\n",
            "dtypes: float64(1), int64(1), object(3)\n",
            "memory usage: 39.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Data cleaning: verify that the dataset did not contain any null values (missing values).\n",
        "\n",
        "=> Here in out dataset, no missing values."
      ],
      "metadata": {
        "id": "TKP7iiTba3Ry"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Missing value\n",
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K-2ccftvXNku",
        "outputId": "8df9aa0e-1efc-4cc9-e237-52521f16295f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                 0\n",
              "Sex                 0\n",
              "Health Condition    0\n",
              "Occupation          0\n",
              "Insurance Cost      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Examine the distribution of categorical data in dataset by printing the counts for each unique category in the specified columns."
      ],
      "metadata": {
        "id": "aib-bFUJraj2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Distribution of each columns data\n",
        "print(data.Age.value_counts())\n",
        "print(\"\")\n",
        "print(data.Sex.value_counts())\n",
        "print(\"\")\n",
        "print(data['Health Condition'].value_counts())\n",
        "print(\"\")\n",
        "print(data.Occupation.value_counts())\n",
        "print(\"\")\n",
        "print(data['Insurance Cost'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bO7JiTWiX1w7",
        "outputId": "97fee15d-dbbb-491b-cd4f-8294a3fe7627"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Age\n",
            "43    33\n",
            "45    30\n",
            "50    30\n",
            "52    28\n",
            "54    27\n",
            "64    27\n",
            "49    25\n",
            "22    25\n",
            "18    25\n",
            "62    25\n",
            "56    25\n",
            "41    25\n",
            "42    24\n",
            "20    24\n",
            "40    24\n",
            "19    24\n",
            "61    23\n",
            "25    23\n",
            "23    23\n",
            "53    23\n",
            "29    22\n",
            "34    22\n",
            "39    22\n",
            "47    21\n",
            "33    21\n",
            "36    20\n",
            "28    20\n",
            "46    19\n",
            "38    19\n",
            "21    19\n",
            "31    19\n",
            "51    18\n",
            "27    18\n",
            "58    18\n",
            "37    18\n",
            "26    18\n",
            "30    18\n",
            "32    18\n",
            "59    17\n",
            "57    17\n",
            "35    16\n",
            "48    16\n",
            "44    16\n",
            "55    16\n",
            "24    14\n",
            "63    13\n",
            "60    12\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Sex\n",
            "Male      526\n",
            "Female    474\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Health Condition\n",
            "Good         270\n",
            "Poor         253\n",
            "Excellent    252\n",
            "Average      225\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Occupation\n",
            "Group 4    271\n",
            "Group 2    248\n",
            "Group 1    243\n",
            "Group 3    238\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Insurance Cost\n",
            "7825131.82    1\n",
            "7452069.33    1\n",
            "7259451.35    1\n",
            "6694432.31    1\n",
            "5493754.84    1\n",
            "             ..\n",
            "7876424.13    1\n",
            "6987909.18    1\n",
            "7179381.33    1\n",
            "7708169.91    1\n",
            "6337036.48    1\n",
            "Name: count, Length: 1000, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Encoding Data**"
      ],
      "metadata": {
        "id": "woc1DPLKY_c5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Prepare data for Machine Learning: machine learning models require all input and output variables to be numeric, we use replace() function to do it"
      ],
      "metadata": {
        "id": "Mff0qFc84XfR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.replace({'Sex': {'Male': 1, 'Female': 0}}, inplace=True)\n",
        "data.replace({'Health Condition': {'Poor': 0, 'Good': 1, 'Excellent': 2, 'Average': 3}}, inplace=True)\n",
        "data.replace({'Occupation': {'Group 1': 1, 'Group 2': 2, 'Group 3': 3, 'Group 4': 4}}, inplace=True)\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "wWWHstkcZDjL",
        "outputId": "16644ccd-19e9-41a5-d598-2eab886bef62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Age  Sex  Health Condition  Occupation  Insurance Cost\n",
              "0     56    1                 1           2      7825131.82\n",
              "1     46    0                 3           4     14875093.63\n",
              "2     32    0                 1           1      5918225.80\n",
              "3     60    0                 1           3      9558453.32\n",
              "4     25    1                 1           2      6291935.48\n",
              "..   ...  ...               ...         ...             ...\n",
              "995   22    1                 0           4     13903113.83\n",
              "996   40    1                 1           4     16175649.29\n",
              "997   27    0                 2           2      2253618.49\n",
              "998   61    1                 0           2      8394113.02\n",
              "999   19    1                 3           2      6337036.48\n",
              "\n",
              "[1000 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-50dbda60-13f0-40ce-bf1b-b8fc664b9038\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Health Condition</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Insurance Cost</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7825131.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>46</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>14875093.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5918225.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>9558453.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>6291935.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>13903113.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>16175649.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2253618.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>8394113.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>6337036.48</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-50dbda60-13f0-40ce-bf1b-b8fc664b9038')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .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",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-50dbda60-13f0-40ce-bf1b-b8fc664b9038 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-50dbda60-13f0-40ce-bf1b-b8fc664b9038');\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",
              "\n",
              "\n",
              "<div id=\"df-7bf4892f-b510-48b3-97a8-554fcd330249\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7bf4892f-b510-48b3-97a8-554fcd330249')\"\n",
              "            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",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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",
              "    }\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-7bf4892f-b510-48b3-97a8-554fcd330249 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_ad8229c2-af64-4e78-97fc-6d83344071af\">\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('data')\"\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_ad8229c2-af64-4e78-97fc-6d83344071af button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 18,\n        \"max\": 64,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          24,\n          48,\n          54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Health Condition\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insurance Cost\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4052020.738864036,\n        \"min\": 2253618.49,\n        \"max\": 19714219.43,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          15439443.46,\n          16894466.16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- After encoding the data,"
      ],
      "metadata": {
        "id": "N5YHgn_Y4yyh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Data Visualization**"
      ],
      "metadata": {
        "id": "SFN9GSXOo5Gp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Setup plot style\n",
        "sns.set(style=\"whitegrid\")"
      ],
      "metadata": {
        "id": "BMQDsTA8NNRq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- **Heat map** is a representation of data, typically presented in the form of a map or diagram where data values are depicted using coulor\n",
        "\n",
        "- **Correlation** is the statistical analysis of the relationship or dependency between two variables. It allows us to study both the strength and direction of the relationship between two sets of variables. Have 2 type: Positive and Negative"
      ],
      "metadata": {
        "id": "ymyK4FDm5HTG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Correlation Matrix HeatMap\n",
        "\n",
        "correlation_matrix = data.corr()\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths = 0.5)\n",
        "plt.title('Correlation Matrix HeatMap')\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "Ppo5kDXuZ8qa",
        "outputId": "cff2de52-3a60-42b1-94fb-b5a09f7e0964"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "source": [
        "# @title Age Frequency\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "data['Age'].plot(kind='hist', bins=20, title='Age')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Frequency')\n",
        "plt.gca().spines[['top', 'right']].set_visible(False)\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "2SsW-SQ5oqZo",
        "outputId": "47a1a41a-fa2e-4a77-d9a1-4820258a4295"
      }
    },
    {
      "source": [
        "# @title Insurance Cost Frequency\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "data['Insurance Cost'].plot(kind='hist', bins=20, title='Insurance Cost')\n",
        "plt.xlabel('Insurance Cost')\n",
        "plt.ylabel('Frequency')\n",
        "plt.gca().spines[['top', 'right']].set_visible(False)\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "EsTR0_jUovhq",
        "outputId": "8c71cfeb-0f79-41d8-d97b-9a61192161d3"
      }
    },
    {
      "source": [
        "# @title Sex Distribution\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "data['Sex'].value_counts().plot(kind='barh', color=sns.color_palette('Dark2'))\n",
        "plt.title('Sex Distribution')\n",
        "plt.xlabel('Count')\n",
        "plt.ylabel('Sex')\n",
        "plt.gca().spines[['top', 'right']].set_visible(False)\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "5Z34Iw7gpS1f",
        "outputId": "2ebe6f5d-5dfd-45a5-a369-9f5723baac8d"
      }
    },
    {
      "source": [
        "# @title Health Condition Distribution\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "data['Health Condition'].value_counts().plot(kind='barh', color=sns.color_palette('Dark2'))\n",
        "plt.title('Health Condition Distribution')\n",
        "plt.xlabel('Count')\n",
        "plt.ylabel('Health Condition')\n",
        "plt.gca().spines[['top', 'right']].set_visible(False)\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "f0Jl8OB7pW-g",
        "outputId": "41cb6e8b-92dc-49f3-e8b9-4654d2b78560"
      }
    },
    {
      "source": [
        "# @title Occupation Distribution\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "data['Occupation'].value_counts().plot(kind='barh', color=sns.color_palette('Dark2'))\n",
        "plt.title('Occupation Distribution')\n",
        "plt.xlabel('Count')\n",
        "plt.ylabel('Occupation')\n",
        "plt.xticks(rotation=45)\n",
        "plt.gca().spines[['top', 'right']].set_visible(False)\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "ZcpiCnK4paTb",
        "outputId": "1f78f8ba-47cc-4d8a-81c0-c5773fb12914"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Spliting data and Target**"
      ],
      "metadata": {
        "id": "g3lMQGvJa-jh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.drop(columns=['Insurance Cost'], axis=1)\n",
        "y = data['Insurance Cost']\n"
      ],
      "metadata": {
        "id": "z-WLSlFIbVr6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x) # data after drop \"Insurance Cost\" column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VY8GmUzzbeft",
        "outputId": "f41f2fb4-7364-44d9-f7bc-6e6f41ee0893"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Age  Sex  Health Condition  Occupation\n",
            "0     56    1                 1           2\n",
            "1     46    0                 3           4\n",
            "2     32    0                 1           1\n",
            "3     60    0                 1           3\n",
            "4     25    1                 1           2\n",
            "..   ...  ...               ...         ...\n",
            "995   22    1                 0           4\n",
            "996   40    1                 1           4\n",
            "997   27    0                 2           2\n",
            "998   61    1                 0           2\n",
            "999   19    1                 3           2\n",
            "\n",
            "[1000 rows x 4 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y) # \"Insurance Cost\" column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KWK4o2o_bh6d",
        "outputId": "caa55da8-6578-4836-c756-b4ec4842b2aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0       7825131.82\n",
            "1      14875093.63\n",
            "2       5918225.80\n",
            "3       9558453.32\n",
            "4       6291935.48\n",
            "          ...     \n",
            "995    13903113.83\n",
            "996    16175649.29\n",
            "997     2253618.49\n",
            "998     8394113.02\n",
            "999     6337036.48\n",
            "Name: Insurance Cost, Length: 1000, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Spliting Training and Test data**"
      ],
      "metadata": {
        "id": "cNCHcvDzb9nn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Split data into x and y in order to train our model for predicting data.\n",
        "- Divede data into x_train, x_test, y_train, y_test.\n",
        "- Random state paramenter set to 2 to ensures reproducibility of the split."
      ],
      "metadata": {
        "id": "avX16QwGlAL4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- x_train and y_train used for training data and fitting data.\n",
        "- x_test and y_test used for testing model if it's predicting right output.\n",
        "- Training data: machine seen and learn from this dataset and Testing data:"
      ],
      "metadata": {
        "id": "fqEt1AJ_urN9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)\n",
        "# x,y: arrays, test_size = 0.2: 20% of the data go into test set(200 out of 1000 go into test dataset and 800 go into train dataset), train_size: None,"
      ],
      "metadata": {
        "id": "hami3xF6cBnL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title X_train\n",
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "qpWrIS9GrTfK",
        "outputId": "c6b82a31-53ac-4fdc-cd1b-f71d2f442483"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Age  Sex  Health Condition  Occupation\n",
              "175   45    1                 1           4\n",
              "818   60    0                 1           3\n",
              "677   33    1                 0           4\n",
              "952   53    0                 0           3\n",
              "200   56    0                 1           3\n",
              "..   ...  ...               ...         ...\n",
              "534   42    1                 2           1\n",
              "584   33    0                 3           4\n",
              "493   49    0                 3           4\n",
              "527   58    1                 2           3\n",
              "168   45    0                 1           3\n",
              "\n",
              "[800 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e58fdb71-f162-494d-98e6-6cf71f1d4e9d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Health Condition</th>\n",
              "      <th>Occupation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>818</th>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>677</th>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>952</th>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>534</th>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>584</th>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>493</th>\n",
              "      <td>49</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>527</th>\n",
              "      <td>58</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>800 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e58fdb71-f162-494d-98e6-6cf71f1d4e9d')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .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",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e58fdb71-f162-494d-98e6-6cf71f1d4e9d 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-e58fdb71-f162-494d-98e6-6cf71f1d4e9d');\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",
              "\n",
              "\n",
              "<div id=\"df-aea2cd2b-7864-48c4-903e-9c9f5487a012\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-aea2cd2b-7864-48c4-903e-9c9f5487a012')\"\n",
              "            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",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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",
              "    }\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-aea2cd2b-7864-48c4-903e-9c9f5487a012 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_82fe0315-286b-401f-b9c1-3f87f1b78fa4\">\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('x_train')\"\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_82fe0315-286b-401f-b9c1-3f87f1b78fa4 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('x_train');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x_train",
              "summary": "{\n  \"name\": \"x_train\",\n  \"rows\": 800,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 18,\n        \"max\": 64,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          26,\n          43,\n          61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Health Condition\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title X_test\n",
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "7fn9GJRorXrf",
        "outputId": "a3d9daff-2ddd-4fd2-ae6d-bba80a44ae8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Age  Sex  Health Condition  Occupation\n",
              "37    54    0                 2           4\n",
              "726   38    0                 2           3\n",
              "846   23    0                 2           3\n",
              "295   52    0                 1           4\n",
              "924   57    1                 1           4\n",
              "..   ...  ...               ...         ...\n",
              "839   38    1                 3           2\n",
              "810   34    0                 1           1\n",
              "930   23    1                 0           4\n",
              "616   55    0                 3           4\n",
              "809   27    1                 0           4\n",
              "\n",
              "[200 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8db86548-be00-4c10-87aa-752e12375e77\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Health Condition</th>\n",
              "      <th>Occupation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>54</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>726</th>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>846</th>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>295</th>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>924</th>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>839</th>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>810</th>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>930</th>\n",
              "      <td>23</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>616</th>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>809</th>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8db86548-be00-4c10-87aa-752e12375e77')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .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",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8db86548-be00-4c10-87aa-752e12375e77 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-8db86548-be00-4c10-87aa-752e12375e77');\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",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cb2e1259-2e70-4bfa-936d-c85b4c00f405')\"\n",
              "            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",
              "      --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",
              "    }\n",
              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      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-cb2e1259-2e70-4bfa-936d-c85b4c00f405 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_3419c87f-018b-4e1a-ab7c-ac0d2a333dbb\">\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('x_test')\"\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_3419c87f-018b-4e1a-ab7c-ac0d2a333dbb button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('x_test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x_test",
              "summary": "{\n  \"name\": \"x_test\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 18,\n        \"max\": 64,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          26,\n          47,\n          56\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\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\": \"Health Condition\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Y_train\n",
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "740WflxDrfeL",
        "outputId": "7052ccaa-0c17-4d1a-c16a-ce2e0354635e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "175    15950515.20\n",
              "818    10793816.72\n",
              "677    14405799.78\n",
              "952     9167273.18\n",
              "200     9447536.18\n",
              "          ...     \n",
              "534     5157644.31\n",
              "584    14271473.73\n",
              "493    16066520.33\n",
              "527    10521175.94\n",
              "168     8250521.48\n",
              "Name: Insurance Cost, Length: 800, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Y_test\n",
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x5CVOL9srldM",
        "outputId": "140de89c-5fca-44f2-87b8-024ce524c3f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37     16588238.55\n",
              "726     7904245.23\n",
              "846     6757298.68\n",
              "295    14473616.90\n",
              "924    15818611.07\n",
              "          ...     \n",
              "839     6410595.51\n",
              "810     7562323.56\n",
              "930    15368263.33\n",
              "616    16655508.16\n",
              "809    16160495.59\n",
              "Name: Insurance Cost, Length: 200, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Model Training**"
      ],
      "metadata": {
        "id": "JQpq8v0QcVYB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. Linear Regression** : used to predict value of a variable based on value of another variable\n"
      ],
      "metadata": {
        "id": "39UK9R_DckN4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lin_reg_model = LinearRegression()          # LinearRegression is a class in sklearn\n",
        "\n",
        "lin_reg_model.fit(x_train, y_train)         #"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "FXDyFbgocXol",
        "outputId": "e7353f51-342d-4759-f314-ac4a2d98240c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "a. Model Evaluation"
      ],
      "metadata": {
        "id": "rnDm1GtucxMN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Prediction with training data\n",
        "training_data_prediction = lin_reg_model.predict(x_train)"
      ],
      "metadata": {
        "id": "4juzUw0vehuC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- R square error:"
      ],
      "metadata": {
        "id": "RUX3P5Cb9ECy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title R square Error for training\n",
        "error_train = metrics.r2_score(y_train, training_data_prediction)\n",
        "print(\"R squared error: \", error_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Irt_cHnjoqw",
        "outputId": "2c99a92a-918d-4824-ba04-ea8f81166f8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared error:  0.7837074421774866\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- The result indicate that the linear regression had predict the target variable accurately for approximately 78.37% of the samples in testing sets."
      ],
      "metadata": {
        "id": "MO_nv_lBBOea"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "b. Visualize the Actual cost and Predictied cost"
      ],
      "metadata": {
        "id": "DeClJX9lj8AR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- x contains paramenters are required for calculating and predicting the insurance cost.\n",
        "- y contains the values insurance cost.\n"
      ],
      "metadata": {
        "id": "XAy6cYnpfn1X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- We predict values in y using values in x then compare actual cost (y_train) and predicted cost (x_train) by using scatter plot"
      ],
      "metadata": {
        "id": "3Zmg-mtogI1P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Visualize for trainning data\n",
        "\n",
        "plt.scatter(y_train, training_data_prediction)\n",
        "plt.xlabel(\"Actual Cost\")\n",
        "plt.ylabel(\"Predicted Cost\")\n",
        "plt.title(\"Linear Regression (trainning data)\")\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "0GfLuXyRkFtG",
        "outputId": "79de2268-59e9-4138-abc1-86c00716523b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- We find the R square error between them. If the errors are less enough, we process to testing the model since the training phase is over."
      ],
      "metadata": {
        "id": "brwgdHrZkE7t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Prediction with testing data\n",
        "test_data_prediction = lin_reg_model.predict(x_test)"
      ],
      "metadata": {
        "id": "19bu2Hg8k9EY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title R square Error for testing\n",
        "error_test = metrics.r2_score(y_test, test_data_prediction)\n",
        "print(\"R squared error: \", error_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "88RNGl2DlJuB",
        "outputId": "245d78c8-bb64-4a59-e287-a59ab81f1f49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared error:  0.7589684620618193\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Visualize for testing data\n",
        "\n",
        "plt.scatter(y_test, test_data_prediction)\n",
        "plt.xlabel(\"Actual Cost\")\n",
        "plt.ylabel(\"Predicted Cost\")\n",
        "plt.title(\"Linear Regression (testing data)\")\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "yrmlYDu8l5jx",
        "outputId": "a0fd1cc5-d403-4761-b4d4-7f0f2b3658ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- After training model, we procceeded to test the model using folowing example insurance data to predict its cost"
      ],
      "metadata": {
        "id": "RbeSC7srlvSI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_data = {\n",
        "    'Age': 30,\n",
        "    'Sex': ['Male'],\n",
        "    'Health Condition': 'Good',\n",
        "    'Occupation': ['Group 1'],\n",
        "}\n",
        "\n",
        "new_data_df = pd.DataFrame(new_data)\n",
        "\n",
        "print(new_data_df)\n",
        "\n",
        "# Encoding new data\n",
        "new_data_df.replace({'Sex': {'Male': 1, 'Female': 0}}, inplace=True)\n",
        "new_data_df.replace({'Health Condition': {'Poor': 0, 'Good': 1, 'Excellent': 2, 'Average': 3}}, inplace=True)\n",
        "new_data_df.replace({'Occupation': {'Group 1': 1, 'Group 2': 2, 'Group 3': 3, 'Group 4': 4}}, inplace=True)\n",
        "\n",
        "print(\"\")\n",
        "print(\"After replacing:\")\n",
        "print(new_data_df)\n",
        "\n",
        "predicted_cost_linear = lin_reg_model.predict(new_data_df)\n",
        "\n",
        "print(\"\")\n",
        "print(\"Predicted Insurance Cost:\", predicted_cost_linear)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "huSYackLc0tp",
        "outputId": "92ec8b32-c21d-4676-f816-7ab0ad43bb58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Age   Sex Health Condition Occupation\n",
            "0   30  Male             Good    Group 1\n",
            "\n",
            "After replacing:\n",
            "   Age  Sex  Health Condition  Occupation\n",
            "0   30    1                 1           1\n",
            "\n",
            "Predicted Insurance Cost: [4591694.14085569]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- We developed another model: Lasso Regression to compare with Linear Regression"
      ],
      "metadata": {
        "id": "NmPJ2ticnQXx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Lasso Regression"
      ],
      "metadata": {
        "id": "UTSyPtRrOl1q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Lasso Regression:  is a linear regression technique,"
      ],
      "metadata": {
        "id": "7bUZpLGqjBcL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lass_reg_model = Lasso()\n",
        "\n",
        "lass_reg_model.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "_yp9OZGAO-RS",
        "outputId": "491b360e-efc0-42ff-ad01-a00668fd7c23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Lasso()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "a. Model Evaluation"
      ],
      "metadata": {
        "id": "Eigj5VYMQYu-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Prediction on Training data with x_train\n",
        "\n",
        "training_data_prediction = lass_reg_model.predict(x_train)"
      ],
      "metadata": {
        "id": "Ah8fn8EJPf27"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title R square Error\n",
        "\n",
        "error_train = metrics.r2_score(y_train, training_data_prediction)\n",
        "print(\"R squared error: \", error_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Q6sFG22Po6V",
        "outputId": "bc57c424-4c0d-43a3-c711-868d9cc43884"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared error:  0.783707442177143\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "b. Visualized Actual cost and Predicted cost"
      ],
      "metadata": {
        "id": "QDApWw6zQvIx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Visualized for training data\n",
        "plt.scatter(y_train, training_data_prediction)\n",
        "plt.xlabel(\"Actual Price\")\n",
        "plt.ylabel(\"Predicted Price\")\n",
        "plt.title(\"Lasso Regression (training data)\")\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "RDlw5-frQD9k",
        "outputId": "3f4e90c3-aebf-474f-9b56-544e90ccc178"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Prediction on Testing data\n",
        "\n",
        "test_data_prediction = lass_reg_model.predict(x_test)"
      ],
      "metadata": {
        "id": "n8GALO22RmiY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title R square Error\n",
        "\n",
        "error_test = metrics.r2_score(y_test, test_data_prediction)\n",
        "print(\"R squared error: \", error_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2_ZCB7JRvy_",
        "outputId": "07ce6996-3073-42a1-e080-84fe53f81b5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared error:  0.7589684388407502\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Visualized testing data\n",
        "plt.scatter(y_test, test_data_prediction)\n",
        "plt.xlabel(\"Actual Price\")\n",
        "plt.ylabel(\"Predicted Price\")\n",
        "plt.title(\"Lasso Regression (testing data)\")\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "zGvkHdayRZVI",
        "outputId": "5d908f02-f00d-480e-d433-86a28225409f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_data2 = {\n",
        "    'Age': 30,\n",
        "    'Sex': ['Male'],\n",
        "    'Health Condition': 'Good',\n",
        "    'Occupation': ['Group 1'],\n",
        "}\n",
        "\n",
        "new_data2_df = pd.DataFrame(new_data2)\n",
        "\n",
        "print(new_data2_df)\n",
        "\n",
        "# Encoding new data\n",
        "new_data2_df.replace({'Sex': {'Male': 1, 'Female': 0}}, inplace=True)\n",
        "new_data2_df.replace({'Health Condition': {'Poor': 0, 'Good': 1, 'Excellent': 2, 'Average': 3}}, inplace=True)\n",
        "new_data2_df.replace({'Occupation': {'Group 1': 1, 'Group 2': 2, 'Group 3': 3, 'Group 4': 4}}, inplace=True)\n",
        "\n",
        "print(\"\")\n",
        "print(\"After replacing:\")\n",
        "print(new_data2_df)\n",
        "\n",
        "predicted_cost_lasso = lass_reg_model.predict(new_data2_df)\n",
        "\n",
        "print(\"\")\n",
        "print(\"Predicted Insurance Cost:\", predicted_cost_linear)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vKBtYZrT71tN",
        "outputId": "56e55447-15f2-4b79-f963-ffa93f1d4b3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Age   Sex Health Condition Occupation\n",
            "0   30  Male             Good    Group 1\n",
            "\n",
            "After replacing:\n",
            "   Age  Sex  Health Condition  Occupation\n",
            "0   30    1                 1           1\n",
            "\n",
            "Predicted Insurance Cost: [4591694.14085569]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. Experiment result**\n",
        "- Performance:\n",
        "\n",
        "=> Linear Regression performs better than Lasso Regression does.\n",
        "\n",
        "- Complexity:\n",
        "\n",
        "=> Linear Regression and Lasso Regression are simpler models\n",
        "\n",
        "- Interpretability:\n",
        "\n",
        "=> Linear Regression provides coefficients for each feature, making it interpretable.\n",
        "=> Lasso Regression can perform features selection by driving some coefficients to zero.\n",
        "\n",
        "**5. Conclusion**\n",
        "- In this project, we developed a machine learning model to predict insurance cost using a dataset containing various features such as age, sex, health condition and occupation. We started by exploring and preprocessing the dataset, then trained a linear regression model to predict cost. Evaluation metrics R-squared (R^2) were used to assess the model's performance. Additionally, visualizations were created to understand the relationship between actual and predicted cost and to analyze the distribution of\n",
        "categorical variables. Overall, the model showed promising results in predicting insurance cost, with opportunities for further improvement through feature engineering and model tuning\n",
        "\n"
      ],
      "metadata": {
        "id": "1fmn3YprnA8G"
      }
    }
  ]
}