From b45dd25b329880d60c824bb79ca2cab2be34a936 Mon Sep 17 00:00:00 2001
From: Ananya Gupta <145869907+ananyag309@users.noreply.github.com>
Date: Tue, 30 Jul 2024 13:47:36 +0530
Subject: [PATCH] Add files via upload

---
 .../Health_insurance_cost_prediction.ipynb    | 2350 +++++++++++++++++
 .../Images/boxplot-1.png                      |  Bin 0 -> 60850 bytes
 .../Images/boxplot-2_processed.png            |  Bin 0 -> 79377 bytes
 .../Images/correlation_matrix.png             |  Bin 0 -> 58288 bytes
 .../Images/countplot.png                      |  Bin 0 -> 36963 bytes
 .../Images/cross_validation_graph.png         |  Bin 0 -> 21190 bytes
 ...valuation_metrix_comparison_all_models.png |  Bin 0 -> 36582 bytes
 .../Images/gbm_actual_vs_predicted.png        |  Bin 0 -> 101634 bytes
 .../Images/histogram (1).png                  |  Bin 0 -> 37311 bytes
 .../Images/mlr_actual_vs_predicted.png        |  Bin 0 -> 100882 bytes
 .../Images/pie-chart-1.png                    |  Bin 0 -> 39216 bytes
 .../Images/pie-chart-2.png                    |  Bin 0 -> 37756 bytes
 .../Images/rf_actual_vs_predicted.png         |  Bin 0 -> 102834 bytes
 .../Images/scatterplot.png                    |  Bin 0 -> 179676 bytes
 .../Images/violin-plot.png                    |  Bin 0 -> 167526 bytes
 .../Images/xgboost_actual_Vs_predicted.png    |  Bin 0 -> 104452 bytes
 .../Health Insurance Price Prediction/app.py  |   57 +
 .../readme.md                                 |  135 +
 .../requirements.txt                          |    5 +
 19 files changed, 2547 insertions(+)
 create mode 100644 Finacial Domain/Health Insurance Price Prediction/Health_insurance_cost_prediction.ipynb
 create mode 100644 Finacial Domain/Health Insurance Price Prediction/Images/boxplot-1.png
 create mode 100644 Finacial Domain/Health Insurance Price Prediction/Images/boxplot-2_processed.png
 create mode 100644 Finacial Domain/Health Insurance Price Prediction/Images/correlation_matrix.png
 create mode 100644 Finacial Domain/Health Insurance Price Prediction/Images/countplot.png
 create mode 100644 Finacial Domain/Health Insurance Price Prediction/Images/cross_validation_graph.png
 create mode 100644 Finacial Domain/Health Insurance Price Prediction/Images/evaluation_metrix_comparison_all_models.png
 create mode 100644 Finacial Domain/Health Insurance Price Prediction/Images/gbm_actual_vs_predicted.png
 create mode 100644 Finacial Domain/Health Insurance Price Prediction/Images/histogram (1).png
 create mode 100644 Finacial Domain/Health Insurance Price Prediction/Images/mlr_actual_vs_predicted.png
 create mode 100644 Finacial Domain/Health Insurance Price Prediction/Images/pie-chart-1.png
 create mode 100644 Finacial Domain/Health Insurance Price Prediction/Images/pie-chart-2.png
 create mode 100644 Finacial Domain/Health Insurance Price Prediction/Images/rf_actual_vs_predicted.png
 create mode 100644 Finacial Domain/Health Insurance Price Prediction/Images/scatterplot.png
 create mode 100644 Finacial Domain/Health Insurance Price Prediction/Images/violin-plot.png
 create mode 100644 Finacial Domain/Health Insurance Price Prediction/Images/xgboost_actual_Vs_predicted.png
 create mode 100644 Finacial Domain/Health Insurance Price Prediction/app.py
 create mode 100644 Finacial Domain/Health Insurance Price Prediction/readme.md
 create mode 100644 Finacial Domain/Health Insurance Price Prediction/requirements.txt

diff --git a/Finacial Domain/Health Insurance Price Prediction/Health_insurance_cost_prediction.ipynb b/Finacial Domain/Health Insurance Price Prediction/Health_insurance_cost_prediction.ipynb
new file mode 100644
index 00000000..399af45c
--- /dev/null
+++ b/Finacial Domain/Health Insurance Price Prediction/Health_insurance_cost_prediction.ipynb	
@@ -0,0 +1,2350 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "15vW_GvjF3-W"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "import plotly.express as px\n",
+        "import plotly.graph_objects as go\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df=pd.read_csv('/content/insurance.csv')"
+      ],
+      "metadata": {
+        "id": "TY35BthJ6cdM"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head(5)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "6fQLERAn6fkm",
+        "outputId": "f64bbd4e-c55c-472f-e170-d4e6c9fcae80"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   age     sex     bmi  children smoker     region      charges\n",
+              "0   19  female  27.900         0    yes  southwest  16884.92400\n",
+              "1   18    male  33.770         1     no  southeast   1725.55230\n",
+              "2   28    male  33.000         3     no  southeast   4449.46200\n",
+              "3   33    male  22.705         0     no  northwest  21984.47061\n",
+              "4   32    male  28.880         0     no  northwest   3866.85520"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b3604aba-451b-4609-a670-7027550836c9\" 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>bmi</th>\n",
+              "      <th>children</th>\n",
+              "      <th>smoker</th>\n",
+              "      <th>region</th>\n",
+              "      <th>charges</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>19</td>\n",
+              "      <td>female</td>\n",
+              "      <td>27.900</td>\n",
+              "      <td>0</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>southwest</td>\n",
+              "      <td>16884.92400</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>18</td>\n",
+              "      <td>male</td>\n",
+              "      <td>33.770</td>\n",
+              "      <td>1</td>\n",
+              "      <td>no</td>\n",
+              "      <td>southeast</td>\n",
+              "      <td>1725.55230</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>28</td>\n",
+              "      <td>male</td>\n",
+              "      <td>33.000</td>\n",
+              "      <td>3</td>\n",
+              "      <td>no</td>\n",
+              "      <td>southeast</td>\n",
+              "      <td>4449.46200</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>33</td>\n",
+              "      <td>male</td>\n",
+              "      <td>22.705</td>\n",
+              "      <td>0</td>\n",
+              "      <td>no</td>\n",
+              "      <td>northwest</td>\n",
+              "      <td>21984.47061</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>32</td>\n",
+              "      <td>male</td>\n",
+              "      <td>28.880</td>\n",
+              "      <td>0</td>\n",
+              "      <td>no</td>\n",
+              "      <td>northwest</td>\n",
+              "      <td>3866.85520</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3604aba-451b-4609-a670-7027550836c9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
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+              "  </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-b3604aba-451b-4609-a670-7027550836c9 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-b3604aba-451b-4609-a670-7027550836c9');\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",
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+              "\n",
+              "\n",
+              "<div id=\"df-7abad04f-9729-4494-9378-e7273034e0eb\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7abad04f-9729-4494-9378-e7273034e0eb')\"\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
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+              "\n",
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+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
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+              "      --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-7abad04f-9729-4494-9378-e7273034e0eb button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1338,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 18,\n        \"max\": 64,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          21,\n          45,\n          36\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          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.098186911679014,\n        \"min\": 15.96,\n        \"max\": 53.13,\n        \"num_unique_values\": 548,\n        \"samples\": [\n          23.18,\n          26.885\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"southeast\",\n          \"northeast\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"charges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12110.011236694001,\n        \"min\": 1121.8739,\n        \"max\": 63770.42801,\n        \"num_unique_values\": 1337,\n        \"samples\": [\n          8688.85885,\n          5708.867\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yP7ST34D6jgp",
+        "outputId": "c7383f26-2315-4e8a-8015-ccd0761c6469"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 1338 entries, 0 to 1337\n",
+            "Data columns (total 7 columns):\n",
+            " #   Column    Non-Null Count  Dtype  \n",
+            "---  ------    --------------  -----  \n",
+            " 0   age       1338 non-null   int64  \n",
+            " 1   sex       1338 non-null   object \n",
+            " 2   bmi       1338 non-null   float64\n",
+            " 3   children  1338 non-null   int64  \n",
+            " 4   smoker    1338 non-null   object \n",
+            " 5   region    1338 non-null   object \n",
+            " 6   charges   1338 non-null   float64\n",
+            "dtypes: float64(2), int64(2), object(3)\n",
+            "memory usage: 73.3+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "--B9Vkih6nJi",
+        "outputId": "6be2bdd7-bc8c-4501-df35-d5f3c0daee65"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "               age          bmi     children       charges\n",
+              "count  1338.000000  1338.000000  1338.000000   1338.000000\n",
+              "mean     39.207025    30.663397     1.094918  13270.422265\n",
+              "std      14.049960     6.098187     1.205493  12110.011237\n",
+              "min      18.000000    15.960000     0.000000   1121.873900\n",
+              "25%      27.000000    26.296250     0.000000   4740.287150\n",
+              "50%      39.000000    30.400000     1.000000   9382.033000\n",
+              "75%      51.000000    34.693750     2.000000  16639.912515\n",
+              "max      64.000000    53.130000     5.000000  63770.428010"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-491d9ebe-b455-44a3-96fa-39488645c635\" 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>bmi</th>\n",
+              "      <th>children</th>\n",
+              "      <th>charges</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>1338.000000</td>\n",
+              "      <td>1338.000000</td>\n",
+              "      <td>1338.000000</td>\n",
+              "      <td>1338.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>39.207025</td>\n",
+              "      <td>30.663397</td>\n",
+              "      <td>1.094918</td>\n",
+              "      <td>13270.422265</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>14.049960</td>\n",
+              "      <td>6.098187</td>\n",
+              "      <td>1.205493</td>\n",
+              "      <td>12110.011237</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>18.000000</td>\n",
+              "      <td>15.960000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1121.873900</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>27.000000</td>\n",
+              "      <td>26.296250</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>4740.287150</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>39.000000</td>\n",
+              "      <td>30.400000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>9382.033000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>51.000000</td>\n",
+              "      <td>34.693750</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>16639.912515</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>64.000000</td>\n",
+              "      <td>53.130000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>63770.428010</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-491d9ebe-b455-44a3-96fa-39488645c635')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </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-491d9ebe-b455-44a3-96fa-39488645c635 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-491d9ebe-b455-44a3-96fa-39488645c635');\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-61f28d3b-09c8-4cf4-b42e-8100b46e847c\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-61f28d3b-09c8-4cf4-b42e-8100b46e847c')\"\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-61f28d3b-09c8-4cf4-b42e-8100b46e847c button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 460.6106090399993,\n        \"min\": 14.049960379216154,\n        \"max\": 1338.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          39.20702541106129,\n          39.0,\n          1338.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 463.29524977918294,\n        \"min\": 6.098186911679014,\n        \"max\": 1338.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          30.66339686098655,\n          30.4,\n          1338.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 472.5368318870757,\n        \"min\": 0.0,\n        \"max\": 1338.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1338.0,\n          1.0949177877429,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"charges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20381.922846226596,\n        \"min\": 1121.8739,\n        \"max\": 63770.42801,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          13270.422265141257,\n          9382.033,\n          1338.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OmYyZjjK6qNA",
+        "outputId": "22d1b337-6711-455e-e21f-cc39442f0d9e"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(1338, 7)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.isnull().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "aGwUyYCa6svq",
+        "outputId": "860b7ee3-2d80-47af-955f-888c79008a3d"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "age         0\n",
+              "sex         0\n",
+              "bmi         0\n",
+              "children    0\n",
+              "smoker      0\n",
+              "region      0\n",
+              "charges     0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.drop_duplicates(inplace=True)"
+      ],
+      "metadata": {
+        "id": "MNevaTuK6y_N"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8r9_qtHq62V5",
+        "outputId": "caec04ab-07b5-4cb5-b390-5c782e0b840b"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(1337, 7)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Unique values in each column:\")\n",
+        "for column in df.columns:\n",
+        "    unique_values = df[column].unique()\n",
+        "    print(f\"{column}: {unique_values}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qnGf88wg7IBX",
+        "outputId": "3251d833-c039-46ef-f6a5-88ae7967a436"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Unique values in each column:\n",
+            "age: [19 18 28 33 32 31 46 37 60 25 62 23 56 27 52 30 34 59 63 55 22 26 35 24\n",
+            " 41 38 36 21 48 40 58 53 43 64 20 61 44 57 29 45 54 49 47 51 42 50 39]\n",
+            "sex: ['female' 'male']\n",
+            "bmi: [27.9   33.77  33.    22.705 28.88  25.74  33.44  27.74  29.83  25.84\n",
+            " 26.22  26.29  34.4   39.82  42.13  24.6   30.78  23.845 40.3   35.3\n",
+            " 36.005 32.4   34.1   31.92  28.025 27.72  23.085 32.775 17.385 36.3\n",
+            " 35.6   26.315 28.6   28.31  36.4   20.425 32.965 20.8   36.67  39.9\n",
+            " 26.6   36.63  21.78  30.8   37.05  37.3   38.665 34.77  24.53  35.2\n",
+            " 35.625 33.63  28.    34.43  28.69  36.955 31.825 31.68  22.88  37.335\n",
+            " 27.36  33.66  24.7   25.935 22.42  28.9   39.1   36.19  23.98  24.75\n",
+            " 28.5   28.1   32.01  27.4   34.01  29.59  35.53  39.805 26.885 38.285\n",
+            " 37.62  41.23  34.8   22.895 31.16  27.2   26.98  39.49  24.795 31.3\n",
+            " 38.28  19.95  19.3   31.6   25.46  30.115 29.92  27.5   28.4   30.875\n",
+            " 27.94  35.09  29.7   35.72  32.205 28.595 49.06  27.17  23.37  37.1\n",
+            " 23.75  28.975 31.35  33.915 28.785 28.3   37.4   17.765 34.7   26.505\n",
+            " 22.04  35.9   25.555 28.05  25.175 31.9   36.    32.49  25.3   29.735\n",
+            " 38.83  30.495 37.73  37.43  24.13  37.145 39.52  24.42  27.83  36.85\n",
+            " 39.6   29.8   29.64  28.215 37.    33.155 18.905 41.47  30.3   15.96\n",
+            " 33.345 37.7   27.835 29.2   26.41  30.69  41.895 30.9   32.2   32.11\n",
+            " 31.57  26.2   30.59  32.8   18.05  39.33  32.23  24.035 36.08  22.3\n",
+            " 26.4   31.8   26.73  23.1   23.21  33.7   33.25  24.64  33.88  38.06\n",
+            " 41.91  31.635 36.195 17.8   24.51  22.22  38.39  29.07  22.135 26.8\n",
+            " 30.02  35.86  20.9   17.29  34.21  25.365 40.15  24.415 25.2   26.84\n",
+            " 24.32  42.35  19.8   32.395 30.2   29.37  34.2   27.455 27.55  20.615\n",
+            " 24.3   31.79  21.56  28.12  40.565 27.645 31.2   26.62  48.07  36.765\n",
+            " 33.4   45.54  28.82  22.99  27.7   25.41  34.39  22.61  37.51  38.\n",
+            " 33.33  34.865 33.06  35.97  31.4   25.27  40.945 34.105 36.48  33.8\n",
+            " 36.7   36.385 34.5   32.3   27.6   29.26  35.75  23.18  25.6   35.245\n",
+            " 43.89  20.79  30.5   21.7   21.89  24.985 32.015 30.4   21.09  22.23\n",
+            " 32.9   24.89  31.46  17.955 30.685 43.34  39.05  30.21  31.445 19.855\n",
+            " 31.02  38.17  20.6   47.52  20.4   38.38  24.31  23.6   21.12  30.03\n",
+            " 17.48  20.235 17.195 23.9   35.15  35.64  22.6   39.16  27.265 29.165\n",
+            " 16.815 33.1   26.9   33.11  31.73  46.75  29.45  32.68  33.5   43.01\n",
+            " 36.52  26.695 25.65  29.6   38.6   23.4   46.53  30.14  30.    38.095\n",
+            " 28.38  28.7   33.82  24.09  32.67  25.1   32.56  41.325 39.5   34.3\n",
+            " 31.065 21.47  25.08  43.4   25.7   27.93  39.2   26.03  30.25  28.93\n",
+            " 35.7   35.31  31.    44.22  26.07  25.8   39.425 40.48  38.9   47.41\n",
+            " 35.435 46.7   46.2   21.4   23.8   44.77  32.12  29.1   37.29  43.12\n",
+            " 36.86  34.295 23.465 45.43  23.65  20.7   28.27  35.91  29.    19.57\n",
+            " 31.13  21.85  40.26  33.725 29.48  32.6   37.525 23.655 37.8   19.\n",
+            " 21.3   33.535 42.46  38.95  36.1   29.3   39.7   38.19  42.4   34.96\n",
+            " 42.68  31.54  29.81  21.375 40.81  17.4   20.3   18.5   26.125 41.69\n",
+            " 24.1   36.2   40.185 39.27  34.87  44.745 29.545 23.54  40.47  40.66\n",
+            " 36.6   35.4   27.075 28.405 21.755 40.28  30.1   32.1   23.7   35.5\n",
+            " 29.15  27.    37.905 22.77  22.8   34.58  27.1   19.475 26.7   34.32\n",
+            " 24.4   41.14  22.515 41.8   26.18  42.24  26.51  35.815 41.42  36.575\n",
+            " 42.94  21.01  24.225 17.67  31.5   31.1   32.78  32.45  50.38  47.6\n",
+            " 25.4   29.9   43.7   24.86  28.8   29.5   29.04  38.94  44.    20.045\n",
+            " 40.92  35.1   29.355 32.585 32.34  39.8   24.605 33.99  28.2   25.\n",
+            " 33.2   23.2   20.1   32.5   37.18  46.09  39.93  35.8   31.255 18.335\n",
+            " 42.9   26.79  39.615 25.9   25.745 28.16  23.56  40.5   35.42  39.995\n",
+            " 34.675 20.52  23.275 36.29  32.7   19.19  20.13  23.32  45.32  34.6\n",
+            " 18.715 21.565 23.    37.07  52.58  42.655 21.66  32.    18.3   47.74\n",
+            " 22.1   19.095 31.24  29.925 20.35  25.85  42.75  18.6   23.87  45.9\n",
+            " 21.5   30.305 44.88  41.1   40.37  28.49  33.55  40.375 27.28  17.86\n",
+            " 33.3   39.14  21.945 24.97  23.94  34.485 21.8   23.3   36.96  21.28\n",
+            " 29.4   27.3   37.9   37.715 23.76  25.52  27.61  27.06  39.4   34.9\n",
+            " 22.    30.36  27.8   53.13  39.71  32.87  44.7   30.97 ]\n",
+            "children: [0 1 3 2 5 4]\n",
+            "smoker: ['yes' 'no']\n",
+            "region: ['southwest' 'southeast' 'northwest' 'northeast']\n",
+            "charges: [16884.924   1725.5523  4449.462  ...  1629.8335  2007.945  29141.3603]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **EDA**"
+      ],
+      "metadata": {
+        "id": "ffKd1BQt8WrI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Pie chart: Gender-wise distribution\n",
+        "fig1 = px.pie(df, names='sex', title='Gender-wise Distribution')\n",
+        "fig1.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "W_I9eMLt7lu4",
+        "outputId": "77efa8e0-3449-4a21-f898-f1b4c4b98ff3"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
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+              "<head><meta charset=\"utf-8\" /></head>\n",
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+    {
+      "cell_type": "code",
+      "source": [
+        "# Pie chart: Smoker distribution\n",
+        "fig2 = px.pie(df, names='smoker', title='Smoker Distribution')\n",
+        "fig2.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "ttUtbiLQ8cy5",
+        "outputId": "fde8ff0f-f8cd-4300-aab1-2bf1a74d488c"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
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+              "                            \n",
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+              "var x = new MutationObserver(function (mutations, observer) {{\n",
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+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
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+              "\n",
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+              "\n",
+              "// Listen for the clearing of the current output cell\n",
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+              "</body>\n",
+              "</html>"
+            ]
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+          "metadata": {}
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+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Histogram: Number of children\n",
+        "fig3 = px.histogram(df, x='children', title='Distribution of Number of Children')\n",
+        "fig3.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "dt6E6kmi8hOF",
+        "outputId": "02e48aab-8877-4f77-a89b-5b3f5f3342e2"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
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+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
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+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Violin plot: Sex vs. BMI\n",
+        "fig4 = px.violin(df, x='sex', y='bmi', box=True, points='all', title='Sex vs. BMI')\n",
+        "fig4.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "yl3cka6u8lhb",
+        "outputId": "b9748804-63b9-4861-a9ee-a2f8ae76b9ef"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
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+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
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+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Count plot: Region distribution\n",
+        "fig5 = px.histogram(df, x='region', title='Region-wise Distribution', category_orders=dict(region=df['region'].unique()))\n",
+        "fig5.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "vRZfhzDC8riB",
+        "outputId": "0880807e-103e-4a76-cf66-e698d01c4bbe"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
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+              "<head><meta charset=\"utf-8\" /></head>\n",
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+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Unique plot: Charges vs. BMI scatter plot with trendline\n",
+        "fig6 = px.scatter(df, x='bmi', y='charges', trendline='ols', title='Charges vs. BMI')\n",
+        "fig6.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "-pFXJ1Ut8w2U",
+        "outputId": "6ef9b45b-bde1-48e9-f11f-62876dd7a949"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
+        "# Label Encoding for categorical columns\n",
+        "label_encoders = {}\n",
+        "categorical_columns = ['sex', 'smoker', 'region']\n",
+        "\n",
+        "for column in categorical_columns:\n",
+        "    le = LabelEncoder()\n",
+        "    df[column] = le.fit_transform(df[column])\n",
+        "    label_encoders[column] = le\n"
+      ],
+      "metadata": {
+        "id": "8Ol1zufr87jx"
+      },
+      "execution_count": 19,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "JfOzRgkg9AAx",
+        "outputId": "d867807b-d14f-4220-93ca-174e69fef743"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   age  sex     bmi  children  smoker  region      charges\n",
+              "0   19    0  27.900         0       1       3  16884.92400\n",
+              "1   18    1  33.770         1       0       2   1725.55230\n",
+              "2   28    1  33.000         3       0       2   4449.46200\n",
+              "3   33    1  22.705         0       0       1  21984.47061\n",
+              "4   32    1  28.880         0       0       1   3866.85520"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6bec0081-d080-40de-be15-f3e0b04ebaaa\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
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+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
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+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>bmi</th>\n",
+              "      <th>children</th>\n",
+              "      <th>smoker</th>\n",
+              "      <th>region</th>\n",
+              "      <th>charges</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>19</td>\n",
+              "      <td>0</td>\n",
+              "      <td>27.900</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>16884.92400</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>18</td>\n",
+              "      <td>1</td>\n",
+              "      <td>33.770</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1725.55230</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>28</td>\n",
+              "      <td>1</td>\n",
+              "      <td>33.000</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>4449.46200</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>33</td>\n",
+              "      <td>1</td>\n",
+              "      <td>22.705</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>21984.47061</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>32</td>\n",
+              "      <td>1</td>\n",
+              "      <td>28.880</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3866.85520</td>\n",
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+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-6bec0081-d080-40de-be15-f3e0b04ebaaa 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-6bec0081-d080-40de-be15-f3e0b04ebaaa');\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-c5ad3c5f-230c-4764-a8f6-f96139ff55ee\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c5ad3c5f-230c-4764-a8f6-f96139ff55ee')\"\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-c5ad3c5f-230c-4764-a8f6-f96139ff55ee button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1337,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 18,\n        \"max\": 64,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          21,\n          45,\n          36\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\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.100468409615801,\n        \"min\": 15.96,\n        \"max\": 53.13,\n        \"num_unique_values\": 548,\n        \"samples\": [\n          23.18,\n          26.885\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoker\",\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\": \"region\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"charges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12110.359656344175,\n        \"min\": 1121.8739,\n        \"max\": 63770.42801,\n        \"num_unique_values\": 1337,\n        \"samples\": [\n          8688.85885,\n          5708.867\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Correlation matrix\n",
+        "plt.figure(figsize=(10, 8))\n",
+        "corr_matrix = df.corr()\n",
+        "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', linewidths=0.5)\n",
+        "plt.title('Correlation Matrix')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 699
+        },
+        "id": "eG6Pgpk_-h6L",
+        "outputId": "153105ef-9a97-4a06-8433-79f679f313d3"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x800 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Box plots for each column before removing outliers\n",
+        "plt.figure(figsize=(15, 10))\n",
+        "\n",
+        "for i, column in enumerate(df.columns, 1):\n",
+        "    plt.subplot(3, 3, i)\n",
+        "    sns.boxplot(y=df[column])\n",
+        "    plt.title(f'Box Plot of {column}')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 691
+        },
+        "id": "EFPGQRKq-ob1",
+        "outputId": "d49dd223-b33d-4a6d-f87e-88112e8e2c5d"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x1000 with 7 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Function to remove outliers using IQR\n",
+        "def remove_outliers(df, column):\n",
+        "    Q1 = df[column].quantile(0.25)\n",
+        "    Q3 = df[column].quantile(0.75)\n",
+        "    IQR = Q3 - Q1\n",
+        "    lower_bound = Q1 - 1.5 * IQR\n",
+        "    upper_bound = Q3 + 1.5 * IQR\n",
+        "    df = df[(df[column] >= lower_bound) & (df[column] <= upper_bound)]\n",
+        "    return df\n",
+        "\n",
+        "# Removing outliers for each column\n",
+        "df2 = df.copy()\n",
+        "for column in df.columns:\n",
+        "    if column != 'charges':  # Assuming 'charges' is our target and we won't remove outliers from the target variable\n",
+        "        df2 = remove_outliers(df2, column)\n",
+        "\n",
+        "# Box plots for each column after removing outliers\n",
+        "plt.figure(figsize=(15, 10))\n",
+        "\n",
+        "for i, column in enumerate(df2.columns, 1):\n",
+        "    plt.subplot(3, 3, i)\n",
+        "    sns.boxplot(y=df2[column])\n",
+        "    plt.title(f'Box Plot of {column} (Outliers Removed)')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 691
+        },
+        "id": "HrSxqKBg-6gH",
+        "outputId": "685cdf53-5870-43b4-f135-4a5b8d1b4478"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x1000 with 7 Axes>"
+            ],
+            "image/png": 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ePGh/X1xubDZnY6K///779veKv7+/evTooUOHDjn0ad68uWrXrq3Y2Fg1bdpURYsWtQ9b89NPPyk8PFwBAQHy9vZWcHBwnk8Uvby81KBBA505c8Z+UJmXOHfu3KlmzZqpaNGiqlq1qj7++GNJ0saNG9WwYUN5e3urevXqWrt2bZYYtm/frnbt2snX11fFixdXq1attGXLFvv0n376SRaLRQsWLMgy76pVq2SxWByG6Tl8+LAeeeQRBQYGymq1qlatWnr33XezzPv333+rU6dOKlasmEqXLq0RI0YoJSUl2/3Upk0bJScna82aNZfZm8CNhTzuKL/z+JkzZzR8+HCHnz+3adNG27Ztc1jmtf4MdebUqVO68847Vb58ee3Zs0fSheOJ0aNHq2rVqrJarQoKCtIzzzyT5bPRYrFoyJAh+uCDD1SrVi1ZrVatXLnS6bo+//xzeXp6qmnTpg7tEydO1NmzZzVr1iyHArokBQQEaObMmUpOTtbrr79ub3c2LuqlOddisSg5OVkLFiywvwcu93pld5yUH/tj8eLFCg0NlY+Pj3x9fVWnTh1NnTrVaRyXU6NGDQUEBGjfvn1XFefSpUtVs2ZNeXt7KywsTLt27ZIkzZw5U1WrVpWXl5eaN2+e7d/Mlf5O3njjDVksFh08eDDLvFFRUfL09NSpU6fsbT/88IPuuece2Ww2FS1aVM2aNct2iIBNmzapQYMG8vLyUpUqVTRz5kyn+6lNmzbatGmTTp486bQPcDMgjzvK7zyemVd+/fVX9erVSyVKlFDjxo3t03NyriRJMTExqly5sry9vXXnnXfqf//7X47vV3alfXVxnHv37rVfuW+z2dS/f3+dPXv2svtMkpYvX67//vsvy3n1zJkzFR8fr4kTJzoU0CXJ29vbnl8vvqLY2fnvpefblSpV0i+//KKNGzfaX9/L1Sqyy/0ZGRmaMmWKatWqJS8vLwUGBurxxx93yDGZ67rvvvu0atUq1a9fX97e3vYcsmbNGjVu3Fh+fn4qXry4qlevnu1QsTlRqlQphYSEZMnRuY1zw4YN9jjr1Kljr0V8+umnqlOnjry8vBQaGqrt27dnieFK75ePP/5YFotFGzduzDLvzJkzZbFYtHv3bntbTuo60oVh+Vq2bClvb2+VL19er7zyitNfDrRp00YHDx7Ujh07nO7Lm45BoTJv3jwjyaxdu9YcO3bMHD161Ozevds8/vjjxs3NzaxevdreNyMjw7Rs2dJYLBbz6KOPmnfeecd06NDBSDLDhw+399uyZYtxd3c3I0aMsLf16NHDeHt7mz179lw2nvXr1xtJZunSpQ7tX3zxhZFknnvuOWOMMfv37zeSzLx58+x91qxZY4oUKWKqVatmXn/9dTN27FgTEBBgSpQoYfbv32+MMebnn382PXv2NJLM5MmTzcKFC83ChQvNv//+e8V91KBBAzN58mTz3HPPGW9vb1OpUiVz6tQpY4wxq1evNgMHDjSSzLhx48zChQvN999/73SZhw8fNuXKlTNFixY1w4cPNzNmzDAvvfSSqVGjhn2Zmeu9/fbbTcuWLc3bb79tRo4cadzd3U23bt0cltesWTPTrFkz+/Ps9s+qVauMm5ubqV27tpk0aZJ54YUXjM1mM7Vq1TIVK1bMMm/NmjVN5cqVzYQJE8zkyZPNwYMHze7du43NZjM1a9Y0r732mnnnnXdM06ZNjcViMZ9++mmWfZaT2LPzyiuvGIvFYhITEx3ae/XqZSSZAwcOOJ23b9++RpL5888/HWLJfA9kynyvrV+/3hhz4TWsV6+eCQgIsL8vPvvsM6f7c/To0ebSj8zMuLt3726mTZtmfw9e/F4x5sLrVaZMGVOqVCkzdOhQM3PmTPP555+bhIQEU6JECVOtWjUzceJEM3v2bPPCCy+YGjVqXHGfNWvWzNSqVStLe/369Y3FYjFnz57NU5zlypUzQUFB5umnnzZvv/22qVmzpnF3dzeLFy82ZcqUMWPGjDFTpkwxt9xyi7HZbCYpKck+/+7du02xYsVM2bJlzcsvv2wmTJhggoODjdVqNVu2bLH3q1y5smnfvn2W2Pv3729KlChhUlNTjTHGxMfHm/Lly5ugoCAzbtw4M336dHP//ffb/54znT171lSrVs14eXmZZ555xkyZMsWEhoaaunXrOrzmmdLS0oy3t7cZOXLkFfczUNCQx12Tx3v16mU8PT1NZGSkmTNnjnnttddMhw4dzPvvv2/vc70+QzO3b+vWrcYYY44dO2bq1atnKlSoYPbu3WuMMSY9Pd20bdvWftwxc+ZMM2TIEFOkSBHTsWNHh22TZGrUqGFKlSplxo4da2JiYsz27dud7ovWrVubO+64I0t7uXLlTKVKlZzOZ4wxlSpVMuXLl7c/79u3r8MxSaZLc+7ChQuN1Wo1TZo0sb8HMl+v7PL+pcdJ+bE/Vq9ebSSZVq1amZiYGBMTE2OGDBliHnzwwctuc+YyIyIiHNpOnz5t3N3dTcOGDfMcZ926dU1QUJCZMGGCmTBhgrHZbKZChQrmnXfeMTVr1jRvvvmmefHFF42np6dp0aKFw/w5+Ts5ePCgsVgs5vXXX8+yTZUrVzb33nuv/fm6deuMp6enCQsLM2+++aaZPHmyqVu3rvH09DQ//PCDvd/OnTuNt7e3qVChgomOjjYvv/yyCQwMtOfsS23atMlIMsuWLbvifgZuBOTx/8fencfHdP1/HH8nJBNLEoJsRKyV2ImlscVWu1LUUorU0halotqmi6W0qfraqta2qKpvlap+u9iXqla/KqpKS1FbkcRSiTVIzu8P38zPSCYSEpPwej4e82DOPefO597cmTP3c8+c65h+PKVfqVixounQoYOZOXOmmTFjhjEm4+dKM2fONJJMw4YNzbvvvmsiIiKMl5eXKVu27G3PzTOyr26Os0aNGqZTp05m5syZpn///kaSefHFF+1uX4r+/fubIkWKpCqvV6+ecXNzM1euXLHbNiwszLi4uFjPI9M6/zUmdb/7xRdfmBIlSpigoCDr3zflOL71HNyYtPv+/v37m7x585oBAwaY2bNnm5deeskUKFDA1K5d23puaIwxgYGBply5cqZw4cLm5ZdfNrNnzzYbN240u3fvNq6urqZWrVpm2rRpZvbs2eaFF14wjRo1uu0+CwwMtOnPjLlxvujr62t8fHzuOM4KFSoYPz8/M2bMGDNlyhRTvHhxU7BgQbNo0SJTsmRJm767XLlyJikpydo+I8fLpUuXTMGCBc2gQYNSbVOTJk1scgUZzeucPHnSFCtWzBQuXNiMGTPGTJw40ZQvX97aR9+aY/n777+NJDN9+vTb7uf7BUn0B0zKB96tD4vFYhYsWGBTd8WKFUaSGT9+vE15ly5djJOTk/VEzRhjIiMjjbOzs9m8ebNZunSpkWSmTp1623hSPlTnzZtnTp06ZU6cOGG++eYbU6pUKePk5GQ9QUyrI6pevbrx9vY2Z86csZb9+uuvxtnZ2fTu3dtaNnHixDTf8Gm5evWq8fb2NpUrVzaXL1+2ln/99ddGkhk1apS17NaT2PT07t3bODs7p1k3OTnZZn3Nmze3lhljzPDhw02ePHnMuXPnrGUZSaJXr17d+Pn52bRLOQFMK4nu4eFh4uLibGJr1qyZqVKlik1nm5ycbOrVq2fKly+fal9kJPa09OrVK83Ovnr16sbT0zPdtpMnTzaSzH/+8x+bWG6XRDfGmLZt26Z58p6RJPrhw4dNnjx5zJtvvmnT9rfffjN58+a1KQ8LCzOSzOzZs23qfvHFFxk+hm4VFhZmgoKCzKlTp8ypU6fM3r17zciRI40kmy8BdxLn4sWLrWV79+41koyzs7NNEmf16tWp9lHHjh2Nq6urOXjwoLXsxIkTxt3d3eYLTGRkpHFxcTFnz561liUmJppChQqZp556ylrWr18/4+fnZ06fPm0Te/fu3Y2np6f1C97UqVONJPPZZ59Z61y8eNGUK1cuzSS6McY89NBDpnXr1ql3LJDD0Y+nL7v6cU9Pz1RJ0Fvdq8/Qm+M+efKkqVSpkilTpozNBeePP/7YODs7m++//94mxtmzZxtJ5ocffrCWpcS3Z8+e2+4HY4wpUaKE6dy5s03ZuXPnjKRUid5bpVwITbl4kNEkujHGFChQwPTp0ydV3Ywk0bNifwwbNsx4eHiY69evp7uNaZFk+vXrZ02Ybd++3bRq1cpIMhMnTrzjOC0Wi812z5kzx0gyvr6+NhdoIiMjbfZRZt4noaGhJiQkxCaebdu2GUlm4cKFxpgb3w3Lly9vWrZsafM98NKlS6Z06dLmkUcesZZ17NjRuLm5mSNHjljLfv/9d5MnT540kzUnTpwwksyECRPS3rlALkM/nr7s6sdT+pUePXrYlGf0XCkxMdEUKVLE1K5d21y7ds1ab8GCBUZShs7NM7KvUuK8+ZzIGGMee+yxNM+Xb9WgQYNUn9nGGFOoUCFTrVq1dNsOHTrUSDK7du2yieVWafW7lSpVstkHKTKSRP/++++NJPPJJ5/YtF21alWq8sDAQCPJrFq1yqbulClTjCRz6tSpdLcxLYGBgaZFixbW8+rffvvNPPnkk6kugN9JnDdf2En57pcvXz6bPjCl7755H2X0eOnRo4fx9va2+W5y8uRJ4+zsbN544w1rWUbzOs8//7yRZHPxOy4uznh6etp9D7u6uppnn3029Y69TzGdywNqxowZWrt2rdauXatFixapSZMm6t+/v5YvX26t8+233ypPnjwaOnSoTdsRI0bIGGNz9/AxY8aoUqVK6tOnjwYNGqSwsLBU7dLz1FNPqVixYvL391fbtm2tP9m9eZ64m508eVI7d+5U37595eXlZS2vWrWqHnnkEX377bcZfu2bbd++XXFxcRo0aJDNPGZt27ZVUFCQvvnmm0yvMzk5WStWrFD79u3T3J5bfyI1cOBAm7KGDRsqKSkpzZ/S2pOyf/r06WNz069HHnlEFStWTLNN586dbX5+ffbsWW3YsEFdu3bV+fPndfr0aZ0+fVpnzpxRy5YttX//fpuf1N1N7GfOnFHhwoVTlZ8/f17u7u7ptk1ZnpCQkG69rLZ8+XIlJyera9eu1n1z+vRp+fr6qnz58tq4caNNfYvFovDwcJuylPn7vv76a127di3TMezdu1fFihWz/txs4sSJevTRR21+OpjZOAsWLKju3btbn1eoUEGFChVScHCwzZQ6Kf//66+/JElJSUlas2aNOnbsaDPXrZ+fn5544glt2bLF+jfq1q2brl27ZvN5s2bNGp07d846t70xRp9//rnat28vY4xN7C1btlR8fLx1GoVvv/1Wfn5+1nkcJSl//vwaOHCg3X1XuHBhnT59OmM7GsiB6MfTlh39uHTj8/q///2vTpw4kW69e/EZmuLvv/9WWFiYrl27ps2bNyswMNC6bOnSpQoODlZQUJDN52fKz/Nv/ewPCwuz+/3gVmn12efPn5ekHNtnZ8X+KFSo0F1NBfbhhx+qWLFi8vb2Vq1atbR+/Xq9+OKLioiIuOM4mzVrZvOT+JTjqnPnzjZ/i1uPt8y8T7p166bo6Gibn7QvWbJEFotFHTp0kCTt3LlT+/fv1xNPPKEzZ85Y47548aKaNWumzZs3Kzk5WUlJSVq9erU6duyokiVLWtcXHBxsd174lGONPhv3G/rxtGVXP57i1ntwZfRcafv27Tpz5owGDBhgM+VOz5490zyPvdmd7Ktb42zYsKHOnDlz2/4zN55XL126VJ6ennrkkUds/gYhISEqWLBgqr6vdOnSqfqMlPPqL7/88o5uWrpmzRrreXWVKlX08ccfKzw8XBMnTrzjOCtWrKjQ0FDr85S+uGnTpjZ94K19dGaOl27duikuLs46TYx0Y5qX5ORk63l1ZvI63377rR5++GHVqVPHur5ixYqpZ8+edvfdg3ZezY1FH1B16tSx6RB79OihGjVqaMiQIWrXrp1cXV115MgR+fv7p/qwDQ4OliSbxKirq6vmzZtnnd9w/vz5mbqxwKhRo9SwYUPlyZNHRYsWVXBwcLo3cUx57QoVKqRaFhwcrNWrV9/RjULSW29QUJC2bNmSqfVJ0qlTp5SQkKDKlStnqP7NH6jS/5883DrPVnpStqN8+fKpllWoUMFmDtcUpUuXtnl+4MABGWP0+uuv6/XXX0/zdeLi4lS8ePEsid38b16/m7m7u9/2AzmjJ+5Zbf/+/TLGpLmPpdQ3TClevHiqG52FhYWpc+fOGjt2rKZMmaLGjRurY8eOeuKJJ2SxWG4bQ6lSpax3Nz948KDefPNNnTp1yuYLZ2bjLFGiRKr3rqenpwICAlKVSf//tz116pQuXbpk9z2ZnJysY8eOqVKlSqpWrZqCgoK0ZMkS9evXT9KNE/KiRYtaEwWnTp3SuXPnNHfuXM2dOzfN2FPmfT9y5IjKlSuXKu60YklhjHlgbn6C+xP9eObXe6f9uCS988476tOnjwICAhQSEqI2bdqod+/eqW6QeS8+Q1M8+eSTyps3r/744w/5+vratNm/f7/++OOPVHOTp7j1vhm3fge4nVv77JRjLKVPtseRffbd7o9Bgwbps88+U+vWrVW8eHG1aNFCXbt2VatWrTIUQ4cOHTRkyBBdvXpVP//8s9566y1dunRJzs7/P6Yps3He+r0r5bi63fGWmffJ448/roiICC1ZskSvvPKKjDFaunSpde7+lLglWecnTkt8fLwSExN1+fJlu99P00okpRxr9Nm439CPZ369d9OPp7j18z2j50opcZUrV85med68edO9r9bNbTOzr9I7r0757LXH3nl1Tu6j4+Pj5e3tnebyjPTR3bp10wcffKD+/fvr5ZdfVrNmzdSpUyd16dLFpp+1p27duho/frySkpK0e/dujR8/Xv/884/N+Xtm48yOPvrW4yXlPiRLlixRs2bNJN04r65evboeeughSZnL6xw5ciTVvejsxZLiQTuvJokOSTfurNukSRNNmzZN+/fvtzlJy6jVq1dLkq5cuaL9+/dn6oSsSpUqqW5+8aDKkydPmuVpdYZZ6dY7WqdcwX3hhRfsjg669UvEncZepEiRNBPtwcHB2rlzp44ePZqqE0qxa9cuSbKOGLP3AX4nN9RJT3JyspycnLRy5co0t7tgwYI2z9O6Y7iTk5OWLVumn376SV999ZVWr16tp556SpMmTdJPP/2Uah23KlCggM37pn79+qpZs6ZeeeUVvfvuu3cUp72/YVYfl926ddObb76p06dPy93dXf/5z3/Uo0cP65f1lOOvV69edk/Kq1atekevLd34kmLvyzKQG9GPZ6+uXbuqYcOG+uKLL7RmzRpNnDhREyZM0PLly9W6dWtrvXv1GSpJnTp10sKFCzVt2jRFRUXZLEtOTlaVKlU0efLkNNveegKXVh9lT1p9tqenp/z8/Kx9sj27du1S8eLFrQmAe9ln3+3+8Pb21s6dO7V69WqtXLlSK1eu1Pz589W7d+80b5Z9qxIlSljfI23atFHRokU1ZMgQNWnSRJ06dbqjOO/F8ebv76+GDRvqs88+0yuvvKKffvpJR48e1YQJE6x1UvrsiRMnqnr16mmup2DBgnZv+J2elGOtaNGimW4L5Cb04/dGWue8mTlXuley47z6l19+UWJiot3BWrt27ZKLi4v1HOle9tHe3t765JNP0lx+64XltProfPnyafPmzdq4caO++eYbrVq1SkuWLFHTpk21Zs0au/szRdGiRa3Hf8uWLRUUFKR27dpp2rRp1l+MZTbOe9FHWywWdezYUV988YVmzpyp2NhY/fDDD3rrrbesde4kr5MZ586de6D6aJLosLp+/bok6cKFC5KkwMBArVu3LtVPf/bu3WtdnmLXrl164403FB4erp07d6p///767bffbKYSyUopr71v375Uy/bu3auiRYtar+Rm5qrYzetNGRGbYt++fTbbnFHFihWTh4eHzZ2Rs1tKnCkjg26W1j5LS8roOhcXl2z/QhUUFKRPPvlE8fHxNsdMu3bt9O9//1sLFy7Ua6+9lqpdQkKCvvzySwUFBVk/+FOu0p87d86mblpTytzNFdOyZcvKGKPSpUtbr/LeqYcfflgPP/yw3nzzTS1evFg9e/bUp59+qv79+2dqPVWrVlWvXr00Z84cvfDCCypZsmSWxpmeYsWKKX/+/Hbfk87OzjYn/926ddPYsWP1+eefy8fHRwkJCTZTIBQrVkzu7u5KSkq67fEXGBio3bt3p7oKbu9Yv379uo4dO6ZHH300s5sJ5Gj049nTj6fw8/PToEGDNGjQIMXFxalmzZp68803bZLodyqzn6GS9Nxzz6lcuXIaNWqUPD099fLLL1uXlS1bVr/++quaNWuW5aODgoKCdOjQoVTl7dq10/vvv68tW7aoQYMGqZZ///33Onz4sJ5++mlrWeHChVP111L29NlZsT9cXV3Vvn17tW/fXsnJyRo0aJDmzJmj119/PdMnoE8//bSmTJmi1157TY899picnJyy9e92s8y+T7p166ZBgwZp3759WrJkifLnz6/27dtbl5ctW1aS5OHhkW6fXaxYMeXLly9T309TjrWUkbfA/Yx+PHv78bRk9Fwp5XUPHDigJk2aWMuvX7+uw4cPpzu4JzP76m4FBQXp888/T1Xerl07bd26VUuXLlWvXr1SLT98+LC+//57NW/e3Jqkvvm8OmW6FCl7+uh169apfv36mbqofytnZ2c1a9ZMzZo10+TJk/XWW2/p1Vdf1caNGzOdz2jbtq3CwsL01ltv6emnn1aBAgWyLM7byezx0q1bN3300Udav369/vjjDxljrFO5SJnL6wQGBmaqjz5+/LiuXr36QPXRzIkOSdK1a9e0Zs0aubq6Wt8Abdq0UVJSkt577z2bulOmTJGTk5P1pPHatWvq27ev/P39NW3aNC1YsECxsbEaPnx4tsXr5+en6tWr66OPPrI5+dq9e7fWrFmjNm3aWMtSPmDSOkm7Va1ateTt7a3Zs2fbjJZZuXKl/vjjD7Vt2zbTsTo7O6tjx4766quvtH379lTLs2OE+c37Jz4+3lq+du1a/f777xlah7e3txo3bqw5c+bo5MmTqZafOnUqy+INDQ2VMUbR0dE25V26dFHFihX19ttvp9p3ycnJevbZZ/XPP/9o9OjR1vKUE7nNmzdby5KSktKcEqRAgQI2+yczOnXqpDx58mjs2LGp/obGGJ05c+a26/jnn39StU0ZwXUno7Uk6cUXX9S1a9eso9iyIs6MyJMnj1q0aKEvv/xShw8ftpbHxsZq8eLFatCggc1PD4ODg1WlShUtWbJES5YskZ+fnxo1amSzvs6dO+vzzz9P8wLUzcdfmzZtdOLECS1btsxadunSJbvTwPz++++6cuWK6tWrdzebDOQo9OM3ZEc/npSUlKqv8Pb2lr+//x1/Vt8qs5+hKV5//XW98MILioyM1KxZs6zlXbt21fHjx/X++++nanP58mVdvHjxjmMNDQ3V7t27U237yJEjlS9fPj399NOp+pazZ8/qmWeeUf78+TVy5EhredmyZRUfH28zgv3kyZP64osvUr1ugQIFMnQMpCUr9set2+Ts7GxNmtzJcZA3b16NGDFCf/zxh7788sssizMjMvs+6dy5s/LkyaN///vfWrp0qdq1a2dzAh8SEqKyZcvqX//6lzX5d7OUPjtPnjxq2bKlVqxYoaNHj1qX//HHH9YRtLeKjo6Wk5OTzbyywP2IfvyG7OjH05PRc6VatWqpSJEiev/9960XOyTpk08+ue3UpZnZV3crNDRU//zzj3V+7RRPP/20vL29NXLkyFTLrly5ovDwcBljNGrUKGt5WufVKXPl3+pu++ikpCSNGzcu1bLr169naL1nz55NVXa359UvvfSSzpw5Y+2TsyLOjMjs8dK8eXN5eXlZz6vr1Klj8yuUzOR12rRpo59++knbtm2zWW5v9H1K/uZBOq9mJPoDauXKldYr2HFxcVq8eLH279+vl19+2XqS1r59ezV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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Multiple_Linear_Regression**"
+      ],
+      "metadata": {
+        "id": "06Fh5X1S_Ndk"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_percentage_error\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n",
+        "# Splitting the data into features and target\n",
+        "X = df2.drop('charges', axis=1)\n",
+        "y = df2['charges']\n",
+        "\n",
+        "# Train-test split\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "\n",
+        "# Initializing and training the Linear Regression model\n",
+        "model_lr = LinearRegression()\n",
+        "model_lr.fit(X_train, y_train)\n",
+        "\n",
+        "# Making predictions\n",
+        "y_pred = model_lr.predict(X_test)\n",
+        "\n",
+        "# Calculating performance metrics\n",
+        "accuracy_lr = r2_score(y_test, y_pred)\n",
+        "mse_lr = mean_squared_error(y_test, y_pred)\n",
+        "rmse_lr = np.sqrt(mse_lr)\n",
+        "mape_lr = mean_absolute_percentage_error(y_test, y_pred)\n",
+        "\n",
+        "# Printing the performance metrics\n",
+        "print(f\"R^2 Score (Linear Regression): {accuracy_lr}\")\n",
+        "print(f\"Mean Squared Error (MSE) (Linear Regression): {mse_lr}\")\n",
+        "print(f\"Root Mean Squared Error (RMSE) (Linear Regression): {rmse_lr}\")\n",
+        "print(f\"Mean Absolute Percentage Error (MAPE) (Linear Regression): {mape_lr}\")\n",
+        "\n",
+        "# Plotting Actual vs Predicted values for Linear Regression\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.plot(y_test.values, label='Actual Values', color='b')\n",
+        "plt.plot(y_pred, label='Predicted Values', color='r')\n",
+        "plt.title('Actual vs Predicted Insurance Charges (Linear Regression)')\n",
+        "plt.xlabel('Samples')\n",
+        "plt.ylabel('Charges')\n",
+        "plt.legend()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 633
+        },
+        "id": "pSezOB1T_Rek",
+        "outputId": "f4afca07-f512-44dc-b226-1632bce508bf"
+      },
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "R^2 Score (Linear Regression): 0.3762784748193787\n",
+            "Mean Squared Error (MSE) (Linear Regression): 24552604.643620126\n",
+            "Root Mean Squared Error (RMSE) (Linear Regression): 4955.058490433804\n",
+            "Mean Absolute Percentage Error (MAPE) (Linear Regression): 0.3003936951976371\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Random Forest**"
+      ],
+      "metadata": {
+        "id": "gPOAMs4-Ae3f"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_percentage_error\n",
+        "\n",
+        "# Initializing and training the Random Forest Regressor\n",
+        "model_rf = RandomForestRegressor(n_estimators=100, random_state=42)\n",
+        "model_rf.fit(X_train, y_train)\n",
+        "\n",
+        "# Making predictions\n",
+        "y_pred = model_rf.predict(X_test)\n",
+        "\n",
+        "# Calculating performance metrics\n",
+        "accuracy_rf = r2_score(y_test, y_pred)\n",
+        "mse_rf = mean_squared_error(y_test, y_pred)\n",
+        "rmse_rf = np.sqrt(mse_rf)\n",
+        "mape_rf = mean_absolute_percentage_error(y_test, y_pred)\n",
+        "\n",
+        "# Printing the performance metrics\n",
+        "print(f\"R^2 Score: {accuracy_rf}\")\n",
+        "print(f\"Mean Squared Error (MSE): {mse_rf}\")\n",
+        "print(f\"Root Mean Squared Error (RMSE): {rmse_rf}\")\n",
+        "print(f\"Mean Absolute Percentage Error (MAPE): {mape_rf}\")\n",
+        "\n",
+        "# Plotting Actual vs Predicted values\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.plot(y_test.values, label='Actual Values', color='b')\n",
+        "plt.plot(y_pred, label='Predicted Values', color='r')\n",
+        "plt.title('Actual vs Predicted Insurance Charges')\n",
+        "plt.xlabel('Samples')\n",
+        "plt.ylabel('Charges')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 633
+        },
+        "id": "iaa0H6pfAQhK",
+        "outputId": "78ec4f60-e691-40fe-8ab1-2e27bdb7f03e"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "R^2 Score: 0.20990321004865142\n",
+            "Mean Squared Error (MSE): 31101915.40728239\n",
+            "Root Mean Squared Error (RMSE): 5576.909126683202\n",
+            "Mean Absolute Percentage Error (MAPE): 0.437794492525885\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Gradient Boosting Machine (GBM)**"
+      ],
+      "metadata": {
+        "id": "TfgxUgWuA-h9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.ensemble import GradientBoostingRegressor\n",
+        "\n",
+        "# Initialize GBM\n",
+        "model_gbm = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, random_state=42)\n",
+        "\n",
+        "# Train the model\n",
+        "model_gbm.fit(X_train, y_train)\n",
+        "\n",
+        "# Making predictions\n",
+        "y_pred = model_gbm.predict(X_test)\n",
+        "\n",
+        "# Calculating performance metrics\n",
+        "accuracy_gbm = r2_score(y_test, y_pred)\n",
+        "mse_gbm = mean_squared_error(y_test, y_pred)\n",
+        "rmse_gbm = np.sqrt(mse_gbm)\n",
+        "mape_gbm = mean_absolute_percentage_error(y_test, y_pred)\n",
+        "\n",
+        "# Printing the performance metrics\n",
+        "print(f\"R^2 Score (GBM): {accuracy_gbm}\")\n",
+        "print(f\"Mean Squared Error (MSE) (GBM): {mse_gbm}\")\n",
+        "print(f\"Root Mean Squared Error (RMSE) (GBM): {rmse_gbm}\")\n",
+        "print(f\"Mean Absolute Percentage Error (MAPE) (GBM): {mape_gbm}\")\n",
+        "\n",
+        "# Plotting Actual vs Predicted values for GBM\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.plot(y_test.values, label='Actual Values', color='b')\n",
+        "plt.plot(y_pred, label='Predicted Values', color='r')\n",
+        "plt.title('Actual vs Predicted Insurance Charges (GBM)')\n",
+        "plt.xlabel('Samples')\n",
+        "plt.ylabel('Charges')\n",
+        "plt.legend()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 633
+        },
+        "id": "Zl0vXag7A3Iq",
+        "outputId": "836bf257-0f0e-4a4c-c709-3b7eb8fd4a8d"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "R^2 Score (GBM): 0.31373608112259155\n",
+            "Mean Squared Error (MSE) (GBM): 27014566.599251162\n",
+            "Root Mean Squared Error (RMSE) (GBM): 5197.5539053723305\n",
+            "Mean Absolute Percentage Error (MAPE) (GBM): 0.41766764663286315\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **XGBOOST**"
+      ],
+      "metadata": {
+        "id": "Ba_9p6_YBT6z"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from xgboost import XGBRegressor\n",
+        "\n",
+        "# Initialize XGBRegressor\n",
+        "model_xgb = XGBRegressor(n_estimators=100, learning_rate=0.1, random_state=42)\n",
+        "\n",
+        "# Train the model\n",
+        "model_xgb.fit(X_train, y_train)\n",
+        "\n",
+        "# Making predictions\n",
+        "y_pred = model_xgb.predict(X_test)\n",
+        "\n",
+        "# Calculating performance metrics\n",
+        "accuracy_xgb = r2_score(y_test, y_pred)\n",
+        "mse_xgb = mean_squared_error(y_test, y_pred)\n",
+        "rmse_xgb = np.sqrt(mse_xgb)\n",
+        "mape_xgb = mean_absolute_percentage_error(y_test, y_pred)\n",
+        "\n",
+        "# Printing the performance metrics\n",
+        "print(f\"R^2 Score (XGBoost): {accuracy_xgb}\")\n",
+        "print(f\"Mean Squared Error (MSE) (XGBoost): {mse_xgb}\")\n",
+        "print(f\"Root Mean Squared Error (RMSE) (XGBoost): {rmse_xgb}\")\n",
+        "print(f\"Mean Absolute Percentage Error (MAPE) (XGBoost): {mape_xgb}\")\n",
+        "\n",
+        "# Plotting Actual vs Predicted values for XGBoost\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.plot(y_test.values, label='Actual Values', color='b')\n",
+        "plt.plot(y_pred, label='Predicted Values', color='r')\n",
+        "plt.title('Actual vs Predicted Insurance Charges (XGBoost)')\n",
+        "plt.xlabel('Samples')\n",
+        "plt.ylabel('Charges')\n",
+        "plt.legend()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 633
+        },
+        "id": "LvZr2alBBE0Y",
+        "outputId": "7b812ea5-bb9b-4810-cc3e-c280cf7b089d"
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "R^2 Score (XGBoost): 0.19354456457277525\n",
+            "Mean Squared Error (MSE) (XGBoost): 31745868.41941873\n",
+            "Root Mean Squared Error (RMSE) (XGBoost): 5634.347204372369\n",
+            "Mean Absolute Percentage Error (MAPE) (XGBoost): 0.4497280705723597\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example values for metrics\n",
+        "metrics = {\n",
+        "    'Model': ['MLR', 'RF', 'XGBoost', 'GBM'],\n",
+        "    'R^2 Score': [accuracy_lr, accuracy_rf, accuracy_xgb, accuracy_gbm],\n",
+        "    'MSE': [mse_lr, mse_rf, mse_xgb, mse_gbm],\n",
+        "    'RMSE': [rmse_lr, rmse_rf, rmse_xgb, rmse_gbm],\n",
+        "    'MAPE': [mape_lr, mape_rf, mape_xgb, mape_gbm]\n",
+        "}\n",
+        "\n",
+        "# Create a DataFrame\n",
+        "df_metrics = pd.DataFrame(metrics)\n",
+        "\n",
+        "# Plotting comparison\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "\n",
+        "# Plot R^2 Score\n",
+        "plt.subplot(221)\n",
+        "plt.bar(df_metrics['Model'], df_metrics['R^2 Score'], color='b')\n",
+        "plt.title('R^2 Score Comparison')\n",
+        "plt.ylim(0, 1)\n",
+        "\n",
+        "# Plot MSE\n",
+        "plt.subplot(222)\n",
+        "plt.bar(df_metrics['Model'], df_metrics['MSE'], color='g')\n",
+        "plt.title('MSE Comparison')\n",
+        "\n",
+        "# Plot RMSE\n",
+        "plt.subplot(223)\n",
+        "plt.bar(df_metrics['Model'], df_metrics['RMSE'], color='r')\n",
+        "plt.title('RMSE Comparison')\n",
+        "\n",
+        "# Plot MAPE\n",
+        "plt.subplot(224)\n",
+        "plt.bar(df_metrics['Model'], df_metrics['MAPE'], color='purple')\n",
+        "plt.title('MAPE Comparison')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "# Display the metrics DataFrame\n",
+        "print(\"Metrics Comparison:\")\n",
+        "print(df_metrics)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 711
+        },
+        "id": "TX-bgKLgB-Dw",
+        "outputId": "1753dba7-e72c-4b51-f80b-abbf2ebc06e1"
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 4 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Metrics Comparison:\n",
+            "     Model  R^2 Score           MSE         RMSE      MAPE\n",
+            "0      MLR   0.376278  2.455260e+07  4955.058490  0.300394\n",
+            "1       RF   0.209903  3.110192e+07  5576.909127  0.437794\n",
+            "2  XGBoost   0.193545  3.174587e+07  5634.347204  0.449728\n",
+            "3      GBM   0.313736  2.701457e+07  5197.553905  0.417668\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import cross_val_score, KFold\n",
+        "\n",
+        "# Initialize the Linear Regression model\n",
+        "model_lr = LinearRegression()\n",
+        "\n",
+        "# Perform 10-fold cross-validation\n",
+        "kf = KFold(n_splits=10, shuffle=True, random_state=42)\n",
+        "cv_scores = cross_val_score(model_lr, X, y, cv=kf, scoring='r2')\n",
+        "\n",
+        "# Print accuracy on each iteration\n",
+        "print(\"Accuracy on each cross-validation iteration:\")\n",
+        "for i, score in enumerate(cv_scores):\n",
+        "    print(f\"Iteration {i+1}: {score}\")\n",
+        "\n",
+        "# Calculate average validated accuracy\n",
+        "avg_accuracy = np.mean(cv_scores)\n",
+        "print(f\"\\nAverage Validated R^2 Score: {avg_accuracy}\")\n",
+        "\n",
+        "# Plotting count plot of accuracy at each iteration\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "plt.bar(range(1, 11), cv_scores, color='skyblue')\n",
+        "plt.axhline(y=avg_accuracy, color='r', linestyle='-', label=f'Average R^2 Score: {avg_accuracy:.2f}')\n",
+        "plt.title('Cross-Validation R^2 Scores')\n",
+        "plt.xlabel('Iteration')\n",
+        "plt.ylabel('R^2 Score')\n",
+        "plt.xticks(range(1, 11))\n",
+        "plt.legend()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 790
+        },
+        "id": "Ksk-HQePCSzX",
+        "outputId": "756807c6-993b-4c75-d140-ef8e7a6c756d"
+      },
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy on each cross-validation iteration:\n",
+            "Iteration 1: 0.3726599322823484\n",
+            "Iteration 2: 0.372416644214427\n",
+            "Iteration 3: 0.3783071334264724\n",
+            "Iteration 4: 0.6065471464628506\n",
+            "Iteration 5: 0.46927877192508505\n",
+            "Iteration 6: 0.29744495032480245\n",
+            "Iteration 7: 0.49197635368504467\n",
+            "Iteration 8: 0.30579011061627837\n",
+            "Iteration 9: 0.4732759087067022\n",
+            "Iteration 10: 0.3915968968529322\n",
+            "\n",
+            "Average Validated R^2 Score: 0.4159293848496944\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n",
+        "# Assume label_encoders dictionary is already defined from training phase\n",
+        "# label_encoders = {'sex': le_sex, 'smoker': le_smoker, 'region': le_region}\n",
+        "\n",
+        "# Load the trained model (model_lr) and label encoders\n",
+        "\n",
+        "def predict_insurance_charges(age, sex, bmi, children, smoker, region, model_lr, label_encoders):\n",
+        "    # Transform categorical variables using label encoders\n",
+        "    sex_encoded = label_encoders['sex'].transform([sex])[0]\n",
+        "    smoker_encoded = label_encoders['smoker'].transform([smoker])[0]\n",
+        "    region_encoded = label_encoders['region'].transform([region])[0]\n",
+        "\n",
+        "    # Prepare input data as DataFrame\n",
+        "    input_data = pd.DataFrame({\n",
+        "        'age': [age],\n",
+        "        'sex': [sex_encoded],\n",
+        "        'bmi': [bmi],\n",
+        "        'children': [children],\n",
+        "        'smoker': [smoker_encoded],\n",
+        "        'region': [region_encoded]\n",
+        "    })\n",
+        "\n",
+        "    # Make prediction using the trained Linear Regression model\n",
+        "    predicted_charge = model_lr.predict(input_data)[0]\n",
+        "\n",
+        "    return predicted_charge\n",
+        "\n",
+        "# Example of interactive user input and prediction\n",
+        "def main():\n",
+        "    print(\"Enter the following details to predict insurance charges:\")\n",
+        "    age = int(input(\"Age: \"))\n",
+        "    sex = input(\"Sex (male/female): \").strip().lower()\n",
+        "    bmi = float(input(\"BMI: \"))\n",
+        "    children = int(input(\"Number of children: \"))\n",
+        "    smoker = input(\"Smoker (yes/no): \").strip().lower()\n",
+        "    region = input(\"Region (northeast, northwest, southeast, southwest): \").strip().lower()\n",
+        "\n",
+        "    # Ensure valid input for categorical variables\n",
+        "    if sex not in ['male', 'female']:\n",
+        "        print(\"Invalid input for sex. Please enter 'male' or 'female'.\")\n",
+        "        return\n",
+        "    if smoker not in ['yes', 'no']:\n",
+        "        print(\"Invalid input for smoker. Please enter 'yes' or 'no'.\")\n",
+        "        return\n",
+        "    if region not in ['northeast', 'northwest', 'southeast', 'southwest']:\n",
+        "        print(\"Invalid input for region. Please enter one of: northeast, northwest, southeast, southwest.\")\n",
+        "        return\n",
+        "\n",
+        "    # Call prediction function\n",
+        "    predicted_charge = predict_insurance_charges(age, sex, bmi, children, smoker, region, model_lr, label_encoders)\n",
+        "\n",
+        "    # Display the prediction\n",
+        "    print(f\"\\nPredicted Insurance Charge: {predicted_charge:.2f} Rs\")\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    main()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Rbxgls2KCpqE",
+        "outputId": "573f6a99-77f4-4574-ff0d-cc83421c95d6"
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Enter the following details to predict insurance charges:\n",
+            "Age: 22\n",
+            "Sex (male/female): male\n",
+            "BMI: 17.90\n",
+            "Number of children: 0\n",
+            "Smoker (yes/no): no\n",
+            "Region (northeast, northwest, southeast, southwest): southwest\n",
+            "\n",
+            "Predicted Insurance Charge: 1614.08 Rs\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import joblib\n",
+        "\n",
+        "# Save the model\n",
+        "joblib.dump(model_lr, 'linear_regression_model.pkl')\n",
+        "\n",
+        "# Save the label encoders\n",
+        "joblib.dump(label_encoders, 'label_encoders.pkl')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OKNxVKLnDbTC",
+        "outputId": "22344ac3-61a0-47ff-95e7-f52e1bf811fb"
+      },
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['label_encoders.pkl']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Finacial Domain/Health Insurance Price Prediction/Images/boxplot-1.png b/Finacial Domain/Health Insurance Price Prediction/Images/boxplot-1.png
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diff --git a/Finacial Domain/Health Insurance Price Prediction/Images/cross_validation_graph.png b/Finacial Domain/Health Insurance Price Prediction/Images/cross_validation_graph.png
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diff --git a/Finacial Domain/Health Insurance Price Prediction/Images/gbm_actual_vs_predicted.png b/Finacial Domain/Health Insurance Price Prediction/Images/gbm_actual_vs_predicted.png
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diff --git a/Finacial Domain/Health Insurance Price Prediction/Images/xgboost_actual_Vs_predicted.png b/Finacial Domain/Health Insurance Price Prediction/Images/xgboost_actual_Vs_predicted.png
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diff --git a/Finacial Domain/Health Insurance Price Prediction/app.py b/Finacial Domain/Health Insurance Price Prediction/app.py
new file mode 100644
index 00000000..6fa84c8e
--- /dev/null
+++ b/Finacial Domain/Health Insurance Price Prediction/app.py	
@@ -0,0 +1,57 @@
+import streamlit as st
+import pandas as pd
+import joblib
+
+# Load the trained model and label encoders
+model_lr = joblib.load('linear_regression_model.pkl')
+label_encoders = joblib.load('label_encoders.pkl')
+
+
+# Function to predict insurance charges
+def predict_insurance_charges(age, sex, bmi, children, smoker, region):
+    # Transform categorical variables using label encoders
+    sex_encoded = label_encoders['sex'].transform([sex])[0]
+    smoker_encoded = label_encoders['smoker'].transform([smoker])[0]
+    region_encoded = label_encoders['region'].transform([region])[0]
+
+    # Prepare input data as DataFrame
+    input_data = pd.DataFrame({
+        'age': [age],
+        'sex': [sex_encoded],
+        'bmi': [bmi],
+        'children': [children],
+        'smoker': [smoker_encoded],
+        'region': [region_encoded]
+    })
+
+    # Make prediction using the trained Linear Regression model
+    predicted_charge = model_lr.predict(input_data)[0]
+
+    return predicted_charge
+
+
+# Streamlit app
+def main():
+    st.title('Health Insurance Price Prediction')
+    st.markdown('Enter the following details to predict insurance charges:')
+
+    # Input fields
+    age = st.number_input('Age', min_value=0, max_value=100, step=1)
+    sex = st.selectbox('Sex', ['male', 'female'])
+    bmi = st.number_input('BMI', min_value=10.0, max_value=50.0, step=0.1)
+    children = st.number_input('Number of Children', min_value=0, max_value=10, step=1)
+    smoker = st.selectbox('Smoker', ['yes', 'no'])
+    region = st.selectbox('Region of India', ['northeast', 'northwest', 'southeast', 'southwest'])
+
+    if st.button('Predict'):
+        # Call prediction function
+        predicted_charge = predict_insurance_charges(age, sex, bmi, children, smoker, region)
+
+        # Display prediction result in a green container with bold text
+        st.markdown(
+            f'<div style="background-color:#00FF00; padding:10px; border-radius:10px;"><h2 style="color:black; text-align:center;">Predicted Insurance Charge: <b>{predicted_charge:.2f}Rs</b></h2></div>',
+            unsafe_allow_html=True)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/Finacial Domain/Health Insurance Price Prediction/readme.md b/Finacial Domain/Health Insurance Price Prediction/readme.md
new file mode 100644
index 00000000..ec2083d2
--- /dev/null
+++ b/Finacial Domain/Health Insurance Price Prediction/readme.md	
@@ -0,0 +1,135 @@
+#  Health Insurance Price Prediction using Machine Learning
+
+Project Summary :
+
+Data Exploration and Preprocessing:
+
+Dataset:  https://www.kaggle.com/datasets/annetxu/health-insurance-cost-prediction
+
+Exploratory Data Analysis (EDA): 
+
+Utilized Plotly and Seaborn for visualizations including pie charts, histograms, violin plots, and box plots to understand data distributions, correlations, and outliers.
+
+Data Preprocessing:
+
+Label Encoding: Converted categorical variables (sex, smoker, region) into numerical format using LabelEncoder from scikit-learn.
+Handling Missing Values: Ensured data completeness by checking for and handling missing values appropriately.
+Normalization: Used StandardScaler from scikit-learn for feature scaling where applicable.
+
+
+Machine Learning Models:
+
+Linear Regression (LR):
+
+Trained a Linear Regression model to predict insurance charges based on features such as age, BMI, and others.
+Evaluated using metrics like R-squared (accuracy), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE).
+
+
+Random Forest (RF):
+
+Applied a Random Forest Regressor for prediction.
+Evaluated performance metrics similar to LR.
+
+
+XGBoost (XGB) and Gradient Boosting Machine (GBM):
+
+Implemented XGBoost and GBM models for comparison.
+Evaluated and compared their performance metrics with LR and RF.
+
+
+Model Evaluation and Comparison:
+
+Compared the performance of LR, RF, XGB, and GBM using metrics such as R-squared, MSE, RMSE, and MAPE.
+Visualized the actual vs. predicted values using line plots and evaluated the accuracy across different models.
+
+
+Deployment with Streamlit:
+
+Developed a Streamlit web application for predicting insurance charges based on user inputs (age, sex, BMI, children, smoker, region).
+Integrated the trained LR model and label encoders into the Streamlit app.
+Provided a user-friendly interface where users can input their data and get the predicted insurance charge displayed in a visually appealing green container with bold text.
+
+
+Future Directions:
+
+
+Model Improvement: Fine-tuning models for better accuracy, exploring ensemble techniques or deep learning approaches if needed.
+Feature Engineering: Further exploring feature interactions or transformations to enhance model performance.
+User Experience: Improving the UI/UX of the Streamlit app, adding more features such as data visualization options and model selection.
+
+
+Tools and Technologies Used:
+
+Programming Languages: Python
+Libraries and Frameworks: pandas, NumPy, scikit-learn, XGBoost, Plotly, Seaborn, Streamlit
+Data Visualization: Plotly, Seaborn for interactive and insightful visualizations.
+Machine Learning: Regression models (Linear Regression, Random Forest, XGBoost, GBM) for predictive analysis.
+Web Application Development: Streamlit for creating interactive and user-friendly web applications.
+
+Conclusion:
+
+The project revolves around leveraging machine learning techniques to predict insurance charges based on various customer attributes. The journey has included data exploration, preprocessing, model building, evaluation, and deployment using modern tools and frameworks. This structured approach ensures robust predictions and a seamless user experience through the Streamlit application.
+
+## How to Use
+
+1. **Clone the Repository**: 
+    ```sh
+    git clone url_to_this_repository
+    ```
+
+2. **Install Dependencies**: 
+    ```sh
+    pip install -r requirements.txt
+    ```
+
+3. **Run the Model**: 
+    ```python
+    streamlit run main.py
+    ```
+
+4. **View Results**: The script will allow you to predict the estimated cost of health insurance for a person
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diff --git a/Finacial Domain/Health Insurance Price Prediction/requirements.txt b/Finacial Domain/Health Insurance Price Prediction/requirements.txt
new file mode 100644
index 00000000..8ca84b6a
--- /dev/null
+++ b/Finacial Domain/Health Insurance Price Prediction/requirements.txt	
@@ -0,0 +1,5 @@
+scikit-learn==1.2.2
+joblib==1.4.2
+pandas==2.0.3
+numpy==1.25.2
+streamlit
\ No newline at end of file