{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 151,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0aRKNOQCMX05",
        "outputId": "0c47537d-2463-4efe-f590-7d4db7866e28"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server>=1.8->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.3.1)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server>=1.8->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.2.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (2.22)\n"
          ]
        }
      ],
      "source": [
        "! pip install pycaret"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 151,
      "metadata": {
        "id": "au4OTjRXymx9"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 152,
      "metadata": {
        "id": "l39NktdRMXwS"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pycaret as py\n",
        "from io import BytesIO"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 153,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "OIZXsSExMXqG",
        "outputId": "960ee927-bffe-4b9e-d78a-043021b2245c"
      },
      "outputs": [
        {
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              "\n",
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              "      border-color: transparent;\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      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",
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              "\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "\n",
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              "  </div>\n"
            ],
            "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\n",
              "...   ...     ...     ...       ...    ...        ...          ...\n",
              "1333   50    male  30.970         3     no  northwest  10600.54830\n",
              "1334   18  female  31.920         0     no  northeast   2205.98080\n",
              "1335   18  female  36.850         0     no  southeast   1629.83350\n",
              "1336   21  female  25.800         0     no  southwest   2007.94500\n",
              "1337   61  female  29.070         0    yes  northwest  29141.36030\n",
              "\n",
              "[1338 rows x 7 columns]"
            ]
          },
          "execution_count": 153,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# import du dataset dans une variable et affichage les lignes du dataset\n",
        "data = pd.read_csv(\"insurance.csv\")\n",
        "data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 154,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ptISOb7sM1Bk",
        "outputId": "e1f1a1ef-73a6-4207-8f87-e60bd16c3251"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1338, 7)"
            ]
          },
          "execution_count": 154,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Connaitre les dimensions du dataset\n",
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 155,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lbkacd3WM0ze",
        "outputId": "11064f76-2055-444d-c686-5996d3d6900c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "False    1337\n",
              "True        1\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 155,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Vérification des lignes dupliquées\n",
        "data_duplicated = data.duplicated()\n",
        "data_duplicated.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 156,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "laLj8fnPMXkq",
        "outputId": "2075a0d4-5b03-423b-ee73-1ac8fbbf9352"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "age           int64\n",
              "sex          object\n",
              "bmi         float64\n",
              "children      int64\n",
              "smoker       object\n",
              "region       object\n",
              "charges     float64\n",
              "dtype: object"
            ]
          },
          "execution_count": 156,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 157,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "anbrMmvhfW8C",
        "outputId": "edd5918a-51e1-49d6-f94b-80810c4ab795"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['female', 'male'], dtype=object)"
            ]
          },
          "execution_count": 157,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data[\"sex\"].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 158,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XAsgzM5QfWza",
        "outputId": "673a57c3-5e85-4cb6-8656-710b4dd424b0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['yes', 'no'], dtype=object)"
            ]
          },
          "execution_count": 158,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data[\"smoker\"].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TN7InbECfWsV",
        "outputId": "ae46c890-a5e1-4e5f-82bf-1de5b835b07b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['southwest', 'southeast', 'northwest', 'northeast'], dtype=object)"
            ]
          },
          "execution_count": 159,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data[\"region\"].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {
        "id": "_J-U449nfWjr"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 160,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "q0zGNXcWfWCS",
        "outputId": "bba59022-0fc0-42d4-a14a-b025871025d0"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"data\",\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\": \"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\": \"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}",
              "type": "dataframe",
              "variable_name": "data"
            },
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              "\n",
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              "\n",
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              "</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",
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              "  <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>1</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",
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              "      <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",
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              "      <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>0</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>0</td>\n",
              "      <td>northwest</td>\n",
              "      <td>3866.85520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <th>1333</th>\n",
              "      <td>50</td>\n",
              "      <td>male</td>\n",
              "      <td>30.970</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>northwest</td>\n",
              "      <td>10600.54830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1334</th>\n",
              "      <td>18</td>\n",
              "      <td>female</td>\n",
              "      <td>31.920</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>northeast</td>\n",
              "      <td>2205.98080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1335</th>\n",
              "      <td>18</td>\n",
              "      <td>female</td>\n",
              "      <td>36.850</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1629.83350</td>\n",
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              "    <tr>\n",
              "      <th>1336</th>\n",
              "      <td>21</td>\n",
              "      <td>female</td>\n",
              "      <td>25.800</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>southwest</td>\n",
              "      <td>2007.94500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1337</th>\n",
              "      <td>61</td>\n",
              "      <td>female</td>\n",
              "      <td>29.070</td>\n",
              "      <td>0</td>\n",
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              "      <td>northwest</td>\n",
              "      <td>29141.36030</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1338 rows × 7 columns</p>\n",
              "</div>\n",
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              "\n",
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              "      display:flex;\n",
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              "\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
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              "  }\n",
              "\n",
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              "  }\n",
              "\n",
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              "\n",
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              "    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",
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              "  }\n",
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              "\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-dff9e275-8acc-4821-83c5-e6472563efa3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_11bcd719-9af0-4c38-9363-c073bea60d4c\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_11bcd719-9af0-4c38-9363-c073bea60d4c button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "      age     sex     bmi  children  smoker     region      charges\n",
              "0      19  female  27.900         0       1  southwest  16884.92400\n",
              "1      18    male  33.770         1       0  southeast   1725.55230\n",
              "2      28    male  33.000         3       0  southeast   4449.46200\n",
              "3      33    male  22.705         0       0  northwest  21984.47061\n",
              "4      32    male  28.880         0       0  northwest   3866.85520\n",
              "...   ...     ...     ...       ...     ...        ...          ...\n",
              "1333   50    male  30.970         3       0  northwest  10600.54830\n",
              "1334   18  female  31.920         0       0  northeast   2205.98080\n",
              "1335   18  female  36.850         0       0  southeast   1629.83350\n",
              "1336   21  female  25.800         0       0  southwest   2007.94500\n",
              "1337   61  female  29.070         0       1  northwest  29141.36030\n",
              "\n",
              "[1338 rows x 7 columns]"
            ]
          },
          "execution_count": 160,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le= LabelEncoder()\n",
        "data.smoker = le.fit_transform (data.smoker)\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 161,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8I2pS-dugbLB",
        "outputId": "fb7c6f62-1d03-45de-c076-9825f3247e39"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['southwest', 'southeast', 'northwest', 'northeast'], dtype=object)"
            ]
          },
          "execution_count": 161,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data[\"region\"].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 162,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FIk0dVUqgbDe",
        "outputId": "8d97e6c5-ec34-40be-9764-83df18b88fc6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "region\n",
              "northeast    324\n",
              "northwest    325\n",
              "southeast    364\n",
              "southwest    325\n",
              "dtype: int64"
            ]
          },
          "execution_count": 162,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.groupby('region').size()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 162,
      "metadata": {
        "id": "ATLXQdxcgaxu"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 163,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5eGXjFYHi6sL",
        "outputId": "cb66da25-cce8-409b-c7dd-0b9e8cdc2a3d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "age           int64\n",
              "sex          object\n",
              "bmi         float64\n",
              "children      int64\n",
              "smoker        int64\n",
              "region       object\n",
              "charges     float64\n",
              "dtype: object"
            ]
          },
          "execution_count": 163,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 164,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0ur-I88MXgR",
        "outputId": "a659980e-aa1f-441d-923e-cb3132433f9c"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 164,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Vérification des lignes nulles\n",
        "data.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 165,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "zNSNxAhdMXX-",
        "outputId": "5d429439-c1e3-4b5d-8e8f-f52e8aa8e467"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"data\",\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\": \"smoker\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 472.9733223901743,\n        \"min\": 0.0,\n        \"max\": 1338.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.20478325859491778,\n          1.0,\n          0.4036940375456173\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}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-4e566803-0c3f-4e7f-a6c7-5ca1fd740722\" class=\"colab-df-container\">\n",
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              "      <th>age</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1338.000000</td>\n",
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              "      <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>0.204783</td>\n",
              "      <td>13270.422265</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>14.049960</td>\n",
              "      <td>6.098187</td>\n",
              "      <td>1.205493</td>\n",
              "      <td>0.403694</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>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>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>0.000000</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>0.000000</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>1.000000</td>\n",
              "      <td>63770.428010</td>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  </button>\n",
              "\n",
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              "\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      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-f77e9ebf-d332-442b-8496-bf1f2d458c08 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "               age          bmi     children       smoker       charges\n",
              "count  1338.000000  1338.000000  1338.000000  1338.000000   1338.000000\n",
              "mean     39.207025    30.663397     1.094918     0.204783  13270.422265\n",
              "std      14.049960     6.098187     1.205493     0.403694  12110.011237\n",
              "min      18.000000    15.960000     0.000000     0.000000   1121.873900\n",
              "25%      27.000000    26.296250     0.000000     0.000000   4740.287150\n",
              "50%      39.000000    30.400000     1.000000     0.000000   9382.033000\n",
              "75%      51.000000    34.693750     2.000000     0.000000  16639.912515\n",
              "max      64.000000    53.130000     5.000000     1.000000  63770.428010"
            ]
          },
          "execution_count": 165,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 166,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "WG3FXLQzMW3q",
        "outputId": "6021b832-e9ea-4179-e896-4fe7ba871662"
      },
      "outputs": [
        {
          "data": {
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              "      <th>0</th>\n",
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              "      <td>female</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>0</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>0</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>0</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>0</td>\n",
              "      <td>northwest</td>\n",
              "      <td>3866.85520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>1333</th>\n",
              "      <td>50</td>\n",
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              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>northwest</td>\n",
              "      <td>10600.54830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1334</th>\n",
              "      <td>18</td>\n",
              "      <td>female</td>\n",
              "      <td>31.920</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>18</td>\n",
              "      <td>female</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1629.83350</td>\n",
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              "      <td>21</td>\n",
              "      <td>female</td>\n",
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              "      <td>southwest</td>\n",
              "      <td>2007.94500</td>\n",
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              "    <tr>\n",
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              "      <td>northwest</td>\n",
              "      <td>29141.36030</td>\n",
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              "  </tbody>\n",
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              "\n",
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              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_6f4446eb-09c3-4402-b836-52ee897d7c2d button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "      age     sex     bmi  children  smoker     region      charges\n",
              "0      19  female  27.900         0       1  southwest  16884.92400\n",
              "1      18    male  33.770         1       0  southeast   1725.55230\n",
              "2      28    male  33.000         3       0  southeast   4449.46200\n",
              "3      33    male  22.705         0       0  northwest  21984.47061\n",
              "4      32    male  28.880         0       0  northwest   3866.85520\n",
              "...   ...     ...     ...       ...     ...        ...          ...\n",
              "1333   50    male  30.970         3       0  northwest  10600.54830\n",
              "1334   18  female  31.920         0       0  northeast   2205.98080\n",
              "1335   18  female  36.850         0       0  southeast   1629.83350\n",
              "1336   21  female  25.800         0       0  southwest   2007.94500\n",
              "1337   61  female  29.070         0       1  northwest  29141.36030\n",
              "\n",
              "[1338 rows x 7 columns]"
            ]
          },
          "execution_count": 166,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 167,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "WFsV7MSOcQFg",
        "outputId": "9f85fad7-7c5c-4eb9-8a84-65aab9a44b7a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: ylabel='age'>"
            ]
          },
          "execution_count": 167,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.boxplot(y= 'age', data=data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 168,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "eYcRO5pfcQLL",
        "outputId": "d1100ff3-6431-4387-f648-ea66240b8a46"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: ylabel='bmi'>"
            ]
          },
          "execution_count": 168,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.boxplot(y= 'bmi', data=data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 169,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ByttuoM8l0pG",
        "outputId": "ae660594-d84c-452d-dd87-95242f8b09c7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "les results sont 47.290000000000006 13.7\n"
          ]
        }
      ],
      "source": [
        "## gestion de bmi\n",
        "\n",
        "\n",
        "Q1 = data['bmi'].quantile(0.25)\n",
        "Q3 = data['bmi'].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "IQR\n",
        "\n",
        "k = 1.5\n",
        "lower_limit = Q1 - k * IQR\n",
        "upper_limit = Q3 + k * IQR\n",
        "print ('les results sont', upper_limit, lower_limit)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 170,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nmm4dNhow07p",
        "outputId": "320ced8b-50e5-4827-edae-2fca0d96aee2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Nombre d'outliers dans la colonne 'bmi': 9\n"
          ]
        }
      ],
      "source": [
        "\n",
        "outliers = data[(data['bmi'] < lower_limit) | (data['bmi'] > upper_limit)]\n",
        "num_outliers = len(outliers)\n",
        "print(\"Nombre d'outliers dans la colonne 'bmi':\", num_outliers)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 171,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zq8_ZgK4w01k",
        "outputId": "a9fa2044-a360-47ac-8aec-19d1c7f95247"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Nombre d'outliers dans la colonne 'bmi': 9\n",
            "Nombre d'outliers : 9\n"
          ]
        }
      ],
      "source": [
        "outliers = data[(data['bmi'] < lower_limit) | (data['bmi'] > upper_limit)]\n",
        "num_outliers = len(outliers)\n",
        "print(\"Nombre d'outliers dans la colonne 'bmi':\", num_outliers)\n",
        "\n",
        "#  Sélectionner les outliers contigus dans la première plage\n",
        "outliers = data[(data['bmi'] < lower_limit) | (data['bmi'] > upper_limit)]\n",
        "len(outliers)\n",
        "\n",
        "\n",
        "# Afficher la taille des deux groupes\n",
        "print(\"Nombre d'outliers :\", len(outliers))\n",
        "\n",
        "\n",
        "# Supprimer les outliers non contigus du DataFrame principal\n",
        "data.drop(outliers.index, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 172,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "kFrC8ql1w0rc",
        "outputId": "193e7c21-9858-42e1-938d-5b1b20f4d3f9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: ylabel='bmi'>"
            ]
          },
          "execution_count": 172,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.boxplot(y='bmi', data=data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 173,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "gTsq6l9rw0lJ",
        "outputId": "bc13a972-afea-4669-b85a-5185d5b79cf5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: ylabel='region'>"
            ]
          },
          "execution_count": 173,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.boxplot(y='region', data=data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 174,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "HlOhDC6gcQSr",
        "outputId": "8d2258bc-339b-4bba-ca0d-a853ac60b154"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: ylabel='children'>"
            ]
          },
          "execution_count": 174,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.boxplot(y= 'children', data=data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 175,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "IgVM3zLRcQXi",
        "outputId": "a92460fd-0c1e-4d90-c3f9-3bfb11cbbcff"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: ylabel='charges'>"
            ]
          },
          "execution_count": 175,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.boxplot(y= 'charges', data=data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "45Wih_yAKpDM"
      },
      "source": [
        "Utilisation de pycaret"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 176,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0QJ9ahKpWq5C",
        "outputId": "432cf733-fff7-4b2c-9fc0-e9556c535f28"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "11848.22951"
            ]
          },
          "execution_count": 176,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "## gestion de charges\n",
        "\n",
        "\n",
        "Q1 = data['charges'].quantile(0.25)\n",
        "Q3 = data['charges'].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "IQR"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 177,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OglcKiQNcQiA",
        "outputId": "b170fe74-1356-4833-ff8b-6888147c7d3e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "les results sont 34358.841975 -13034.076065\n"
          ]
        }
      ],
      "source": [
        "k = 1.5\n",
        "lower_limit = Q1 - k * IQR\n",
        "upper_limit = Q3 + k * IQR\n",
        "print ('les results sont', upper_limit, lower_limit)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 178,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0EHHK1ycQoO",
        "outputId": "4a081e5e-b6e1-462d-ad46-fa2f81602d09"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Nombre d'outliers dans la colonne 'charges': 138\n"
          ]
        }
      ],
      "source": [
        "outliers = data[(data['charges'] < lower_limit) | (data['charges'] > upper_limit)]\n",
        "num_outliers = len(outliers)\n",
        "print(\"Nombre d'outliers dans la colonne 'charges':\", num_outliers)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 179,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yugyBA9BcQuW",
        "outputId": "e3a6b01c-d862-429c-92c4-403214809e8d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Nombre d'outliers : 138\n"
          ]
        }
      ],
      "source": [
        "#  Sélectionner les outliers contigus dans la première plage\n",
        "outliers = data[(data['charges'] < lower_limit) | (data['charges'] > upper_limit)]\n",
        "len(outliers)\n",
        "\n",
        "\n",
        "# Afficher la taille des deux groupes\n",
        "print(\"Nombre d'outliers :\", len(outliers))\n",
        "\n",
        "\n",
        "# Supprimer les outliers non contigus du DataFrame principal\n",
        "data.drop(outliers.index, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 180,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "9X95AXZScQy-",
        "outputId": "4052ccf8-8b03-419a-9a17-09c1111aff29"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: ylabel='charges'>"
            ]
          },
          "execution_count": 180,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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2wLmrV6/W4MGD1aNHD2VmZmrfvn3+mtvt1uzZs3XNNdcoIyNDkydPltPp9NcLCgo0btw4ZWRkqH///nr66afl9Xob56IBIMhqwq4k3XDDDdq4caPuu+8+3XDDDbrvvvu0ceNG3XDDDf4xLpcrCF0CQOMJWiCuqqrS2LFjddVVV2nnzp3avHmzTpw4oblz56qoqEgTJ07UqFGjtHPnTs2YMUOzZs2Sw+GQJG3btk1LlizRU089pR07dqh///4aP368ysvLJUmLFy9WTk6OsrOz9f7778vn8+lPf/qT/89+8MEHFRcXp61bt2rVqlXaunWrXnnllaD8HgAgWMxms2bNmiWLxRJw3GKxaNasWf5ZYZ/PF4z2AKDRBG3JREVFhbKysjR06FCFhobKbrdr4MCBeu2117Rp0yZ17NhRw4cPlyT17t1b1157rdauXav09HRlZ2dr2LBh6t69uyTp3nvv1erVq/Xxxx9r8ODBWrdunRYsWKB27dpJkqZOnaqbbrpJx44dU1FRkfbv369Vq1YpMjJSkZGRGj16tF555RWNGTOm1v37fD5/AAeAS0nNemGv16vS0tLzPnijsrLS/y9nVquVzzsAlxyfz1frfdSDFoijo6M1YsQI/+sDBw5ow4YNuuGGG5STk6OUlJSA8SkpKdqyZYskKScnRzfeeKO/ZjablZycLIfDoeTkZJWWlio1NdVf79Spk8LCwpSTk6OioiLFx8crOjraX09NTdXBgwdVVlamiIiIWvXv8XiUm5tbp2sHgGByu93+n2+77TY99thjslgs8ng8/u9z5szxj3E4HLLZbMFoFQAuSs0EwG8J+k11BQUFGjx4sKqrqzVy5EhNnjxZ9913n+Li4gLGxcTE+NcBu1yugEArnQnYTqfTv9YtKioqoB4VFeWv/7xW815Op7PWgdhisSgpKanW1wkATYXX6/UH3xMnTuiBBx6QyWTyz6acvUTCYrGoe/fuPK0OwCUnLy+v1mODHojj4+PlcDh06NAhzZ49Ww8//HCtzvutNW2/Vq+P9XAmk0nh4eEX/T4AEAzXX3+93n//fVVVVUn6v8/Fsz8frVarrr/+erVs2TIoPQLAxbiQv8g3iX10TCaTOnbsqKysLG3evFmhoaHn3NXsdDplt9slSbGxsefUXS6X7Ha7f8zP6ydPnlSrVq1kt9vPe67JZPKfCwDNXZ8+fVRVVaWoqCi1aNEioNaiRQtFRUWpqqpKV199dZA6BIDGE7RAvHPnTg0ePDhgu7OaO5q7desWsI2aJO3bt89/E11aWppycnL8tdOnT+ubb75R9+7d1b59e0VHRwfUv/vuO1VVVSktLU1paWkqLCwM2HbI4XAoKSmJWRAAhrF9+3aFh4erpKREVVVV6tmzpwYNGqSePXuqqqpKJSUlatGihT7//PNgtwoADS5ogTgtLU1lZWV6+umnVVFRoeLiYi1ZskRXXHGFMjMzVVBQoLVr18rtduvTTz/Vp59+qpEjR0qSMjMztXHjRu3du1cVFRVatmyZrFar+vXrp5CQEI0cOVLLly9XYWGhnE6nnnnmGQ0cOFCtW7dWSkqK0tPTtWjRIpWVlSk/P1+rVq1SZmZmsH4VANCovF6v3n//fVVWVqpr166y2WzavXu3PvjgA+3evVs2m01du3aV2+3We++9x7ZrAJo9ky+In3Tffvut5s2bp3/+858KDw9Xr1699MgjjyguLk67du3SvHnzlJ+fr/j4eD300EMaNGiQ/9zXX39dK1eu1IkTJ5Senq65c+eqc+fOks7scTx//ny98847qq6uVv/+/TV37lxFRkZKko4ePapZs2bpyy+/VEREhEaNGuW/qaQ2avZDTk9Pr+ffCAA0vIqKCl199dWyWq2qqqpSSEiIevToofbt2+vw4cP66quvdPr0aX/9888/P2dZBQA0dReS14IaiC9VBGIAl7Lq6mpdddVVkqSEhARlZ2fLYrGopKREUVFR8ng8uu2223TkyBFJ0j/+8Q8e3QzgknMheS3ou0wAABqXx+Px/3zPPfdo1KhROnz4sP9Y+/btdc899+ixxx6TdGbfYmaIATRnBGIAMJiQkBD/zzWh12w2y+v1ymw26/Dhw/7jUu03tgeASxWBGAAMpqysLOC12WxWz5491bFjR33//ffavXt3wA5AJSUlio2Nbew2AaDREIgBwGDOflpnmzZtdOWVV2rbtm3atWuXwsLCdP3112vXrl06fvy4JJ3zZFAAaG4IxABgYCdOnNBXX32lu+66S5GRkSotLdXbb7+tEydOBLs1AGg0BGIAMJizn9YZERGhkydPauXKlf5jLVq0UEREhEpKSvzjeZIngOaMQAwABmOz2STJv9WadCYYx8bGyul0qqysTBUVFbJYLPJ4PAoLCwtmuwDQ4AjEAGAwNY+p93g8Cg8Pl9vtVllZmf9mu5CQENlsNpWXl0sSW64BaPYIxABgMGazWTabTW63W+Xl5f5dJuLi4nTs2DHt2bPHH4ZtNlutn+IJAJcqAjEAGEx1dbXcbrf/tdfr1Z49e/z7EJ+95Zrb7fYfB4Dmik84ADCYmnXDZ6sJwWeH4RonT55s8J4AIJgIxABgMOHh4f6fe/TooYSEhIB6QkKCevTo4X8dGRnZaL0BQDCwZAIADKZmfbAk7dmzR7/73e90//33y263q7i4WG+//bb27NnjH1NaWsqT6gA0awRiADCYiIgI/882m03dunXTyy+/LLfbLZvNpn79+snpdKqyslJS4JPtAKA5IhADgMGcPn3a/3NlZaXef/99/04SVVVVev/99wPGV1VVsfUagGaNNcQAYDAWiyXgtdlsls/nkyT5fL5zdpSoeZAHADRXBGIAMBiPxxPw2uv1+meITSbTOTtNnL1FGwA0RwRiADAYZogBIBCBGAAM5uczvjVh+Jde19xcBwDNFYEYAAzO5/MFLJn4eSAGgOaOQAwABvPzJRBhYWEBgTgsLOycOgA0ZwRiADCYn99U5/F4Ah7d/PM6N9UBaO4IxABgMDWzwTXO3pf4fK9/fhMeADQ3BGIAMJgTJ05c0Pjjx483UCcA0DQQiAHAYFq1ahXwOiwsTCEhIZKkkJCQc9YMt2nTptF6A4Bg4NHNAGAwZy+JMJlMio6O1tChQ9W2bVsVFRVpw4YNcrvd/t0mqqqqFBrK/y4ANF98wgGAwVRXV/t/tlqt6tGjh15++WVVVlYqLCxM/fv310cffaSqqipJ5+5LDADNDYEYAAzm7F0j3G63tmzZonbt2ikuLk7Hjh3Tli1bAsZXVlaqZcuWjd0mADQa1hADgMG0aNHC/3PHjh0VEhKiwsJC7d27V4WFhQoJCVGHDh3OOx4AmiNmiAHAYM6e7f3+++8l/d8T6kwmk06fPq1Dhw75xxCIATR3zBADgMGYzWYlJycHHDv7SXVnS0lJOecYADQ3BGIAMKDw8PCA12c/qe7XxgFAc0QgBgCD8Xq92rNnz28+gS40NFR79uxhlwkAzR6BGAAMpqKiQl6vVx6P51fHVVdX6/Tp06qoqGikzgAgOAjEAGBwZrP5V18DQHPHLhMAYDA2m+2cY1deeaUSExN18OBB7d69O6D280c5A0BzQyAGAIM5ewlEixYt9N577ykyMtJ/rLS0VNdff71/XEVFBQ/mANCsEYgBwGDODsQVFRW67bbbNHToUMXHx6ugoEAbNmwIGON2uwnEAJo1AjEAGMzZu0uYzWadPHlSL774oqqrqxUaGiqLxSKz2ezfgu23dqMAgEsdd04AgMGcPn3a/3OvXr00YMAAhYSESJJCQkI0YMAAZWRk+Mf81m4UAHCpY4YYAAwmJibG//OOHTvUtm1bjRkzRm3btlVRUZHWr1+voqKi844HgOaIQAwABhMaGqq4uDgdO3ZMknT8+HGtXLlSXq9XZrM54EEcv/vd79iGDUCzRyAG8IuOHDmi0tLSYLeBBjBy5EgtWbJEkmQymQIe3Xx2KB4xYoRyc3OD1icaTmRkpBISEoLdBtAkmHw8k/OCORwOSVJ6enqQOwEajtPp1MCBA/1BCUDzEhISog8++ECxsbHBbgVoEBeS15ghBnBesbGx2rhxIzPEzdwXX3yhtWvX6ujRo/5jv/vd7zRixAj16tUriJ2hoUVGRhKGgf+PQAzgF/HPqc1fcnKyxowZI4fDobvvvluvvvqqUlNTg90WADQq7pQAACg09Mz8CDfQATCioH7yFRQUaNKkScrIyFDv3r31yCOPqKSkREeOHFGXLl2Unp4e8PXXv/7Vf+67776rW265RZdffrmGDRum7du3+2ter1eLFy/WgAEDdOWVV+qee+7R4cOH/XWXy6WpU6eqd+/e6tOnj2bMmKHKyspGvXYAAAA0DUENxOPHj1dUVJS2bdum9evX61//+pcWLFjgrzscjoCve+65R5KUm5ur6dOna9q0afriiy80evRoPfDAA/41cGvWrNGmTZu0cuVKffzxx+rYsaMmTZrkv2t61qxZqqio0ObNm/Xmm28qPz9fCxcubPxfAAAAAIIuaGuIS0pKlJaWpoceekgtW7ZUy5YtNXToUL366qu/ee7atWvVt29f9e3bV5I0ZMgQvfbaa3r77bc1btw4ZWdna/To0erUqZMkKSsrSxkZGfr666+VkJCgrVu3asOGDbLb7ZKkiRMnasqUKZo+fXqtH1Hq8/lUXl5ex6sHgKal5l/JKisr+WwD0Cz4fD6ZTKZajQ1aII6KitL8+fMDjhUWFqpt27b+1w8//LB27Nih6upqjRgxQpMnT5bFYlFOTo4/DNdISUmRw+FQZWWl8vLylJKS4q9FRESoQ4cOcjgcKi0tVUhIiLp06eKvp6amqry8XAcOHAg4/ms8Hg97cwJoNg4dOiRJOnjwIFvtAWg2rFZrrcY1mV0mHA6HXnvtNS1btkxWq1WXX365Bg4cqCeffFK5ubl68MEHFRoaqilTpsjlcik6Ojrg/OjoaOXl5enkyZPy+XznrTudTsXExCgiIiLgbww1Y51OZ637tVgsSkpKuogrBoCmo+ZmusTExFpPDABAU5aXl1frsU0iEO/evVsTJkzQQw89pN69e0uS/ud//sdf79atm+6//36tWLFCU6ZMkST91vNEfq1eH88iMZlMCg8Pv+j3AYCmICwszP+dzzYAzUFtl0tITWDbtW3btmncuHF69NFHddddd/3iuPj4eP3000/y+XyKjY2Vy+UKqLtcLtntdsXExMhsNp+33qpVK9ntdpWVlen06dMBNUlq1apVfV0WAAAALhFBDcRfffWVpk+frueee0633nqr//jOnTu1bNmygLEHDhxQfHy8TCaT0tLStG/fvoC6w+FQ9+7dZbPZdNlllyknJ8dfKykp0Q8//KBu3bopOTlZPp9P+/fvDzg3KipKiYmJDXOhAAAAaLKCFoirq6s1c+ZMTZs2TX369AmoRUZG6oUXXtBbb70lj8cjh8Ohv/71r8rMzJQkjRw5Ujt27NAnn3wit9utdevW6fvvv9eQIUMkSZmZmVq9erXy8/NVVlamhQsXKjk5Wenp6bLb7Ro8eLCeffZZFRcX6+jRo3rhhRc0fPhw/8b0AAAAMI6gJcC9e/cqPz9f8+bN07x58wJq7733nhYvXqylS5dq9uzZioyM1J133qm7775bktS5c2ctXLhQ8+fPV0FBgZKSkrRixQq1adNGkjRq1CgdP35cd955p06dOqWMjAwtXbrU//6PP/645syZowEDBshisejmm29WVlZW4108AAAAmgyTrz7uMDMYh8MhSUpPTw9yJwBQP3Jzc3X77bdrzZo1Sk5ODnY7AHDRLiSvBf2mOgAAACCYCMQAAAAwNAIxAAAADI1ADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADI1ADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADI1ADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADI1ADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADI1ADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADI1ADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADI1ADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADI1ADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADI1ADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADI1ADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADI1ADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADC2ogbigoECTJk1SRkaGevfurUceeUQlJSWSpNzcXN1xxx3q2bOnBg0apJdeeing3HfffVe33HKLLr/8cg0bNkzbt2/317xerxYvXqwBAwboyiuv1D333KPDhw/76y6XS1OnTlXv3r3Vp08fzZgxQ5WVlY1z0QAAAGhSghqIx48fr6ioKG3btk3r16/Xv/71Ly1YsECVlZW6//771atXL3322WdavHixVqxYoQ8++EDSmbA8ffp0TZs2TV988YVGjx6tBx54QEePHpUkrVmzRps2bdLKlSv18ccfq2PHjpo0aZJ8Pp8kadasWaqoqNDmzZv15ptvKj8/XwsXLgza7wEAAADBExqsP7ikpERpaWl66KGH1LJlS7Vs2VJDhw7Vq6++qk8++UQej0cTJkxQSEiIUlNTNWLECGVnZ2vQoEFau3at+vbtq759+0qShgwZotdee01vv/22xo0bp+zsbI0ePVqdOnWSJGVlZSkjI0Nff/21EhIStHXrVm3YsEF2u12SNHHiRE2ZMkXTp0+XxWKpVf8+n0/l5eUN88sBgEZW869klZWVfLYBaBZ8Pp9MJlOtxgYtEEdFRWn+/PkBxwoLC9W2bVvl5OSoS5cuCgkJ8ddSUlK0du1aSVJOTo4/DJ9ddzgcqqysVF5enlJSUvy1iIgIdejQQQ6HQ6WlpQoJCVGXLl389dTUVJWXl+vAgQMBx3+Nx+NRbm7uBV83ADRFhw4dkiQdPHhQXq83yN0AQP2wWq21Ghe0QPxzDodDr732mpYtW6YtW7YoKioqoB4TEyOXyyWv1yuXy6Xo6OiAenR0tPLy8nTy5En5fL7z1p1Op2JiYhQRERHwN4aasU6ns9b9WiwWJSUlXehlAkCTZDafWUGXmJhY64kBAGjK8vLyaj22SQTi3bt3a8KECXrooYfUu3dvbdmy5bzjzg6xNeuBf8mv1X/r3NowmUwKDw+/6PcBgKYgLCzM/53PNgDNQW2XS0h1vKmuurpa//u//+t//emnn2rixIlatGiRqqqqLui9tm3bpnHjxunRRx/VXXfdJUmy2+3nzNa6XC7FxMTIbDYrNjZWLpfrnLrdbvePOV+9VatWstvtKisr0+nTpwNqktSqVasL6h0AAACXvjoF4oULF2rVqlWSpB9//FEPPvigIiIi9Pe//11PPfVUrd/nq6++0vTp0/Xcc8/p1ltv9R9PS0vTt99+q+rqav8xh8Oh7t27++v79u0LeK+aus1m02WXXaacnBx/raSkRD/88IO6deum5ORk+Xw+7d+/P+DcqKgoJSYmXtDvAQAAAJe+OgXiLVu2aPny5ZKkTZs2qXv37nrqqae0ZMkSbdu2rVbvUV1drZkzZ2ratGnq06dPQK1v376KiIjQsmXLVFFRoa+//lrr1q1TZmamJGnkyJHasWOHPvnkE7ndbq1bt07ff/+9hgwZIknKzMzU6tWrlZ+fr7KyMi1cuFDJyclKT0+X3W7X4MGD9eyzz6q4uFhHjx7VCy+8oOHDhys0tEmsIAEAAEAjqlMCLCkpUYcOHSRJO3bs0IABAyRJcXFxKi4urtV77N27V/n5+Zo3b57mzZsXUHvvvfe0fPlyzZkzRytXrlTr1q2VlZWlfv36SZI6d+6shQsXav78+SooKFBSUpJWrFihNm3aSJJGjRql48eP684779SpU6eUkZGhpUuX+t//8ccf15w5czRgwABZLBbdfPPNysrKqsuvAgAAAJe4OgXi2NhYHTlyRDabTXv27NHs2bMlndk2rbY3Y1xxxRX69ttvf3XMG2+88Yu1QYMGadCgQeetmUwmTZ48WZMnTz5vPTIyUs8880yt+gQAAEDzVqdAfOutt2rUqFEKCQlRjx491KlTJ506dUrTp0/3z+ICAAAAl4I6BeLJkycrKSlJJSUluvnmmyWd2Ze3Q4cOmj59er02CAAAADSkOt9FduONN0qSfycIq9WqJ554on66AgAAABpJnXaZ8Hq9ev7553XttdeqR48ekqSKigrNmTPngvchBgAAAIKpToF4yZIlevPNN3XHHXf4j5WXl2vv3r167rnn6q05AAAAoKHVKRC/9dZbWrZsmcaOHet/LF6rVq20ePFivfXWW/XaIAAAANCQ6hSIi4uLlZKScs7xDh066OTJkxfdFAAAANBY6hSIf//73ys3N1eS5PP5/Md37NjhfzgGAAAAcCmo0y4TQ4YM0aRJk3TPPffI5/Ppgw8+0L59+/TGG29ozJgx9d0jAAAA0GDqFIjvv/9+VVVV6fnnn5fH49HkyZPVunVrjR8/nkAMAACAS0qdAnHNo5EffPBBFRcXy2azKSIior57AwAAABpcnQLxrl27frFmNpvVrl07/f73v69zUwAAAEBjqVMgvvPOO/3brdXcVHf2a5PJpNTUVD3//PMEYwAAADRpddpl4uWXX1ZSUpIeffRRrV+/Xhs3btTMmTOVnJysv/zlL/rrX/+qli1basGCBfXdLwAAAFCv6jRD/Pzzz+vPf/6z0tPT/ce6du2qbt266fnnn9eLL76ozp0769Zbb62vPgEAAIAGUacZ4m+++UZdunQ553jXrl21e/duSVJMTIzKy8svrjsAAACggdUpELdu3VovvviiTp8+7T/m8/m0evVq/24TL730kjp27FgvTQIAAAANpU5LJqZMmaJHHnlEr7zyitq1ayeLxaIff/xRTqdTM2fOlMfj0dKlS/X888/Xd78AAABAvapTIL7lllvUs2dPvfPOOzp27Ji8Xq+uu+46DRw4UJ06dZIkbdu2Ta1bt67XZgEAAID6VqdA/PLLL2v06NG67777fnEMYRgAAACXgjqtIV62bBk3zAEAAKBZqFMgnjZtmp544gnt379fp06dUlVVVcAXAAAAcKmo05KJp556SlVVVdq4ceN567m5uRfTEwAAANBo6hSIH3300fruAwAAAAiKOgXioUOH/mLtueeeq3MzAAAAQGOrUyCWpPz8fDkcDrndbv+xH3/8Ua+88oqmTJlSL80BAAAADa1OgXjTpk2aPn26vF6vTCaTfD6fJCk6Olp33XVXvTYIAAAANKQ67TKxYsUKzZkzR//85z9lsVj0zTffaM2aNerRo4dGjhxZ3z0CAAAADaZOM8QFBQUaOXKkTCaTJMlsNqtnz54ym82aPXu2XnrppXptEgAAAGgodZohtlqtKisrkySFh4erqKhIktStWzft3bu33poDAAAAGlqdAnGfPn00btw4lZeXq1u3bpo/f74cDodWr16tyMjI+u4RAAAAaDB1CsSPPvqooqOjFRoaqqlTp2rHjh0aMWKEFi1apAceeKC+ewQAAAAaTJ3WELdq1UrLly+XJKWkpOijjz5Sfn6+4uPj1bp163ptEAAAAGhIdd6HuLq6WkVFRf59iKOiolRaWqrS0lIlJibWW4MAAABAQ6pTIN66datmzJihkpKSgOM+n08mk0m5ubn10hwAAADQ0OoUiJ988kldc801uuWWWxQWFlbfPQEAAACNpk6BuLi4WE8++aSsVmt99wMAAAA0qjrtMnHZZZfp5MmT9d0LAAAA0OjqFIjnzp2rp59+Wnl5eXK73aqqqgr4AgAAAC4VtV4y0bVrV/+jmqUzN9Bt2rTpvGO5qQ4AAACXiloH4j//+c/nBOKqqirZbDZJUmlpqSIiIgLGAAAAAE1drQPxsGHD/D//+OOPGj9+vCZOnKjrr79ekvTSSy/ppZde0ooVK+q/SwAAAKCB1GkN8fz585WYmKgePXr4jw0ZMkRpaWn685//XG/NAQAAAA2tTtuu7dq1S9u2bVN4eLj/WOvWrTVr1ixde+219dYcAAAA0NDqNEPs8/nk8XjOOX7q1CmdPn36opsCAAAAGkudAvE111yj6dOna//+/SorK1NJSYn27Nmjhx56SP369avnFgEAAICGU6clE3/60580adIk3XrrrQG7SlxxxRWaM2dOvTUHAAAANLQ6BWK73a433nhD+/fv16FDhxQSEqKOHTsqKSnpgt/rs88+0/Tp05WRkaHFixf7j69fv16PPvqoLBZLwPg1a9aoW7du8nq9eu6557R582aVlJSoW7dumjt3rtq3by9Jcrlcmjt3rr788kuZzWb17dtXs2bNUlhYmKQzeyU/+eSTys3NVatWrTRq1CiNHTu2Lr8OAAAAXMLqFIhrdO3aVV27dq3z+S+++KLWrVunDh06nLd+5ZVX6tVXXz1vbc2aNdq0aZNefPFFxcXFafHixZo0aZLeeustmUwmzZo1S1VVVdq8ebM8Ho+mTJmihQsXaubMmaqsrNT999+vkSNHauXKlTp48KDGjh2rhIQEDRo0qM7XAwAAgEvPRQXii2Wz2bRu3To9+eSTcrvdF3Rudna2Ro8erU6dOkmSsrKylJGRoa+//loJCQnaunWrNmzYILvdLkmaOHGipkyZounTp+uTTz6Rx+PRhAkTFBISotTUVI0YMULZ2dm1DsQ+n0/l5eUXdsEA0ERVVlb6v/PZBqA58Pl8tX5gXFAD8V133fWr9cLCQo0ZM0b79u1TVFSUJk+erD/84Q+qrKxUXl6eUlJS/GMjIiLUoUMHORwOlZaWKiQkRF26dPHXU1NTVV5ergMHDignJ0ddunRRSEiIv56SkqK1a9fWunePx8MjqgE0G4cOHZIkHTx4UF6vN8jdAED9sFqttRoX1ED8a+x2uzp27Kj/+q//UlJSkj788EM9/PDDatu2rf7t3/5NPp9P0dHRAedER0fL6XQqJibmnMdI14x1Op1yuVyKiooKODcmJkYul0ter1dm829vvmGxWOq0ZhoAmqKaz73ExMSAyQQAuFTl5eXVemyTDcT9+vUL2MLtpptu0ocffqj169dr2rRpks5Mhf+SX6v9ktpOq9eMPfvBJABwKau54TgsLIzPNgDNwoXkujrtQxws8fHxKioqUkxMjMxms1wuV0Dd5XKpVatWstvtKisrC3hISM3YmrrT6Tzn3Jr3BQAAgHE02fT3xhtv6N133w04lp+fr/bt28tms+myyy5TTk6Ov1ZSUqIffvhB3bp1U3Jysnw+n/bv3++vOxwORUVFKTExUWlpafr2229VXV0dUO/evXvDXxgAAACalCYbiKuqqvTEE0/I4XDI4/Fo8+bN+tvf/qZRo0ZJkjIzM7V69Wrl5+errKxMCxcuVHJystLT02W32zV48GA9++yzKi4u1tGjR/XCCy9o+PDhCg0NVd++fRUREaFly5apoqJCX3/9tdatW6fMzMwgXzUAAAAaW1DXEKenp0uSf6Z269atks7M1t511106deqUpkyZouPHjyshIUEvvPCC0tLSJEmjRo3S8ePHdeedd+rUqVPKyMjQ0qVL/e/9+OOPa86cORowYIAsFotuvvlmZWVlSTpzx+Hy5cs1Z84crVy5Uq1bt1ZWVhaPnQYAADAgk68ud58ZnMPhkPR/gR4ALnW5ubm6/fbbtWbNGiUnJwe7HQC4aBeS15rskgkAAACgMRCIAQAAYGgEYgAAABgagRgAAACGRiAGAACAoRGIAQAAYGgEYgAAABgagRgAAACGRiAGAACAoRGIAQAAYGgEYgAAABgagRgAAACGRiAGAACAoRGIAQAAYGgEYgAAABhaaLAbwKWlsLBQLpcr2G0AqGcHDx4M+A6geYmJiVG7du2C3UaTRSBGrRUWFmrosGGqcruD3QqABjJz5sxgtwCgAVhtNm1Yv55Q/AsIxKg1l8ulKrdbxUlXq7pFdLDbAQAAtRBacVL2vM/lcrkIxL+AQIwLVt0iWp6IVsFuAwAAoF5wUx0AAAAMjUAMAAAAQyMQAwAAwNAIxAAAADA0AjEAAAAMjUAMAAAAQyMQAwAAwNAIxAAAADA0AjEAAAAMjUAMAAAAQyMQAwAAwNAIxAAAADA0AjEAAAAMjUAMAAAAQyMQAwAAwNAIxAAAADA0AjEAAAAMjUAMAAAAQyMQAwAAwNAIxAAAADA0AjEAAAAMjUAMAAAAQyMQAwAAwNAIxAAAADA0AjEAAAAMjUAMAAAAQyMQAwAAwNAIxAAAADC0oAfizz77TL1791ZWVtY5tXfffVe33HKLLr/8cg0bNkzbt2/317xerxYvXqwBAwboyiuv1D333KPDhw/76y6XS1OnTlXv3r3Vp08fzZgxQ5WVlf56bm6u7rjjDvXs2VODBg3SSy+91LAXCgAAgCYpqIH4xRdf1Lx589ShQ4dzarm5uZo+fbqmTZumL774QqNHj9YDDzygo0ePSpLWrFmjTZs2aeXKlfr444/VsWNHTZo0ST6fT5I0a9YsVVRUaPPmzXrzzTeVn5+vhQsXSpIqKyt1//33q1evXvrss8+0ePFirVixQh988EHjXTwAAACahNBg/uE2m03r1q3Tk08+KbfbHVBbu3at+vbtq759+0qShgwZotdee01vv/22xo0bp+zsbI0ePVqdOnWSJGVlZSkjI0Nff/21EhIStHXrVm3YsEF2u12SNHHiRE2ZMkXTp0/XJ598Io/HowkTJigkJESpqakaMWKEsrOzNWjQoFr17vP5VF5eXo+/jabv7Bl2AABwaamsrDRUdvH5fDKZTLUaG9RAfNddd/1iLScnxx+Ga6SkpMjhcKiyslJ5eXlKSUnx1yIiItShQwc5HA6VlpYqJCREXbp08ddTU1NVXl6uAwcOKCcnR126dFFISEjAe69du7bWvXs8HuXm5tZ6fHNw6NChYLcAAADq6ODBg/J6vcFuo1FZrdZajQtqIP41LpdL0dHRAceio6OVl5enkydPyufznbfudDoVExOjiIiIgL8V1Ix1Op1yuVyKiooKODcmJkYul0ter1dm82+vJLFYLEpKSqrr5V2SavN7AQAATVNiYmLAZGFzl5eXV+uxTTYQS/KvB65L/bfOPZ/aTqvXjA0PD7/gP+NSFhYWFuwWAABAHYWFhRkqu1xIrmuyU36xsbFyuVwBx1wul+x2u2JiYmQ2m89bb9Wqlex2u8rKynT69OmAmiR/3el0nnNuzfsCAADAOJps+ktLS9O+ffsCjjkcDnXv3l02m02XXXaZcnJy/LWSkhL98MMP6tatm5KTk+Xz+bR///6Ac6OiopSYmKi0tDR9++23qq6uPue9AQAAYCxNNhCPHDlSO3bs0CeffCK3261169bp+++/15AhQyRJmZmZWr16tfLz81VWVqaFCxcqOTlZ6enpstvtGjx4sJ599lkVFxfr6NGjeuGFFzR8+HCFhoaqb9++ioiI0LJly1RRUaGvv/5a69atU2ZmZpCvGgAAAI0tqGuI09PTJck/U7t161ZJZ2ZrO3furIULF2r+/PkqKChQUlKSVqxYoTZt2kiSRo0apePHj+vOO+/UqVOnlJGRoaVLl/rf+/HHH9ecOXM0YMAAWSwW3Xzzzf6Hf1itVi1fvlxz5szRypUr1bp1a2VlZalfv36NePUAAABoCky+utx9ZnAOh0PS/wV6o8jNzdXtt9+uovQb5YloFex2AABALVjKTqit412tWbNGycnJwW6n0VxIXmuySyYAAACAxkAgBgAAgKERiAEAAGBoBGIAAAAYGoEYAAAAhkYgBgAAgKERiAEAAGBoBGIAAAAYGoEYAAAAhkYgBgAAgKERiAEAAGBoocFuAJee0IqTwW4BAADUEv/f/m0EYlwwe97nwW4BAACg3hCIccGKk65WdYvoYLcBAABqIbTiJJNZv4FAjAtW3SJanohWwW4DAACgXnBTHQAAAAyNQAwAAABDIxADAADA0AjEAAAAMDQCMQAAAAyNQAwAAABDIxADAADA0AjEAAAAMDQCMQAAAAyNQAwAAABDIxADAADA0AjEAAAAMDQCMQAAAAyNQAwAAABDIxADAADA0AjEAAAAMDQCMQAAAAyNQAwAAABDIxADAADA0AjEAAAAMDQCMQAAAAyNQAwAAABDIxADAADA0AjEAAAAMDQCMQAAAAyNQAwAAABDIxADAADA0AjEAAAAMDQCMQAAAAyNQAwAAABDIxADAADA0AjEAAAAMLQmHYi7dOmitLQ0paen+7+eeOIJSdLOnTs1fPhw9ejRQzfddJPefvvtgHNXr16twYMHq0ePHsrMzNS+ffv8NbfbrdmzZ+uaa65RRkaGJk+eLKfT2ajXBgAAgKYhNNgN/Jb33ntPCQkJAceKioo0ceJEzZgxQ7fccot2796tCRMmKDExUenp6dq2bZuWLFmiv/zlL+rSpYtWr16t8ePH64MPPlB4eLgWL16snJwcZWdnq0WLFpo1a5b+9Kc/afny5UG6SgAAAARLkw/E57Np0yZ17NhRw4cPlyT17t1b1157rdauXav09HRlZ2dr2LBh6t69uyTp3nvv1erVq/Xxxx9r8ODBWrdunRYsWKB27dpJkqZOnaqbbrpJx44dU1xcXK168Pl8Ki8vb5gLbKIqKyuD3QIAAKijyspKQ2UXn88nk8lUq7FNPhAvWrRIe/bsUVlZmW644QY98sgjysnJUUpKSsC4lJQUbdmyRZKUk5OjG2+80V8zm81KTk6Ww+FQcnKySktLlZqa6q936tRJYWFhysnJqXUg9ng8ys3NrYcrvHQcOnQo2C0AAIA6OnjwoLxeb7DbaFRWq7VW45p0IP73f/939e7dWwsWLNDhw4c1depUPfbYY3K5XOcE15iYGP86YJfLpejo6IB6dHS0nE6nXC6XJCkqKiqgHhUVdUHriC0Wi5KSkupwVZcus7lJLzkHAAC/IjExUV26dAl2G40mLy+v1mObdCDOzs72/9ypUydNmzZNEyZMUM+ePX/zXJ/Pd1H132IymRQeHn5R73GpCQsLC3YLAACgjsLCwgyVXWq7XEJq4rtM/FxCQoJOnz4ts9nsn+mt4XQ6ZbfbJUmxsbHn1F0ul+x2u3/Mz+snT55Uq1atGqp1AAAANFFNNhB/8803+u///u+AY/n5+bJarerbt2/ANmqStG/fPv9NdGlpacrJyfHXTp8+rW+++Ubdu3dX+/btFR0dHVD/7rvvVFVVpbS0tAa8IgAAADRFTTYQt2rVStnZ2Vq5cqWqqqp08OBBPffcc7rtttv0hz/8QQUFBVq7dq3cbrc+/fRTffrppxo5cqQkKTMzUxs3btTevXtVUVGhZcuWyWq1ql+/fgoJCdHIkSO1fPlyFRYWyul06plnntHAgQPVunXrIF81AAAAGluTXUMcFxenlStXatGiRf5AO3ToUGVlZclms2nFihWaN2+eHnvsMcXHx+vpp59W165dJUnXXHON/uu//ktTp07ViRMnlJ6erpUrV/rXwE6ePFmnTp3SH/7wB1VXV6t///6aO3duEK/20hJacTLYLQAAgFri/9u/zeS72LvLDMjhcEiS0tPTg9xJ4yosLNTQYcNU5XYHuxUAAHABrDabNqxf738GgxFcSF5rsjPEaHratWunDevXn3NDIoBL38GDBzVz5kzNmzdPiYmJwW4HQD2LiYkxVBi+UARiXJB27drxHxTQjCUmJio5OTnYbQBAo2qyN9UBAAAAjYFADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADI1ADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADI1ADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADI1ADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADI1ADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADI1ADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADI1ADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADI1ADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADI1ADAAAAEMLDXYDAJquI0eOqLS0NNhtoBEcPHgw4Duav8jISCUkJAS7DaBJIBADOC+n06lbb71VXq832K2gEc2cOTPYLaCRhISE6IMPPlBsbGywWwGCjkAM4LxiY2O1ceNGZoiBZioyMpIwDPx/BGIAv4h/TgUAGAE31QEAAMDQCMQAAAAwNAIxAAAADI1ADAAAAEMjEAMAAMDQCMQAAAAwNAIxAAAADM2wgbigoEDjxo1TRkaG+vfvr6effponcgEAABiQYR/M8eCDDyo1NVVbt27ViRMndP/996t169YaM2ZMsFsDAABAIzLkDLHD4dD+/fs1bdo0RUZGqmPHjho9erSys7OD3RoAAAAamSFniHNychQfH6/o6Gj/sdTUVB08eFBlZWWKiIj4zffw+XwqLy9vyDYBAABQRz6fTyaTqVZjDRmIXS6XoqKiAo7VhGOn01mrQOzxeJSbm9sg/QEAAODiWa3WWo0zZCCWzvyt4WJYLBYlJSXVUzcAAACoT3l5ebUea8hAbLfb5XK5Ao65XC6ZTCbZ7fZavYfJZFJ4eHgDdAcAAICLVdvlEpJBb6pLS0tTYWGhiouL/cccDoeSkpLUsmXLIHYGAACAxmbIQJySkqL09HQtWrRIZWVlys/P16pVq5SZmRns1gAAANDITL6LXUx7iTp69KhmzZqlL7/8UhERERo1apQeeOCBWk2vf/XVV/L5fLVeqA0AAIDGVVVVJZPJpB49evzmWMMG4ouxZ88e+Xw+WSyWYLcCAACA8/B4PDKZTLr88st/cyyBGAAAAIZmyDXEAAAAQA0CMQAAAAyNQAwAAABDIxADAADA0AjEAAAAMDQCMQAAAAyNQAwAAABDIxADAADA0AjEAAAAMDQCMQAAAAzt/wEQNTGSwfMPUAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.boxplot(y='charges', data=data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 181,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "dwvatnkpcQ4n",
        "outputId": "a9766689-25cd-4b5e-cc79-993e22a7c5ae"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: ylabel='smoker'>"
            ]
          },
          "execution_count": 181,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.boxplot(y='smoker', data=data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 182,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "zmSRT9MBcQ_U",
        "outputId": "5687b9ab-31b4-4285-b5da-72da44c90a81"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_e55b6_row9_col1, #T_e55b6_row15_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_e55b6\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_e55b6_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_e55b6_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_e55b6_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_e55b6_row0_col1\" class=\"data row0 col1\" >123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_e55b6_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_e55b6_row1_col1\" class=\"data row1 col1\" >charges</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_e55b6_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_e55b6_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_e55b6_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_e55b6_row3_col1\" class=\"data row3 col1\" >(1191, 7)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_e55b6_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_e55b6_row4_col1\" class=\"data row4 col1\" >(1191, 10)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_e55b6_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_e55b6_row5_col1\" class=\"data row5 col1\" >(952, 10)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_e55b6_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_e55b6_row6_col1\" class=\"data row6 col1\" >(239, 10)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_e55b6_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
              "      <td id=\"T_e55b6_row7_col1\" class=\"data row7 col1\" >4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_e55b6_row8_col0\" class=\"data row8 col0\" >Categorical features</td>\n",
              "      <td id=\"T_e55b6_row8_col1\" class=\"data row8 col1\" >2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_e55b6_row9_col0\" class=\"data row9 col0\" >Preprocess</td>\n",
              "      <td id=\"T_e55b6_row9_col1\" class=\"data row9 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_e55b6_row10_col0\" class=\"data row10 col0\" >Imputation type</td>\n",
              "      <td id=\"T_e55b6_row10_col1\" class=\"data row10 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_e55b6_row11_col0\" class=\"data row11 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_e55b6_row11_col1\" class=\"data row11 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_e55b6_row12_col0\" class=\"data row12 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_e55b6_row12_col1\" class=\"data row12 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_e55b6_row13_col0\" class=\"data row13 col0\" >Maximum one-hot encoding</td>\n",
              "      <td id=\"T_e55b6_row13_col1\" class=\"data row13 col1\" >25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_e55b6_row14_col0\" class=\"data row14 col0\" >Encoding method</td>\n",
              "      <td id=\"T_e55b6_row14_col1\" class=\"data row14 col1\" >None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_e55b6_row15_col0\" class=\"data row15 col0\" >Normalize</td>\n",
              "      <td id=\"T_e55b6_row15_col1\" class=\"data row15 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_e55b6_row16_col0\" class=\"data row16 col0\" >Normalize method</td>\n",
              "      <td id=\"T_e55b6_row16_col1\" class=\"data row16 col1\" >minmax</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_e55b6_row17_col0\" class=\"data row17 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_e55b6_row17_col1\" class=\"data row17 col1\" >KFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_e55b6_row18_col0\" class=\"data row18 col0\" >Fold Number</td>\n",
              "      <td id=\"T_e55b6_row18_col1\" class=\"data row18 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
              "      <td id=\"T_e55b6_row19_col0\" class=\"data row19 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_e55b6_row19_col1\" class=\"data row19 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
              "      <td id=\"T_e55b6_row20_col0\" class=\"data row20 col0\" >Use GPU</td>\n",
              "      <td id=\"T_e55b6_row20_col1\" class=\"data row20 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
              "      <td id=\"T_e55b6_row21_col0\" class=\"data row21 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_e55b6_row21_col1\" class=\"data row21 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
              "      <td id=\"T_e55b6_row22_col0\" class=\"data row22 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_e55b6_row22_col1\" class=\"data row22 col1\" >reg-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e55b6_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
              "      <td id=\"T_e55b6_row23_col0\" class=\"data row23 col0\" >USI</td>\n",
              "      <td id=\"T_e55b6_row23_col1\" class=\"data row23 col1\" >81cc</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x79c2ee0ebd90>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# configuration de  l'environnement PyCaret pour la régression\n",
        "from pycaret.regression import*\n",
        "# minmax :methode de normalisation utilisée\n",
        "s = setup(data,target='charges',train_size=0.8,session_id=123,normalize=True,normalize_method='minmax')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 183,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "eCaXcX5KcRHY",
        "outputId": "8ebcf213-444d-49b3-92df-703ed52243a1"
      },
      "outputs": [
        {
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              "\n",
              "<div id=\"df-58dba662-c183-40a0-887b-b06404bc6cd5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-58dba662-c183-40a0-887b-b06404bc6cd5')\"\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-58dba662-c183-40a0-887b-b06404bc6cd5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "           age  sex       bmi  children  smoker  region_northeast  \\\n",
              "825   1.000000  0.0  0.515265       0.4     0.0               1.0   \n",
              "194   0.000000  1.0  0.599870       0.0     0.0               0.0   \n",
              "1058  0.130435  0.0  0.764209       0.0     0.0               0.0   \n",
              "821   0.173913  1.0  0.055538       0.0     0.0               0.0   \n",
              "994   0.195652  0.0  0.132673       0.6     1.0               0.0   \n",
              "...        ...  ...       ...       ...     ...               ...   \n",
              "112   0.413043  1.0  0.481975       0.0     0.0               0.0   \n",
              "1286  0.217391  0.0  0.043196       0.0     0.0               1.0   \n",
              "124   0.630435  0.0  0.583144       0.6     0.0               0.0   \n",
              "1171  0.543478  0.0  0.348815       0.4     1.0               0.0   \n",
              "1260  0.304348  0.0  0.148100       0.0     0.0               1.0   \n",
              "\n",
              "      region_southeast  region_northwest  region_southwest  \n",
              "825                0.0               0.0               0.0  \n",
              "194                1.0               0.0               0.0  \n",
              "1058               1.0               0.0               0.0  \n",
              "821                0.0               1.0               0.0  \n",
              "994                0.0               1.0               0.0  \n",
              "...                ...               ...               ...  \n",
              "112                0.0               0.0               1.0  \n",
              "1286               0.0               0.0               0.0  \n",
              "124                0.0               1.0               0.0  \n",
              "1171               0.0               0.0               1.0  \n",
              "1260               0.0               0.0               0.0  \n",
              "\n",
              "[952 rows x 9 columns]"
            ]
          },
          "execution_count": 183,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "get_config('X_train_transformed')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 184,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 709,
          "referenced_widgets": [
            "eabb37aa2c9e496d8dea0223004e8433",
            "3b412bab52774f4b8042a40b6d82c26b",
            "e3c4e503194b487ab89eebf9c7285213",
            "d0065db588724c628b0da4ab4b33b280",
            "490504390d334af6bb42fbafd0c73473",
            "32d4e7b6a3524223b1c50f47f80c9ef2",
            "57195257f2da4645be4170751ae02226",
            "292776b320e14d47b54649e7e9624fb3",
            "6e41f043d3a04854891539aaf55ad008",
            "9e6006f7244441ab8f1616010608f363",
            "8fb1891685ab41b4b824eb66354d46d4"
          ]
        },
        "id": "3b7lt0-kcRP6",
        "outputId": "d2477bc0-7a7b-4a4b-eb23-14322bae63e1"
      },
      "outputs": [
        {
          "data": {
            "text/html": [],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_3d3ae th {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_3d3ae_row0_col0, #T_3d3ae_row0_col1, #T_3d3ae_row0_col5, #T_3d3ae_row0_col6, #T_3d3ae_row1_col0, #T_3d3ae_row1_col1, #T_3d3ae_row1_col2, #T_3d3ae_row1_col3, #T_3d3ae_row1_col4, #T_3d3ae_row1_col5, #T_3d3ae_row1_col6, #T_3d3ae_row2_col0, #T_3d3ae_row2_col1, #T_3d3ae_row2_col2, #T_3d3ae_row2_col3, #T_3d3ae_row2_col4, #T_3d3ae_row2_col5, #T_3d3ae_row2_col6, #T_3d3ae_row3_col0, #T_3d3ae_row3_col1, #T_3d3ae_row3_col2, #T_3d3ae_row3_col3, #T_3d3ae_row3_col4, #T_3d3ae_row3_col5, #T_3d3ae_row3_col6, #T_3d3ae_row4_col0, #T_3d3ae_row4_col1, #T_3d3ae_row4_col2, #T_3d3ae_row4_col3, #T_3d3ae_row4_col4, #T_3d3ae_row4_col5, #T_3d3ae_row4_col6, #T_3d3ae_row5_col0, #T_3d3ae_row5_col1, #T_3d3ae_row5_col2, #T_3d3ae_row5_col3, #T_3d3ae_row5_col4, #T_3d3ae_row5_col5, #T_3d3ae_row5_col6, #T_3d3ae_row6_col0, #T_3d3ae_row6_col1, #T_3d3ae_row6_col2, #T_3d3ae_row6_col3, #T_3d3ae_row6_col4, #T_3d3ae_row6_col5, #T_3d3ae_row6_col6, #T_3d3ae_row7_col0, #T_3d3ae_row7_col2, #T_3d3ae_row7_col3, #T_3d3ae_row7_col4, #T_3d3ae_row8_col0, #T_3d3ae_row8_col1, #T_3d3ae_row8_col2, #T_3d3ae_row8_col3, #T_3d3ae_row8_col4, #T_3d3ae_row8_col5, #T_3d3ae_row8_col6, #T_3d3ae_row9_col0, #T_3d3ae_row9_col1, #T_3d3ae_row9_col2, #T_3d3ae_row9_col3, #T_3d3ae_row9_col4, #T_3d3ae_row9_col5, #T_3d3ae_row9_col6, #T_3d3ae_row10_col0, #T_3d3ae_row10_col1, #T_3d3ae_row10_col2, #T_3d3ae_row10_col3, #T_3d3ae_row10_col4, #T_3d3ae_row10_col5, #T_3d3ae_row10_col6, #T_3d3ae_row11_col0, #T_3d3ae_row11_col1, #T_3d3ae_row11_col2, #T_3d3ae_row11_col3, #T_3d3ae_row11_col4, #T_3d3ae_row11_col5, #T_3d3ae_row11_col6, #T_3d3ae_row12_col0, #T_3d3ae_row12_col1, #T_3d3ae_row12_col2, #T_3d3ae_row12_col3, #T_3d3ae_row12_col4, #T_3d3ae_row12_col5, #T_3d3ae_row12_col6, #T_3d3ae_row13_col0, #T_3d3ae_row13_col1, #T_3d3ae_row13_col2, #T_3d3ae_row13_col3, #T_3d3ae_row13_col4, #T_3d3ae_row13_col5, #T_3d3ae_row13_col6, #T_3d3ae_row14_col0, #T_3d3ae_row14_col1, #T_3d3ae_row14_col2, #T_3d3ae_row14_col3, #T_3d3ae_row14_col4, #T_3d3ae_row14_col5, #T_3d3ae_row14_col6, #T_3d3ae_row15_col0, #T_3d3ae_row15_col1, #T_3d3ae_row15_col2, #T_3d3ae_row15_col3, #T_3d3ae_row15_col4, #T_3d3ae_row15_col5, #T_3d3ae_row15_col6, #T_3d3ae_row16_col0, #T_3d3ae_row16_col1, #T_3d3ae_row16_col2, #T_3d3ae_row16_col3, #T_3d3ae_row16_col4, #T_3d3ae_row16_col5, #T_3d3ae_row16_col6, #T_3d3ae_row17_col0, #T_3d3ae_row17_col1, #T_3d3ae_row17_col2, #T_3d3ae_row17_col3, #T_3d3ae_row17_col4, #T_3d3ae_row17_col5, #T_3d3ae_row17_col6, #T_3d3ae_row18_col0, #T_3d3ae_row18_col1, #T_3d3ae_row18_col2, #T_3d3ae_row18_col3, #T_3d3ae_row18_col4, #T_3d3ae_row18_col5, #T_3d3ae_row18_col6 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_3d3ae_row0_col2, #T_3d3ae_row0_col3, #T_3d3ae_row0_col4, #T_3d3ae_row7_col1, #T_3d3ae_row7_col5, #T_3d3ae_row7_col6 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_3d3ae_row0_col7, #T_3d3ae_row1_col7, #T_3d3ae_row2_col7, #T_3d3ae_row4_col7, #T_3d3ae_row5_col7, #T_3d3ae_row6_col7, #T_3d3ae_row7_col7, #T_3d3ae_row8_col7, #T_3d3ae_row9_col7, #T_3d3ae_row10_col7, #T_3d3ae_row11_col7, #T_3d3ae_row12_col7, #T_3d3ae_row13_col7, #T_3d3ae_row14_col7, #T_3d3ae_row15_col7, #T_3d3ae_row16_col7, #T_3d3ae_row17_col7, #T_3d3ae_row18_col7 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_3d3ae_row3_col7 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_3d3ae\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_3d3ae_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_3d3ae_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
              "      <th id=\"T_3d3ae_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
              "      <th id=\"T_3d3ae_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
              "      <th id=\"T_3d3ae_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
              "      <th id=\"T_3d3ae_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
              "      <th id=\"T_3d3ae_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
              "      <th id=\"T_3d3ae_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_3d3ae_level0_row0\" class=\"row_heading level0 row0\" >gbr</th>\n",
              "      <td id=\"T_3d3ae_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Regressor</td>\n",
              "      <td id=\"T_3d3ae_row0_col1\" class=\"data row0 col1\" >2408.4907</td>\n",
              "      <td id=\"T_3d3ae_row0_col2\" class=\"data row0 col2\" >19706959.9398</td>\n",
              "      <td id=\"T_3d3ae_row0_col3\" class=\"data row0 col3\" >4396.7196</td>\n",
              "      <td id=\"T_3d3ae_row0_col4\" class=\"data row0 col4\" >0.5936</td>\n",
              "      <td id=\"T_3d3ae_row0_col5\" class=\"data row0 col5\" >0.4530</td>\n",
              "      <td id=\"T_3d3ae_row0_col6\" class=\"data row0 col6\" >0.3209</td>\n",
              "      <td id=\"T_3d3ae_row0_col7\" class=\"data row0 col7\" >0.3720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3d3ae_level0_row1\" class=\"row_heading level0 row1\" >lr</th>\n",
              "      <td id=\"T_3d3ae_row1_col0\" class=\"data row1 col0\" >Linear Regression</td>\n",
              "      <td id=\"T_3d3ae_row1_col1\" class=\"data row1 col1\" >2474.4497</td>\n",
              "      <td id=\"T_3d3ae_row1_col2\" class=\"data row1 col2\" >20337491.7305</td>\n",
              "      <td id=\"T_3d3ae_row1_col3\" class=\"data row1 col3\" >4459.7437</td>\n",
              "      <td id=\"T_3d3ae_row1_col4\" class=\"data row1 col4\" >0.5872</td>\n",
              "      <td id=\"T_3d3ae_row1_col5\" class=\"data row1 col5\" >0.4312</td>\n",
              "      <td id=\"T_3d3ae_row1_col6\" class=\"data row1 col6\" >0.3134</td>\n",
              "      <td id=\"T_3d3ae_row1_col7\" class=\"data row1 col7\" >0.0940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3d3ae_level0_row2\" class=\"row_heading level0 row2\" >ridge</th>\n",
              "      <td id=\"T_3d3ae_row2_col0\" class=\"data row2 col0\" >Ridge Regression</td>\n",
              "      <td id=\"T_3d3ae_row2_col1\" class=\"data row2 col1\" >2481.6426</td>\n",
              "      <td id=\"T_3d3ae_row2_col2\" class=\"data row2 col2\" >20356593.7261</td>\n",
              "      <td id=\"T_3d3ae_row2_col3\" class=\"data row2 col3\" >4461.8272</td>\n",
              "      <td id=\"T_3d3ae_row2_col4\" class=\"data row2 col4\" >0.5871</td>\n",
              "      <td id=\"T_3d3ae_row2_col5\" class=\"data row2 col5\" >0.4345</td>\n",
              "      <td id=\"T_3d3ae_row2_col6\" class=\"data row2 col6\" >0.3211</td>\n",
              "      <td id=\"T_3d3ae_row2_col7\" class=\"data row2 col7\" >0.1560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3d3ae_level0_row3\" class=\"row_heading level0 row3\" >br</th>\n",
              "      <td id=\"T_3d3ae_row3_col0\" class=\"data row3 col0\" >Bayesian Ridge</td>\n",
              "      <td id=\"T_3d3ae_row3_col1\" class=\"data row3 col1\" >2477.3704</td>\n",
              "      <td id=\"T_3d3ae_row3_col2\" class=\"data row3 col2\" >20354777.6915</td>\n",
              "      <td id=\"T_3d3ae_row3_col3\" class=\"data row3 col3\" >4461.4588</td>\n",
              "      <td id=\"T_3d3ae_row3_col4\" class=\"data row3 col4\" >0.5870</td>\n",
              "      <td id=\"T_3d3ae_row3_col5\" class=\"data row3 col5\" >0.4324</td>\n",
              "      <td id=\"T_3d3ae_row3_col6\" class=\"data row3 col6\" >0.3165</td>\n",
              "      <td id=\"T_3d3ae_row3_col7\" class=\"data row3 col7\" >0.0890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3d3ae_level0_row4\" class=\"row_heading level0 row4\" >lasso</th>\n",
              "      <td id=\"T_3d3ae_row4_col0\" class=\"data row4 col0\" >Lasso Regression</td>\n",
              "      <td id=\"T_3d3ae_row4_col1\" class=\"data row4 col1\" >2474.2266</td>\n",
              "      <td id=\"T_3d3ae_row4_col2\" class=\"data row4 col2\" >20354221.2265</td>\n",
              "      <td id=\"T_3d3ae_row4_col3\" class=\"data row4 col3\" >4461.2480</td>\n",
              "      <td id=\"T_3d3ae_row4_col4\" class=\"data row4 col4\" >0.5869</td>\n",
              "      <td id=\"T_3d3ae_row4_col5\" class=\"data row4 col5\" >0.4310</td>\n",
              "      <td id=\"T_3d3ae_row4_col6\" class=\"data row4 col6\" >0.3133</td>\n",
              "      <td id=\"T_3d3ae_row4_col7\" class=\"data row4 col7\" >0.1050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3d3ae_level0_row5\" class=\"row_heading level0 row5\" >lar</th>\n",
              "      <td id=\"T_3d3ae_row5_col0\" class=\"data row5 col0\" >Least Angle Regression</td>\n",
              "      <td id=\"T_3d3ae_row5_col1\" class=\"data row5 col1\" >2473.9016</td>\n",
              "      <td id=\"T_3d3ae_row5_col2\" class=\"data row5 col2\" >20353954.6798</td>\n",
              "      <td id=\"T_3d3ae_row5_col3\" class=\"data row5 col3\" >4461.2023</td>\n",
              "      <td id=\"T_3d3ae_row5_col4\" class=\"data row5 col4\" >0.5869</td>\n",
              "      <td id=\"T_3d3ae_row5_col5\" class=\"data row5 col5\" >0.4307</td>\n",
              "      <td id=\"T_3d3ae_row5_col6\" class=\"data row5 col6\" >0.3125</td>\n",
              "      <td id=\"T_3d3ae_row5_col7\" class=\"data row5 col7\" >0.0950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3d3ae_level0_row6\" class=\"row_heading level0 row6\" >llar</th>\n",
              "      <td id=\"T_3d3ae_row6_col0\" class=\"data row6 col0\" >Lasso Least Angle Regression</td>\n",
              "      <td id=\"T_3d3ae_row6_col1\" class=\"data row6 col1\" >2474.2316</td>\n",
              "      <td id=\"T_3d3ae_row6_col2\" class=\"data row6 col2\" >20354279.0035</td>\n",
              "      <td id=\"T_3d3ae_row6_col3\" class=\"data row6 col3\" >4461.2545</td>\n",
              "      <td id=\"T_3d3ae_row6_col4\" class=\"data row6 col4\" >0.5869</td>\n",
              "      <td id=\"T_3d3ae_row6_col5\" class=\"data row6 col5\" >0.4310</td>\n",
              "      <td id=\"T_3d3ae_row6_col6\" class=\"data row6 col6\" >0.3133</td>\n",
              "      <td id=\"T_3d3ae_row6_col7\" class=\"data row6 col7\" >0.1250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3d3ae_level0_row7\" class=\"row_heading level0 row7\" >huber</th>\n",
              "      <td id=\"T_3d3ae_row7_col0\" class=\"data row7 col0\" >Huber Regressor</td>\n",
              "      <td id=\"T_3d3ae_row7_col1\" class=\"data row7 col1\" >1833.1541</td>\n",
              "      <td id=\"T_3d3ae_row7_col2\" class=\"data row7 col2\" >21724249.6893</td>\n",
              "      <td id=\"T_3d3ae_row7_col3\" class=\"data row7 col3\" >4587.7841</td>\n",
              "      <td id=\"T_3d3ae_row7_col4\" class=\"data row7 col4\" >0.5635</td>\n",
              "      <td id=\"T_3d3ae_row7_col5\" class=\"data row7 col5\" >0.4272</td>\n",
              "      <td id=\"T_3d3ae_row7_col6\" class=\"data row7 col6\" >0.1467</td>\n",
              "      <td id=\"T_3d3ae_row7_col7\" class=\"data row7 col7\" >0.1100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3d3ae_level0_row8\" class=\"row_heading level0 row8\" >par</th>\n",
              "      <td id=\"T_3d3ae_row8_col0\" class=\"data row8 col0\" >Passive Aggressive Regressor</td>\n",
              "      <td id=\"T_3d3ae_row8_col1\" class=\"data row8 col1\" >1835.6646</td>\n",
              "      <td id=\"T_3d3ae_row8_col2\" class=\"data row8 col2\" >21916731.9882</td>\n",
              "      <td id=\"T_3d3ae_row8_col3\" class=\"data row8 col3\" >4607.9643</td>\n",
              "      <td id=\"T_3d3ae_row8_col4\" class=\"data row8 col4\" >0.5597</td>\n",
              "      <td id=\"T_3d3ae_row8_col5\" class=\"data row8 col5\" >0.4369</td>\n",
              "      <td id=\"T_3d3ae_row8_col6\" class=\"data row8 col6\" >0.1476</td>\n",
              "      <td id=\"T_3d3ae_row8_col7\" class=\"data row8 col7\" >0.1070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3d3ae_level0_row9\" class=\"row_heading level0 row9\" >rf</th>\n",
              "      <td id=\"T_3d3ae_row9_col0\" class=\"data row9 col0\" >Random Forest Regressor</td>\n",
              "      <td id=\"T_3d3ae_row9_col1\" class=\"data row9 col1\" >2555.1672</td>\n",
              "      <td id=\"T_3d3ae_row9_col2\" class=\"data row9 col2\" >21625680.6768</td>\n",
              "      <td id=\"T_3d3ae_row9_col3\" class=\"data row9 col3\" >4614.0673</td>\n",
              "      <td id=\"T_3d3ae_row9_col4\" class=\"data row9 col4\" >0.5549</td>\n",
              "      <td id=\"T_3d3ae_row9_col5\" class=\"data row9 col5\" >0.4828</td>\n",
              "      <td id=\"T_3d3ae_row9_col6\" class=\"data row9 col6\" >0.3452</td>\n",
              "      <td id=\"T_3d3ae_row9_col7\" class=\"data row9 col7\" >0.4000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3d3ae_level0_row10\" class=\"row_heading level0 row10\" >lightgbm</th>\n",
              "      <td id=\"T_3d3ae_row10_col0\" class=\"data row10 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_3d3ae_row10_col1\" class=\"data row10 col1\" >2815.6165</td>\n",
              "      <td id=\"T_3d3ae_row10_col2\" class=\"data row10 col2\" >22744049.5063</td>\n",
              "      <td id=\"T_3d3ae_row10_col3\" class=\"data row10 col3\" >4733.8289</td>\n",
              "      <td id=\"T_3d3ae_row10_col4\" class=\"data row10 col4\" >0.5309</td>\n",
              "      <td id=\"T_3d3ae_row10_col5\" class=\"data row10 col5\" >0.5958</td>\n",
              "      <td id=\"T_3d3ae_row10_col6\" class=\"data row10 col6\" >0.3984</td>\n",
              "      <td id=\"T_3d3ae_row10_col7\" class=\"data row10 col7\" >0.6930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3d3ae_level0_row11\" class=\"row_heading level0 row11\" >ada</th>\n",
              "      <td id=\"T_3d3ae_row11_col0\" class=\"data row11 col0\" >AdaBoost Regressor</td>\n",
              "      <td id=\"T_3d3ae_row11_col1\" class=\"data row11 col1\" >3586.2694</td>\n",
              "      <td id=\"T_3d3ae_row11_col2\" class=\"data row11 col2\" >23273907.4953</td>\n",
              "      <td id=\"T_3d3ae_row11_col3\" class=\"data row11 col3\" >4782.2269</td>\n",
              "      <td id=\"T_3d3ae_row11_col4\" class=\"data row11 col4\" >0.5186</td>\n",
              "      <td id=\"T_3d3ae_row11_col5\" class=\"data row11 col5\" >0.6103</td>\n",
              "      <td id=\"T_3d3ae_row11_col6\" class=\"data row11 col6\" >0.6702</td>\n",
              "      <td id=\"T_3d3ae_row11_col7\" class=\"data row11 col7\" >0.1100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3d3ae_level0_row12\" class=\"row_heading level0 row12\" >knn</th>\n",
              "      <td id=\"T_3d3ae_row12_col0\" class=\"data row12 col0\" >K Neighbors Regressor</td>\n",
              "      <td id=\"T_3d3ae_row12_col1\" class=\"data row12 col1\" >2844.9708</td>\n",
              "      <td id=\"T_3d3ae_row12_col2\" class=\"data row12 col2\" >24097670.9000</td>\n",
              "      <td id=\"T_3d3ae_row12_col3\" class=\"data row12 col3\" >4861.9527</td>\n",
              "      <td id=\"T_3d3ae_row12_col4\" class=\"data row12 col4\" >0.5083</td>\n",
              "      <td id=\"T_3d3ae_row12_col5\" class=\"data row12 col5\" >0.5090</td>\n",
              "      <td id=\"T_3d3ae_row12_col6\" class=\"data row12 col6\" >0.3665</td>\n",
              "      <td id=\"T_3d3ae_row12_col7\" class=\"data row12 col7\" >0.1270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3d3ae_level0_row13\" class=\"row_heading level0 row13\" >et</th>\n",
              "      <td id=\"T_3d3ae_row13_col0\" class=\"data row13 col0\" >Extra Trees Regressor</td>\n",
              "      <td id=\"T_3d3ae_row13_col1\" class=\"data row13 col1\" >2491.0009</td>\n",
              "      <td id=\"T_3d3ae_row13_col2\" class=\"data row13 col2\" >24442821.4301</td>\n",
              "      <td id=\"T_3d3ae_row13_col3\" class=\"data row13 col3\" >4899.5757</td>\n",
              "      <td id=\"T_3d3ae_row13_col4\" class=\"data row13 col4\" >0.5008</td>\n",
              "      <td id=\"T_3d3ae_row13_col5\" class=\"data row13 col5\" >0.4882</td>\n",
              "      <td id=\"T_3d3ae_row13_col6\" class=\"data row13 col6\" >0.3173</td>\n",
              "      <td id=\"T_3d3ae_row13_col7\" class=\"data row13 col7\" >0.3290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3d3ae_level0_row14\" class=\"row_heading level0 row14\" >xgboost</th>\n",
              "      <td id=\"T_3d3ae_row14_col0\" class=\"data row14 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_3d3ae_row14_col1\" class=\"data row14 col1\" >2966.1781</td>\n",
              "      <td id=\"T_3d3ae_row14_col2\" class=\"data row14 col2\" >27182171.2000</td>\n",
              "      <td id=\"T_3d3ae_row14_col3\" class=\"data row14 col3\" >5184.2656</td>\n",
              "      <td id=\"T_3d3ae_row14_col4\" class=\"data row14 col4\" >0.4313</td>\n",
              "      <td id=\"T_3d3ae_row14_col5\" class=\"data row14 col5\" >0.5816</td>\n",
              "      <td id=\"T_3d3ae_row14_col6\" class=\"data row14 col6\" >0.4255</td>\n",
              "      <td id=\"T_3d3ae_row14_col7\" class=\"data row14 col7\" >0.6260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3d3ae_level0_row15\" class=\"row_heading level0 row15\" >omp</th>\n",
              "      <td id=\"T_3d3ae_row15_col0\" class=\"data row15 col0\" >Orthogonal Matching Pursuit</td>\n",
              "      <td id=\"T_3d3ae_row15_col1\" class=\"data row15 col1\" >4414.0776</td>\n",
              "      <td id=\"T_3d3ae_row15_col2\" class=\"data row15 col2\" >33606445.8659</td>\n",
              "      <td id=\"T_3d3ae_row15_col3\" class=\"data row15 col3\" >5762.6754</td>\n",
              "      <td id=\"T_3d3ae_row15_col4\" class=\"data row15 col4\" >0.3186</td>\n",
              "      <td id=\"T_3d3ae_row15_col5\" class=\"data row15 col5\" >0.7547</td>\n",
              "      <td id=\"T_3d3ae_row15_col6\" class=\"data row15 col6\" >0.9265</td>\n",
              "      <td id=\"T_3d3ae_row15_col7\" class=\"data row15 col7\" >0.1090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3d3ae_level0_row16\" class=\"row_heading level0 row16\" >dt</th>\n",
              "      <td id=\"T_3d3ae_row16_col0\" class=\"data row16 col0\" >Decision Tree Regressor</td>\n",
              "      <td id=\"T_3d3ae_row16_col1\" class=\"data row16 col1\" >2904.0751</td>\n",
              "      <td id=\"T_3d3ae_row16_col2\" class=\"data row16 col2\" >39500706.4719</td>\n",
              "      <td id=\"T_3d3ae_row16_col3\" class=\"data row16 col3\" >6233.5161</td>\n",
              "      <td id=\"T_3d3ae_row16_col4\" class=\"data row16 col4\" >0.1809</td>\n",
              "      <td id=\"T_3d3ae_row16_col5\" class=\"data row16 col5\" >0.5602</td>\n",
              "      <td id=\"T_3d3ae_row16_col6\" class=\"data row16 col6\" >0.3952</td>\n",
              "      <td id=\"T_3d3ae_row16_col7\" class=\"data row16 col7\" >0.1590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3d3ae_level0_row17\" class=\"row_heading level0 row17\" >en</th>\n",
              "      <td id=\"T_3d3ae_row17_col0\" class=\"data row17 col0\" >Elastic Net</td>\n",
              "      <td id=\"T_3d3ae_row17_col1\" class=\"data row17 col1\" >4970.9924</td>\n",
              "      <td id=\"T_3d3ae_row17_col2\" class=\"data row17 col2\" >42311215.6665</td>\n",
              "      <td id=\"T_3d3ae_row17_col3\" class=\"data row17 col3\" >6460.3559</td>\n",
              "      <td id=\"T_3d3ae_row17_col4\" class=\"data row17 col4\" >0.1492</td>\n",
              "      <td id=\"T_3d3ae_row17_col5\" class=\"data row17 col5\" >0.7874</td>\n",
              "      <td id=\"T_3d3ae_row17_col6\" class=\"data row17 col6\" >1.0125</td>\n",
              "      <td id=\"T_3d3ae_row17_col7\" class=\"data row17 col7\" >0.1480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3d3ae_level0_row18\" class=\"row_heading level0 row18\" >dummy</th>\n",
              "      <td id=\"T_3d3ae_row18_col0\" class=\"data row18 col0\" >Dummy Regressor</td>\n",
              "      <td id=\"T_3d3ae_row18_col1\" class=\"data row18 col1\" >5530.7755</td>\n",
              "      <td id=\"T_3d3ae_row18_col2\" class=\"data row18 col2\" >50881793.2000</td>\n",
              "      <td id=\"T_3d3ae_row18_col3\" class=\"data row18 col3\" >7087.3234</td>\n",
              "      <td id=\"T_3d3ae_row18_col4\" class=\"data row18 col4\" >-0.0250</td>\n",
              "      <td id=\"T_3d3ae_row18_col5\" class=\"data row18 col5\" >0.8522</td>\n",
              "      <td id=\"T_3d3ae_row18_col6\" class=\"data row18 col6\" >1.1488</td>\n",
              "      <td id=\"T_3d3ae_row18_col7\" class=\"data row18 col7\" >0.1740</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
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              "<style>#sk-container-id-6 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-6 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-6 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-6 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-6 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-6 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-6 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-6 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-6 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-6 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(random_state=123)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor(random_state=123)</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "GradientBoostingRegressor(random_state=123)"
            ]
          },
          "execution_count": 184,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "compare_models()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 185,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457,
          "referenced_widgets": [
            "ed13942317be4ebfbf35d46c96810abc",
            "c15cb55c09df4dcf878d639fb1fea451",
            "772851bb8fcd411c93091e182b352adb",
            "cc9b43c550e74f02b14183ebe37b8030",
            "b22442b4e846481abbbb3718759eb1d0",
            "a1b72fda51c5437b89abcde2218cf343",
            "b5643195fdd041f6ad5b1be69457e4dc",
            "9701fe45d03240ed87a14fdf5433e6c4",
            "c83e8fdccacd4a78a99366298c53006c",
            "0d02c123214f4c7ea2219e3f9c562ca0",
            "3a4856d6bfd14c2a9ce277b42b2b963c"
          ]
        },
        "id": "EylQNEKrcRUX",
        "outputId": "0b5f041e-3b67-4254-c72d-6cd6dae44e2d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_772ee_row10_col0, #T_772ee_row10_col1, #T_772ee_row10_col2, #T_772ee_row10_col3, #T_772ee_row10_col4, #T_772ee_row10_col5 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_772ee\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_772ee_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
              "      <th id=\"T_772ee_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
              "      <th id=\"T_772ee_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
              "      <th id=\"T_772ee_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
              "      <th id=\"T_772ee_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
              "      <th id=\"T_772ee_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_772ee_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_772ee_row0_col0\" class=\"data row0 col0\" >2054.4400</td>\n",
              "      <td id=\"T_772ee_row0_col1\" class=\"data row0 col1\" >11914038.7441</td>\n",
              "      <td id=\"T_772ee_row0_col2\" class=\"data row0 col2\" >3451.6719</td>\n",
              "      <td id=\"T_772ee_row0_col3\" class=\"data row0 col3\" >0.7123</td>\n",
              "      <td id=\"T_772ee_row0_col4\" class=\"data row0 col4\" >0.3972</td>\n",
              "      <td id=\"T_772ee_row0_col5\" class=\"data row0 col5\" >0.3554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_772ee_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_772ee_row1_col0\" class=\"data row1 col0\" >1925.5019</td>\n",
              "      <td id=\"T_772ee_row1_col1\" class=\"data row1 col1\" >10513662.3529</td>\n",
              "      <td id=\"T_772ee_row1_col2\" class=\"data row1 col2\" >3242.4778</td>\n",
              "      <td id=\"T_772ee_row1_col3\" class=\"data row1 col3\" >0.6206</td>\n",
              "      <td id=\"T_772ee_row1_col4\" class=\"data row1 col4\" >0.4335</td>\n",
              "      <td id=\"T_772ee_row1_col5\" class=\"data row1 col5\" >0.3520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_772ee_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_772ee_row2_col0\" class=\"data row2 col0\" >2349.4091</td>\n",
              "      <td id=\"T_772ee_row2_col1\" class=\"data row2 col1\" >23001504.5398</td>\n",
              "      <td id=\"T_772ee_row2_col2\" class=\"data row2 col2\" >4795.9884</td>\n",
              "      <td id=\"T_772ee_row2_col3\" class=\"data row2 col3\" >0.6059</td>\n",
              "      <td id=\"T_772ee_row2_col4\" class=\"data row2 col4\" >0.4544</td>\n",
              "      <td id=\"T_772ee_row2_col5\" class=\"data row2 col5\" >0.2584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_772ee_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_772ee_row3_col0\" class=\"data row3 col0\" >2397.9176</td>\n",
              "      <td id=\"T_772ee_row3_col1\" class=\"data row3 col1\" >17738926.1974</td>\n",
              "      <td id=\"T_772ee_row3_col2\" class=\"data row3 col2\" >4211.7605</td>\n",
              "      <td id=\"T_772ee_row3_col3\" class=\"data row3 col3\" >0.6833</td>\n",
              "      <td id=\"T_772ee_row3_col4\" class=\"data row3 col4\" >0.3804</td>\n",
              "      <td id=\"T_772ee_row3_col5\" class=\"data row3 col5\" >0.2938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_772ee_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_772ee_row4_col0\" class=\"data row4 col0\" >2815.7279</td>\n",
              "      <td id=\"T_772ee_row4_col1\" class=\"data row4 col1\" >26749090.9929</td>\n",
              "      <td id=\"T_772ee_row4_col2\" class=\"data row4 col2\" >5171.9523</td>\n",
              "      <td id=\"T_772ee_row4_col3\" class=\"data row4 col3\" >0.5776</td>\n",
              "      <td id=\"T_772ee_row4_col4\" class=\"data row4 col4\" >0.5459</td>\n",
              "      <td id=\"T_772ee_row4_col5\" class=\"data row4 col5\" >0.4098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_772ee_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_772ee_row5_col0\" class=\"data row5 col0\" >2277.9101</td>\n",
              "      <td id=\"T_772ee_row5_col1\" class=\"data row5 col1\" >17104396.0848</td>\n",
              "      <td id=\"T_772ee_row5_col2\" class=\"data row5 col2\" >4135.7461</td>\n",
              "      <td id=\"T_772ee_row5_col3\" class=\"data row5 col3\" >0.6496</td>\n",
              "      <td id=\"T_772ee_row5_col4\" class=\"data row5 col4\" >0.4004</td>\n",
              "      <td id=\"T_772ee_row5_col5\" class=\"data row5 col5\" >0.2917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_772ee_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_772ee_row6_col0\" class=\"data row6 col0\" >2690.8479</td>\n",
              "      <td id=\"T_772ee_row6_col1\" class=\"data row6 col1\" >25410525.3758</td>\n",
              "      <td id=\"T_772ee_row6_col2\" class=\"data row6 col2\" >5040.8854</td>\n",
              "      <td id=\"T_772ee_row6_col3\" class=\"data row6 col3\" >0.2572</td>\n",
              "      <td id=\"T_772ee_row6_col4\" class=\"data row6 col4\" >0.5121</td>\n",
              "      <td id=\"T_772ee_row6_col5\" class=\"data row6 col5\" >0.2545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_772ee_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_772ee_row7_col0\" class=\"data row7 col0\" >2533.3342</td>\n",
              "      <td id=\"T_772ee_row7_col1\" class=\"data row7 col1\" >19529291.6958</td>\n",
              "      <td id=\"T_772ee_row7_col2\" class=\"data row7 col2\" >4419.1958</td>\n",
              "      <td id=\"T_772ee_row7_col3\" class=\"data row7 col3\" >0.6844</td>\n",
              "      <td id=\"T_772ee_row7_col4\" class=\"data row7 col4\" >0.4582</td>\n",
              "      <td id=\"T_772ee_row7_col5\" class=\"data row7 col5\" >0.4038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_772ee_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_772ee_row8_col0\" class=\"data row8 col0\" >2473.6004</td>\n",
              "      <td id=\"T_772ee_row8_col1\" class=\"data row8 col1\" >23105546.3596</td>\n",
              "      <td id=\"T_772ee_row8_col2\" class=\"data row8 col2\" >4806.8229</td>\n",
              "      <td id=\"T_772ee_row8_col3\" class=\"data row8 col3\" >0.4984</td>\n",
              "      <td id=\"T_772ee_row8_col4\" class=\"data row8 col4\" >0.4757</td>\n",
              "      <td id=\"T_772ee_row8_col5\" class=\"data row8 col5\" >0.2893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_772ee_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_772ee_row9_col0\" class=\"data row9 col0\" >2566.2182</td>\n",
              "      <td id=\"T_772ee_row9_col1\" class=\"data row9 col1\" >22002617.0551</td>\n",
              "      <td id=\"T_772ee_row9_col2\" class=\"data row9 col2\" >4690.6947</td>\n",
              "      <td id=\"T_772ee_row9_col3\" class=\"data row9 col3\" >0.6461</td>\n",
              "      <td id=\"T_772ee_row9_col4\" class=\"data row9 col4\" >0.4720</td>\n",
              "      <td id=\"T_772ee_row9_col5\" class=\"data row9 col5\" >0.3007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_772ee_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_772ee_row10_col0\" class=\"data row10 col0\" >2408.4907</td>\n",
              "      <td id=\"T_772ee_row10_col1\" class=\"data row10 col1\" >19706959.9398</td>\n",
              "      <td id=\"T_772ee_row10_col2\" class=\"data row10 col2\" >4396.7196</td>\n",
              "      <td id=\"T_772ee_row10_col3\" class=\"data row10 col3\" >0.5936</td>\n",
              "      <td id=\"T_772ee_row10_col4\" class=\"data row10 col4\" >0.4530</td>\n",
              "      <td id=\"T_772ee_row10_col5\" class=\"data row10 col5\" >0.3209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_772ee_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_772ee_row11_col0\" class=\"data row11 col0\" >258.7576</td>\n",
              "      <td id=\"T_772ee_row11_col1\" class=\"data row11 col1\" >5154814.5121</td>\n",
              "      <td id=\"T_772ee_row11_col2\" class=\"data row11 col2\" >613.0390</td>\n",
              "      <td id=\"T_772ee_row11_col3\" class=\"data row11 col3\" >0.1263</td>\n",
              "      <td id=\"T_772ee_row11_col4\" class=\"data row11 col4\" >0.0496</td>\n",
              "      <td id=\"T_772ee_row11_col5\" class=\"data row11 col5\" >0.0532</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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        "# crossvalidation\n",
        "# création du modèle de régression Gradient Boosting Regressor (GBR) en utilisant la fonction create_model() de PyCaret.\n",
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                  "text/plain": "<Figure size 2700x600 with 1 Axes>"
                },
                "metadata": {},
                "output_type": "display_data"
              }
            ]
          }
        },
        "e5ab7e504f6b4feb818b8ab620ebf0eb": {
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