Amazing-Python-Scripts

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  "metadata": {
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    "colab": {
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      "provenance": []
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    },
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    "kernelspec": {
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      "name": "python3",
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      "display_name": "Python 3"
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    },
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    "language_info": {
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      "name": "python"
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    }
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  },
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  "cells": [
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    {
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      "cell_type": "code",
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      "execution_count": 66,
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      "metadata": {
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        "id": "1-x2VVh6pDe5"
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      },
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      "outputs": [],
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      "source": [
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        "import pandas as pd\n",
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        "import matplotlib.pyplot as plt\n",
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        "import seaborn as sns\n",
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        "from sklearn.preprocessing import LabelEncoder\n",
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        "from sklearn.model_selection import train_test_split\n",
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        "from sklearn.linear_model import LinearRegression\n",
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        "from sklearn.preprocessing import StandardScaler\n",
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        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score"
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      ]
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    },
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    {
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      "cell_type": "code",
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      "source": [
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        "df=pd.read_csv(\"insurance.csv\")"
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      ],
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      "metadata": {
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        "id": "aP61JKMf5bFp"
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      },
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      "execution_count": 38,
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      "outputs": []
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    },
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    {
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      "cell_type": "code",
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      "source": [
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        "df.shape"
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      ],
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      "metadata": {
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        "colab": {
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          "base_uri": "https://localhost:8080/"
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        },
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        "id": "1x0KkISj5lrT",
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        "outputId": "d29ecb95-2078-4936-fc04-19f13e725de7"
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      },
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      "execution_count": 39,
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      "outputs": [
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        {
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          "output_type": "execute_result",
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          "data": {
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            "text/plain": [
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              "(1338, 7)"
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            ]
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          },
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          "metadata": {},
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          "execution_count": 39
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        }
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      ]
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    },
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    {
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      "cell_type": "code",
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      "source": [
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        "df.head()"
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      ],
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      "metadata": {
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        "colab": {
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          "base_uri": "https://localhost:8080/",
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          "height": 206
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        },
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        "id": "UG54_Ypv5jJ4",
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        "outputId": "f2d13f2b-9e62-4a34-ffe6-39acca810300"
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      },
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      "execution_count": 40,
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      "outputs": [
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        {
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          "output_type": "execute_result",
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          "data": {
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            "text/plain": [
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              "   age     sex     bmi  children smoker     region      charges\n",
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              "0   19  female  27.900         0    yes  southwest  16884.92400\n",
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              "1   18    male  33.770         1     no  southeast   1725.55230\n",
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              "2   28    male  33.000         3     no  southeast   4449.46200\n",
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              "3   33    male  22.705         0     no  northwest  21984.47061\n",
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              "4   32    male  28.880         0     no  northwest   3866.85520"
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            ],
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            "text/html": [
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              "\n",
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              "\n",
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              "  <div id=\"df-e1355ccc-e05d-4c7c-b0d9-b96dc95a0199\">\n",
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              "    <div class=\"colab-df-container\">\n",
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              "      <div>\n",
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              "</style>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "  <thead>\n",
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th></th>\n",
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              "      <th>age</th>\n",
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              "      <th>sex</th>\n",
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              "      <th>bmi</th>\n",
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              "      <th>children</th>\n",
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              "      <th>smoker</th>\n",
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              "      <th>region</th>\n",
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              "      <th>charges</th>\n",
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              "    </tr>\n",
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              "  </thead>\n",
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              "  <tbody>\n",
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              "    <tr>\n",
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              "      <th>0</th>\n",
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              "      <td>19</td>\n",
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              "      <td>female</td>\n",
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              "      <td>27.900</td>\n",
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              "      <td>0</td>\n",
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              "      <td>yes</td>\n",
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              "      <td>southwest</td>\n",
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              "      <td>16884.92400</td>\n",
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              "    </tr>\n",
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              "    <tr>\n",
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              "      <th>1</th>\n",
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              "      <td>18</td>\n",
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              "      <td>male</td>\n",
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              "      <td>33.770</td>\n",
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              "      <td>1</td>\n",
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              "      <td>no</td>\n",
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              "      <td>southeast</td>\n",
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              "      <td>1725.55230</td>\n",
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              "    </tr>\n",
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              "    <tr>\n",
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              "      <th>2</th>\n",
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              "      <td>28</td>\n",
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              "      <td>male</td>\n",
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              "      <td>33.000</td>\n",
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              "      <td>3</td>\n",
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              "      <td>no</td>\n",
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              "      <td>southeast</td>\n",
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              "      <td>4449.46200</td>\n",
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              "    </tr>\n",
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              "    <tr>\n",
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              "      <th>3</th>\n",
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              "      <td>33</td>\n",
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              "      <td>male</td>\n",
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              "      <td>22.705</td>\n",
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              "      <td>0</td>\n",
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              "      <td>no</td>\n",
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              "      <td>northwest</td>\n",
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              "      <td>21984.47061</td>\n",
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              "    </tr>\n",
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              "    <tr>\n",
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              "      <th>4</th>\n",
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              "      <td>32</td>\n",
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              "      <td>male</td>\n",
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              "      <td>28.880</td>\n",
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              "      <td>0</td>\n",
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              "      <td>no</td>\n",
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              "      <td>northwest</td>\n",
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              "      <td>3866.85520</td>\n",
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              "    </tr>\n",
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              "  </tbody>\n",
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              "</table>\n",
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              "</div>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e1355ccc-e05d-4c7c-b0d9-b96dc95a0199')\"\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "    <div id=\"df-62b682af-f176-4fee-90d0-22ddad3071aa\">\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-62b682af-f176-4fee-90d0-22ddad3071aa')\"\n",
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              "              title=\"Suggest charts.\"\n",
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              "\n",
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              "<style>\n",
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              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
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              "  }\n",
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              "\n",
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              "  .colab-df-quickchart:hover {\n",
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              "    background-color: #E2EBFA;\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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              "    fill: #174EA6;\n",
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              "  }\n",
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              "\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "    background-color: #3B4455;\n",
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              "    fill: #D2E3FC;\n",
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              "  }\n",
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              "\n",
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              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "    background-color: #434B5C;\n",
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              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    fill: #FFFFFF;\n",
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              "  }\n",
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              "</style>\n",
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              "\n",
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              "    <script>\n",
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              "      async function quickchart(key) {\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "            'suggestCharts', [key], {});\n",
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              "      }\n",
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              "    </script>\n",
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              "\n",
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              "      <script>\n",
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              "\n",
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              "function displayQuickchartButton(domScope) {\n",
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              "  let quickchartButtonEl =\n",
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              "    domScope.querySelector('#df-62b682af-f176-4fee-90d0-22ddad3071aa button.colab-df-quickchart');\n",
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              "  quickchartButtonEl.style.display =\n",
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              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "}\n",
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              "\n",
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              "        displayQuickchartButton(document);\n",
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              "      </script>\n",
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              "      <style>\n",
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              "    .colab-df-container {\n",
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              "      display:flex;\n",
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              "      flex-wrap:wrap;\n",
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              "      gap: 12px;\n",
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              "    }\n",
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              "\n",
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              "    .colab-df-convert {\n",
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              "      background-color: #E8F0FE;\n",
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              "      border: none;\n",
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              "      border-radius: 50%;\n",
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              "      cursor: pointer;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
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              "      padding: 0 0 0 0;\n",
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              "      width: 32px;\n",
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              "    }\n",
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              "\n",
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              "    .colab-df-convert:hover {\n",
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              "      background-color: #E2EBFA;\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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              "      fill: #174EA6;\n",
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              "    }\n",
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              "\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      background-color: #3B4455;\n",
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              "      fill: #D2E3FC;\n",
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              "    }\n",
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              "\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      background-color: #434B5C;\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
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              "\n",
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              "      <script>\n",
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              "        const buttonEl =\n",
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              "          document.querySelector('#df-e1355ccc-e05d-4c7c-b0d9-b96dc95a0199 button.colab-df-convert');\n",
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              "        buttonEl.style.display =\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "        async function convertToInteractive(key) {\n",
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              "          const element = document.querySelector('#df-e1355ccc-e05d-4c7c-b0d9-b96dc95a0199');\n",
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              "          const dataTable =\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "                                                     [key], {});\n",
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              "          if (!dataTable) return;\n",
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              "\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "          element.innerHTML = '';\n",
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              "          dataTable['output_type'] = 'display_data';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          const docLink = document.createElement('div');\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "          element.appendChild(docLink);\n",
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              "      </script>\n",
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              "    </div>\n",
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              "  </div>\n"
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            ]
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          },
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          "metadata": {},
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          "execution_count": 40
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        }
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      ]
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    },
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    {
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      "cell_type": "code",
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      "source": [
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        "df.describe()"
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      ],
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      "metadata": {
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        "colab": {
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          "base_uri": "https://localhost:8080/",
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          "height": 300
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        },
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        "id": "ZBEKJHvI51n9",
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        "outputId": "93735769-e531-46e7-e36d-9a3a4ece6e3f"
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      },
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      "execution_count": 41,
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      "outputs": [
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        {
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          "output_type": "execute_result",
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          "data": {
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            "text/plain": [
352
              "               age          bmi     children       charges\n",
353
              "count  1338.000000  1338.000000  1338.000000   1338.000000\n",
354
              "mean     39.207025    30.663397     1.094918  13270.422265\n",
355
              "std      14.049960     6.098187     1.205493  12110.011237\n",
356
              "min      18.000000    15.960000     0.000000   1121.873900\n",
357
              "25%      27.000000    26.296250     0.000000   4740.287150\n",
358
              "50%      39.000000    30.400000     1.000000   9382.033000\n",
359
              "75%      51.000000    34.693750     2.000000  16639.912515\n",
360
              "max      64.000000    53.130000     5.000000  63770.428010"
361
            ],
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            "text/html": [
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              "\n",
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              "\n",
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              "  <div id=\"df-d93edbdc-c962-43ea-80bb-411781e0f366\">\n",
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              "    <div class=\"colab-df-container\">\n",
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              "      <div>\n",
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              "<style scoped>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "        vertical-align: middle;\n",
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              "    }\n",
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              "\n",
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              "    .dataframe tbody tr th {\n",
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              "        vertical-align: top;\n",
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              "    }\n",
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              "\n",
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              "    .dataframe thead th {\n",
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              "        text-align: right;\n",
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              "    }\n",
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              "</style>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "  <thead>\n",
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th></th>\n",
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              "      <th>age</th>\n",
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              "      <th>bmi</th>\n",
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              "      <th>children</th>\n",
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              "      <th>charges</th>\n",
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              "    </tr>\n",
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              "  </thead>\n",
391
              "  <tbody>\n",
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              "    <tr>\n",
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              "      <th>count</th>\n",
394
              "      <td>1338.000000</td>\n",
395
              "      <td>1338.000000</td>\n",
396
              "      <td>1338.000000</td>\n",
397
              "      <td>1338.000000</td>\n",
398
              "    </tr>\n",
399
              "    <tr>\n",
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              "      <th>mean</th>\n",
401
              "      <td>39.207025</td>\n",
402
              "      <td>30.663397</td>\n",
403
              "      <td>1.094918</td>\n",
404
              "      <td>13270.422265</td>\n",
405
              "    </tr>\n",
406
              "    <tr>\n",
407
              "      <th>std</th>\n",
408
              "      <td>14.049960</td>\n",
409
              "      <td>6.098187</td>\n",
410
              "      <td>1.205493</td>\n",
411
              "      <td>12110.011237</td>\n",
412
              "    </tr>\n",
413
              "    <tr>\n",
414
              "      <th>min</th>\n",
415
              "      <td>18.000000</td>\n",
416
              "      <td>15.960000</td>\n",
417
              "      <td>0.000000</td>\n",
418
              "      <td>1121.873900</td>\n",
419
              "    </tr>\n",
420
              "    <tr>\n",
421
              "      <th>25%</th>\n",
422
              "      <td>27.000000</td>\n",
423
              "      <td>26.296250</td>\n",
424
              "      <td>0.000000</td>\n",
425
              "      <td>4740.287150</td>\n",
426
              "    </tr>\n",
427
              "    <tr>\n",
428
              "      <th>50%</th>\n",
429
              "      <td>39.000000</td>\n",
430
              "      <td>30.400000</td>\n",
431
              "      <td>1.000000</td>\n",
432
              "      <td>9382.033000</td>\n",
433
              "    </tr>\n",
434
              "    <tr>\n",
435
              "      <th>75%</th>\n",
436
              "      <td>51.000000</td>\n",
437
              "      <td>34.693750</td>\n",
438
              "      <td>2.000000</td>\n",
439
              "      <td>16639.912515</td>\n",
440
              "    </tr>\n",
441
              "    <tr>\n",
442
              "      <th>max</th>\n",
443
              "      <td>64.000000</td>\n",
444
              "      <td>53.130000</td>\n",
445
              "      <td>5.000000</td>\n",
446
              "      <td>63770.428010</td>\n",
447
              "    </tr>\n",
448
              "  </tbody>\n",
449
              "</table>\n",
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              "</div>\n",
451
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d93edbdc-c962-43ea-80bb-411781e0f366')\"\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
453
              "              style=\"display:none;\">\n",
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              "\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "       width=\"24px\">\n",
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
458
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
459
              "  </svg>\n",
460
              "      </button>\n",
461
              "\n",
462
              "\n",
463
              "\n",
464
              "    <div id=\"df-46ab814f-f9c7-4a5e-9deb-e2e2b004a7b9\">\n",
465
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-46ab814f-f9c7-4a5e-9deb-e2e2b004a7b9')\"\n",
466
              "              title=\"Suggest charts.\"\n",
467
              "              style=\"display:none;\">\n",
468
              "\n",
469
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
470
              "     width=\"24px\">\n",
471
              "    <g>\n",
472
              "        <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",
473
              "    </g>\n",
474
              "</svg>\n",
475
              "      </button>\n",
476
              "    </div>\n",
477
              "\n",
478
              "<style>\n",
479
              "  .colab-df-quickchart {\n",
480
              "    background-color: #E8F0FE;\n",
481
              "    border: none;\n",
482
              "    border-radius: 50%;\n",
483
              "    cursor: pointer;\n",
484
              "    display: none;\n",
485
              "    fill: #1967D2;\n",
486
              "    height: 32px;\n",
487
              "    padding: 0 0 0 0;\n",
488
              "    width: 32px;\n",
489
              "  }\n",
490
              "\n",
491
              "  .colab-df-quickchart:hover {\n",
492
              "    background-color: #E2EBFA;\n",
493
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
494
              "    fill: #174EA6;\n",
495
              "  }\n",
496
              "\n",
497
              "  [theme=dark] .colab-df-quickchart {\n",
498
              "    background-color: #3B4455;\n",
499
              "    fill: #D2E3FC;\n",
500
              "  }\n",
501
              "\n",
502
              "  [theme=dark] .colab-df-quickchart:hover {\n",
503
              "    background-color: #434B5C;\n",
504
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
505
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
506
              "    fill: #FFFFFF;\n",
507
              "  }\n",
508
              "</style>\n",
509
              "\n",
510
              "    <script>\n",
511
              "      async function quickchart(key) {\n",
512
              "        const containerElement = document.querySelector('#' + key);\n",
513
              "        const charts = await google.colab.kernel.invokeFunction(\n",
514
              "            'suggestCharts', [key], {});\n",
515
              "      }\n",
516
              "    </script>\n",
517
              "\n",
518
              "      <script>\n",
519
              "\n",
520
              "function displayQuickchartButton(domScope) {\n",
521
              "  let quickchartButtonEl =\n",
522
              "    domScope.querySelector('#df-46ab814f-f9c7-4a5e-9deb-e2e2b004a7b9 button.colab-df-quickchart');\n",
523
              "  quickchartButtonEl.style.display =\n",
524
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
525
              "}\n",
526
              "\n",
527
              "        displayQuickchartButton(document);\n",
528
              "      </script>\n",
529
              "      <style>\n",
530
              "    .colab-df-container {\n",
531
              "      display:flex;\n",
532
              "      flex-wrap:wrap;\n",
533
              "      gap: 12px;\n",
534
              "    }\n",
535
              "\n",
536
              "    .colab-df-convert {\n",
537
              "      background-color: #E8F0FE;\n",
538
              "      border: none;\n",
539
              "      border-radius: 50%;\n",
540
              "      cursor: pointer;\n",
541
              "      display: none;\n",
542
              "      fill: #1967D2;\n",
543
              "      height: 32px;\n",
544
              "      padding: 0 0 0 0;\n",
545
              "      width: 32px;\n",
546
              "    }\n",
547
              "\n",
548
              "    .colab-df-convert:hover {\n",
549
              "      background-color: #E2EBFA;\n",
550
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
551
              "      fill: #174EA6;\n",
552
              "    }\n",
553
              "\n",
554
              "    [theme=dark] .colab-df-convert {\n",
555
              "      background-color: #3B4455;\n",
556
              "      fill: #D2E3FC;\n",
557
              "    }\n",
558
              "\n",
559
              "    [theme=dark] .colab-df-convert:hover {\n",
560
              "      background-color: #434B5C;\n",
561
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
562
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
563
              "      fill: #FFFFFF;\n",
564
              "    }\n",
565
              "  </style>\n",
566
              "\n",
567
              "      <script>\n",
568
              "        const buttonEl =\n",
569
              "          document.querySelector('#df-d93edbdc-c962-43ea-80bb-411781e0f366 button.colab-df-convert');\n",
570
              "        buttonEl.style.display =\n",
571
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
572
              "\n",
573
              "        async function convertToInteractive(key) {\n",
574
              "          const element = document.querySelector('#df-d93edbdc-c962-43ea-80bb-411781e0f366');\n",
575
              "          const dataTable =\n",
576
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
577
              "                                                     [key], {});\n",
578
              "          if (!dataTable) return;\n",
579
              "\n",
580
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
581
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
582
              "            + ' to learn more about interactive tables.';\n",
583
              "          element.innerHTML = '';\n",
584
              "          dataTable['output_type'] = 'display_data';\n",
585
              "          await google.colab.output.renderOutput(dataTable, element);\n",
586
              "          const docLink = document.createElement('div');\n",
587
              "          docLink.innerHTML = docLinkHtml;\n",
588
              "          element.appendChild(docLink);\n",
589
              "        }\n",
590
              "      </script>\n",
591
              "    </div>\n",
592
              "  </div>\n"
593
            ]
594
          },
595
          "metadata": {},
596
          "execution_count": 41
597
        }
598
      ]
599
    },
600
    {
601
      "cell_type": "code",
602
      "source": [
603
        "df.isna().sum()"
604
      ],
605
      "metadata": {
606
        "colab": {
607
          "base_uri": "https://localhost:8080/"
608
        },
609
        "id": "OlTGZBj557dl",
610
        "outputId": "2a3d031d-6cf6-43ca-aa49-86041dbbb561"
611
      },
612
      "execution_count": 42,
613
      "outputs": [
614
        {
615
          "output_type": "execute_result",
616
          "data": {
617
            "text/plain": [
618
              "age         0\n",
619
              "sex         0\n",
620
              "bmi         0\n",
621
              "children    0\n",
622
              "smoker      0\n",
623
              "region      0\n",
624
              "charges     0\n",
625
              "dtype: int64"
626
            ]
627
          },
628
          "metadata": {},
629
          "execution_count": 42
630
        }
631
      ]
632
    },
633
    {
634
      "cell_type": "code",
635
      "source": [
636
        "df.drop_duplicates(inplace=True)"
637
      ],
638
      "metadata": {
639
        "id": "uCb8gl_b6Bfd"
640
      },
641
      "execution_count": 43,
642
      "outputs": []
643
    },
644
    {
645
      "cell_type": "code",
646
      "source": [
647
        "lblenc=LabelEncoder()"
648
      ],
649
      "metadata": {
650
        "id": "fpcH90tp7ZfK"
651
      },
652
      "execution_count": 44,
653
      "outputs": []
654
    },
655
    {
656
      "cell_type": "code",
657
      "source": [
658
        "df[\"region\"]=lblenc.fit_transform(df[\"region\"])\n",
659
        "df[\"smoker\"]=lblenc.fit_transform(df[\"smoker\"])\n",
660
        "df[\"sex\"]=lblenc.fit_transform(df[\"region\"])"
661
      ],
662
      "metadata": {
663
        "id": "roC7gHTz7gcF"
664
      },
665
      "execution_count": 45,
666
      "outputs": []
667
    },
668
    {
669
      "cell_type": "code",
670
      "source": [
671
        "sns.kdeplot(x=\"charges\",data=df,hue=\"sex\")\n",
672
        "plt.title(\"Distribution among the charges\")"
673
      ],
674
      "metadata": {
675
        "colab": {
676
          "base_uri": "https://localhost:8080/",
677
          "height": 489
678
        },
679
        "id": "2-CgFzN_8uMo",
680
        "outputId": "6ed688c6-9a57-4b16-f0cc-e6eca24c376f"
681
      },
682
      "execution_count": 46,
683
      "outputs": [
684
        {
685
          "output_type": "execute_result",
686
          "data": {
687
            "text/plain": [
688
              "Text(0.5, 1.0, 'Distribution among the charges')"
689
            ]
690
          },
691
          "metadata": {},
692
          "execution_count": 46
693
        },
694
        {
695
          "output_type": "display_data",
696
          "data": {
697
            "text/plain": [
698
              "<Figure size 640x480 with 1 Axes>"
699
            ],
700
            "image/png": 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\n"
701
          },
702
          "metadata": {}
703
        }
704
      ]
705
    },
706
    {
707
      "cell_type": "code",
708
      "source": [
709
        "sns.kdeplot(x=\"bmi\",data=df)\n",
710
        "plt.title(\"Distribution among the bmi\")"
711
      ],
712
      "metadata": {
713
        "colab": {
714
          "base_uri": "https://localhost:8080/",
715
          "height": 489
716
        },
717
        "id": "_jK0FqLr8loL",
718
        "outputId": "f31daced-c23b-4f34-9009-a62b31d7061b"
719
      },
720
      "execution_count": 47,
721
      "outputs": [
722
        {
723
          "output_type": "execute_result",
724
          "data": {
725
            "text/plain": [
726
              "Text(0.5, 1.0, 'Distribution among the bmi')"
727
            ]
728
          },
729
          "metadata": {},
730
          "execution_count": 47
731
        },
732
        {
733
          "output_type": "display_data",
734
          "data": {
735
            "text/plain": [
736
              "<Figure size 640x480 with 1 Axes>"
737
            ],
738
            "image/png": 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\n"
739
          },
740
          "metadata": {}
741
        }
742
      ]
743
    },
744
    {
745
      "cell_type": "code",
746
      "source": [
747
        "sns.kdeplot(x=\"age\",data=df)\n",
748
        "plt.title(\"Distribution among the age\")"
749
      ],
750
      "metadata": {
751
        "colab": {
752
          "base_uri": "https://localhost:8080/",
753
          "height": 489
754
        },
755
        "id": "CKpExygb8SmZ",
756
        "outputId": "7b34094b-a105-4cb1-a25b-7e07f74f56c3"
757
      },
758
      "execution_count": 48,
759
      "outputs": [
760
        {
761
          "output_type": "execute_result",
762
          "data": {
763
            "text/plain": [
764
              "Text(0.5, 1.0, 'Distribution among the age')"
765
            ]
766
          },
767
          "metadata": {},
768
          "execution_count": 48
769
        },
770
        {
771
          "output_type": "display_data",
772
          "data": {
773
            "text/plain": [
774
              "<Figure size 640x480 with 1 Axes>"
775
            ],
776
            "image/png": 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\n"
777
          },
778
          "metadata": {}
779
        }
780
      ]
781
    },
782
    {
783
      "cell_type": "code",
784
      "source": [
785
        "sns.countplot(x=\"sex\",data=df)\n",
786
        "plt.title(\"Distribution among the genders\")"
787
      ],
788
      "metadata": {
789
        "colab": {
790
          "base_uri": "https://localhost:8080/",
791
          "height": 489
792
        },
793
        "id": "8zOdXV2E6gfI",
794
        "outputId": "70a1cc64-cdd9-4778-f5bf-8d46cd6bb7a7"
795
      },
796
      "execution_count": 49,
797
      "outputs": [
798
        {
799
          "output_type": "execute_result",
800
          "data": {
801
            "text/plain": [
802
              "Text(0.5, 1.0, 'Distribution among the genders')"
803
            ]
804
          },
805
          "metadata": {},
806
          "execution_count": 49
807
        },
808
        {
809
          "output_type": "display_data",
810
          "data": {
811
            "text/plain": [
812
              "<Figure size 640x480 with 1 Axes>"
813
            ],
814
            "image/png": 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\n"
815
          },
816
          "metadata": {}
817
        }
818
      ]
819
    },
820
    {
821
      "cell_type": "code",
822
      "source": [
823
        "sns.countplot(x=\"age\",data=df)\n",
824
        "plt.title(\"Distribution among the age\")"
825
      ],
826
      "metadata": {
827
        "colab": {
828
          "base_uri": "https://localhost:8080/",
829
          "height": 489
830
        },
831
        "id": "ATJdTvt_63DM",
832
        "outputId": "e0ae5b71-b7b1-485f-ceba-02c1fde4f19b"
833
      },
834
      "execution_count": 50,
835
      "outputs": [
836
        {
837
          "output_type": "execute_result",
838
          "data": {
839
            "text/plain": [
840
              "Text(0.5, 1.0, 'Distribution among the age')"
841
            ]
842
          },
843
          "metadata": {},
844
          "execution_count": 50
845
        },
846
        {
847
          "output_type": "display_data",
848
          "data": {
849
            "text/plain": [
850
              "<Figure size 640x480 with 1 Axes>"
851
            ],
852
            "image/png": 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\n"
853
          },
854
          "metadata": {}
855
        }
856
      ]
857
    },
858
    {
859
      "cell_type": "code",
860
      "source": [
861
        "sns.countplot(x=\"region\",data=df)\n",
862
        "plt.title(\"Distribution among the region\")"
863
      ],
864
      "metadata": {
865
        "colab": {
866
          "base_uri": "https://localhost:8080/",
867
          "height": 489
868
        },
869
        "id": "oIliR1SJ7G16",
870
        "outputId": "1d5ea8bb-ae12-418f-cd55-6d8119490ad7"
871
      },
872
      "execution_count": 51,
873
      "outputs": [
874
        {
875
          "output_type": "execute_result",
876
          "data": {
877
            "text/plain": [
878
              "Text(0.5, 1.0, 'Distribution among the region')"
879
            ]
880
          },
881
          "metadata": {},
882
          "execution_count": 51
883
        },
884
        {
885
          "output_type": "display_data",
886
          "data": {
887
            "text/plain": [
888
              "<Figure size 640x480 with 1 Axes>"
889
            ],
890
            "image/png": 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\n"
891
          },
892
          "metadata": {}
893
        }
894
      ]
895
    },
896
    {
897
      "cell_type": "code",
898
      "source": [
899
        "sns.heatmap(df.corr(),annot=True)"
900
      ],
901
      "metadata": {
902
        "colab": {
903
          "base_uri": "https://localhost:8080/",
904
          "height": 452
905
        },
906
        "id": "g2ZNDpAG75H-",
907
        "outputId": "bdf9b1b8-aab9-42b0-8bab-51857ca901b2"
908
      },
909
      "execution_count": 52,
910
      "outputs": [
911
        {
912
          "output_type": "execute_result",
913
          "data": {
914
            "text/plain": [
915
              "<Axes: >"
916
            ]
917
          },
918
          "metadata": {},
919
          "execution_count": 52
920
        },
921
        {
922
          "output_type": "display_data",
923
          "data": {
924
            "text/plain": [
925
              "<Figure size 640x480 with 2 Axes>"
926
            ],
927
            "image/png": 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\n"
928
          },
929
          "metadata": {}
930
        }
931
      ]
932
    },
933
    {
934
      "cell_type": "code",
935
      "source": [
936
        "x=df.drop(labels=\"charges\",axis=1)\n",
937
        "y=df[\"charges\"]"
938
      ],
939
      "metadata": {
940
        "id": "RSrVAkbc9RyL"
941
      },
942
      "execution_count": 54,
943
      "outputs": []
944
    },
945
    {
946
      "cell_type": "code",
947
      "source": [
948
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)"
949
      ],
950
      "metadata": {
951
        "id": "j6L3QLuh-HKM"
952
      },
953
      "execution_count": 56,
954
      "outputs": []
955
    },
956
    {
957
      "cell_type": "code",
958
      "source": [
959
        "x_train=StandardScaler().fit_transform(x_train)\n",
960
        "x_test=StandardScaler().fit_transform(x_test)"
961
      ],
962
      "metadata": {
963
        "id": "lOvrJDNp-fik"
964
      },
965
      "execution_count": 59,
966
      "outputs": []
967
    },
968
    {
969
      "cell_type": "code",
970
      "source": [
971
        "model=LinearRegression()\n",
972
        "model.fit(x_train,y_train)"
973
      ],
974
      "metadata": {
975
        "colab": {
976
          "base_uri": "https://localhost:8080/",
977
          "height": 74
978
        },
979
        "id": "4oRMRHod_AAT",
980
        "outputId": "1be27c64-fe7e-4841-9976-3948f1745ab1"
981
      },
982
      "execution_count": 62,
983
      "outputs": [
984
        {
985
          "output_type": "execute_result",
986
          "data": {
987
            "text/plain": [
988
              "LinearRegression()"
989
            ],
990
            "text/html": [
991
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
992
            ]
993
          },
994
          "metadata": {},
995
          "execution_count": 62
996
        }
997
      ]
998
    },
999
    {
1000
      "cell_type": "code",
1001
      "source": [
1002
        "y_pred=model.predict(x_test)"
1003
      ],
1004
      "metadata": {
1005
        "id": "vJCNEaea_UUB"
1006
      },
1007
      "execution_count": 63,
1008
      "outputs": []
1009
    },
1010
    {
1011
      "cell_type": "code",
1012
      "source": [
1013
        "y_pred"
1014
      ],
1015
      "metadata": {
1016
        "colab": {
1017
          "base_uri": "https://localhost:8080/"
1018
        },
1019
        "id": "JS7UxpsL_e-c",
1020
        "outputId": "53334350-3b37-41df-86aa-c54a3ee7c9fb"
1021
      },
1022
      "execution_count": 64,
1023
      "outputs": [
1024
        {
1025
          "output_type": "execute_result",
1026
          "data": {
1027
            "text/plain": [
1028
              "array([ 4941.17552888,  9551.37206839, 11414.26710354, 25485.16935817,\n",
1029
              "        7047.95930171,   673.38292304,  1650.94231767, -1206.79039293,\n",
1030
              "        1834.21917014, 14337.96186307,  9842.85352932, 26279.68414361,\n",
1031
              "       14460.93089325,  9420.02329158,  5370.67237389,  9853.59891659,\n",
1032
              "        5946.09070453,  5997.19053503,  4780.51558164, 14784.41763995,\n",
1033
              "        2396.45853249, 12221.89436679,  1977.86133809,  4269.42803685,\n",
1034
              "        4125.18607042,  8955.31503562,  1443.64561107, 12379.54757757,\n",
1035
              "        3966.4096274 , 29197.1630604 ,  8953.17021437, 38274.45960148,\n",
1036
              "        8749.62518031, 13065.87920232, 24581.17470128, 15710.06113132,\n",
1037
              "       12432.52478477, 29690.72631048,  6990.66809012,  3099.55036623,\n",
1038
              "       26633.46999684,  4126.54231153,  5543.02205033, 38624.86205441,\n",
1039
              "       27217.64933425, 11811.78009344, 10815.35997847,  9864.46494705,\n",
1040
              "       13212.88640366,  7050.93313373, 33218.61103583,  5141.14766527,\n",
1041
              "       32175.36772787, 32346.73326482, 15585.7480994 ,  3819.06471021,\n",
1042
              "        5429.39474277,  9309.21164068, 11736.82243065, 37205.28242862,\n",
1043
              "       10289.83115752, 23421.17667335,  5695.46648829, 15670.97275978,\n",
1044
              "        5861.14874196,  7875.81123504,  -966.43931889, 31580.70132199,\n",
1045
              "        1280.97080957, 10097.33637203, 34736.09386964,  4004.84011865,\n",
1046
              "        8629.26094429, 15317.6094798 , 11099.83323702, 12539.4724473 ,\n",
1047
              "        6305.54910142,  9684.40947303,  3393.01539217,  5485.25556269,\n",
1048
              "       11908.84887889,  4088.85757187,  5137.75817023,  8916.30685951,\n",
1049
              "       34165.13018445, 28729.65743203, 35398.04346273,  4697.76121899,\n",
1050
              "       11610.95137099, 29373.36906647,  9873.64622892,  6365.72474121,\n",
1051
              "       25058.46289267,  9075.57076807,  8169.27319723,  7214.27194305,\n",
1052
              "        9978.22521981,  2716.15006236,  7439.00813683, 14217.61211471,\n",
1053
              "       13167.38962823, 35952.20717414,  6371.76104348, 30998.0822606 ,\n",
1054
              "        9957.92954558,  8000.49944945,  7980.46202847,  6311.71990012,\n",
1055
              "       14236.34981107, 36134.76350229, 15309.59267629, 10243.51762636,\n",
1056
              "       10038.17009899, 26232.84028557,  1394.0671073 ,  6586.56259363,\n",
1057
              "        7660.72771992,  8599.10166334, 14291.86220829, 11802.34210664,\n",
1058
              "       34676.79463138,  9286.71311326, 10896.76424462,  5790.83060992,\n",
1059
              "        7854.70559989,  9480.74371313, 32512.41854948, 31145.27892655,\n",
1060
              "       38267.87928439,  9982.3092292 , 14704.50640949,  3611.22781815,\n",
1061
              "        5850.93747817,  3466.0926145 ,  5514.16325961, 29023.05981395,\n",
1062
              "       29559.28430048,  7540.30996562, 32272.79945643,  9432.54361397,\n",
1063
              "       13911.34863077, 10910.04483902,   496.67338267, 12606.76709118,\n",
1064
              "        5447.75649103,  8789.27823173,  5899.57543661,  2552.96636465,\n",
1065
              "       13677.33489093, 13189.05682961,  9481.14790238, 31057.71388557,\n",
1066
              "        1467.11530576,  7134.08102867,  6305.09295788, 11535.10052086,\n",
1067
              "        8858.37993112,  2289.29691093, 33073.19619814, 12275.87785987,\n",
1068
              "        7738.43225072, 11012.15662872, 11642.12534783,  9080.53655801,\n",
1069
              "        5520.58769947,  7301.97990613, 22532.82057566,   376.09442202,\n",
1070
              "        5565.31274624,  3823.17088309,  6551.50935804, 25799.66510309,\n",
1071
              "        9816.08757278,  5253.93602082,  5414.27039183,  6794.21609872,\n",
1072
              "       12383.79362591,  5672.33757321, 37605.95565139,  6544.42014475,\n",
1073
              "       12860.7960885 ,  7930.65046593, 13345.46153723,   192.29873211,\n",
1074
              "        5462.80975517,  9500.5427462 , 14905.84091453,  1764.64602407,\n",
1075
              "       30435.07345064, 13896.50319814, 34488.46181954,  4945.57663895,\n",
1076
              "        6685.90223819, 30995.68606356, 27089.7297674 ,  1847.92458607,\n",
1077
              "        8440.31073268,  9867.18740328,  4048.87216651, 37924.42312264,\n",
1078
              "       12951.76592766,  9280.32532455, 13449.00137059, 28262.78219132,\n",
1079
              "       32529.36447636,  7823.74942856,  4217.60232012,  9003.22629234,\n",
1080
              "        8061.80056974, 23984.02729084,  9987.54554455, 39057.91271446,\n",
1081
              "       31497.22107311,  3880.8434047 ,  7494.02694098,  5344.21972143,\n",
1082
              "        7992.60107362,  4639.06101361, 13069.10744112, 33432.9251532 ,\n",
1083
              "       11161.03199838,  7459.47417594,  7340.89023696, 10669.22155269,\n",
1084
              "       10498.58957782, 16268.58552214, 14738.39899609, 10181.81836169,\n",
1085
              "       11065.67094051,  2463.91131896,  6281.35355327, 37844.49966991,\n",
1086
              "       11462.54821362, 39261.31102982, 24717.21649435,  4116.14537973,\n",
1087
              "       12380.72429531, 27749.30229597,  1491.71888964, 36083.05624602,\n",
1088
              "        1797.59803256,  7260.09800866, 10475.37662084, 17975.91022236,\n",
1089
              "        4037.61485204, 12200.51677344, 13617.54512336,  3777.55264899,\n",
1090
              "        8392.18473421,  8056.89056346,  1126.87136395, 37681.16392436,\n",
1091
              "        8608.49384048, 11280.61153352,  7432.39419959,   651.9831662 ,\n",
1092
              "       29203.39492202,  2487.58654701,  5085.25623908,  2692.34110349,\n",
1093
              "        7315.9955125 , 28357.97288432, 37550.5418165 ,  3748.85584734,\n",
1094
              "        8142.5469556 ,  1818.60428989, 33782.0079663 , 14516.91550276])"
1095
            ]
1096
          },
1097
          "metadata": {},
1098
          "execution_count": 64
1099
        }
1100
      ]
1101
    },
1102
    {
1103
      "cell_type": "code",
1104
      "source": [
1105
        "r2 = r2_score(y_test, y_pred)\n",
1106
        "print(f\"R-squared: {r2}\")"
1107
      ],
1108
      "metadata": {
1109
        "colab": {
1110
          "base_uri": "https://localhost:8080/"
1111
        },
1112
        "id": "nclOIZRyAPwN",
1113
        "outputId": "72450951-2997-4173-faa7-ad563a3601d4"
1114
      },
1115
      "execution_count": 68,
1116
      "outputs": [
1117
        {
1118
          "output_type": "stream",
1119
          "name": "stdout",
1120
          "text": [
1121
            "R-squared: 0.7512381273554272\n"
1122
          ]
1123
        }
1124
      ]
1125
    }
1126
  ]
1127
}