Insurance prediction-using pycaret.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Insurance prediction using PyCaret\n",
    "\n",
    "Pycaret is one of the library, where with low code it can create an automated process to apply all the features such as feature engineering, feature selection, handling missing values, Normalization etc. It also allows to apply machine learning algorithms and compare between them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.datasets import get_data\n",
    "data = get_data('insurance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing\n",
    "In this step with the command 'Setup' and specifying all the required parameters in it. You can perform data preprocessing steps. By using this you dont need to handle one feature at a time it will handle all at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Setup Succesfully Completed.\n"
     ]
    },
    {
     "data": {
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       "                <tr>\n",
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       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row0_col1\" class=\"data row0 col1\" >2559</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row1_col0\" class=\"data row1 col0\" >Transform Target </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row1_col1\" class=\"data row1 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row2_col0\" class=\"data row2 col0\" >Transform Target Method</td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row2_col1\" class=\"data row2 col1\" >None</td>\n",
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       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row3_col1\" class=\"data row3 col1\" >(1338, 7)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row4_col1\" class=\"data row4 col1\" >False</td>\n",
       "            </tr>\n",
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       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row5_col1\" class=\"data row5 col1\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row6_col1\" class=\"data row6 col1\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
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       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row9_col1\" class=\"data row9 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row10_col1\" class=\"data row10 col1\" >(1338, 7)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row11_col1\" class=\"data row11 col1\" >(936, 65)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row12_col1\" class=\"data row12 col1\" >(402, 65)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row13_col1\" class=\"data row13 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row14_col1\" class=\"data row14 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row15_col1\" class=\"data row15 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row16_col1\" class=\"data row16 col1\" >zscore</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row18_col1\" class=\"data row18 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row19_col0\" class=\"data row19 col0\" >PCA </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row20_col1\" class=\"data row20 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row22_col1\" class=\"data row22 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row24_col1\" class=\"data row24 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row25_col1\" class=\"data row25 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row26_col1\" class=\"data row26 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row27_col1\" class=\"data row27 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row28_col1\" class=\"data row28 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row29_col1\" class=\"data row29 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row30_col1\" class=\"data row30 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row31_col1\" class=\"data row31 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row32_col1\" class=\"data row32 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row33_col1\" class=\"data row33 col1\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row34_col1\" class=\"data row34 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row35_col1\" class=\"data row35 col1\" >0.1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row38_col1\" class=\"data row38 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row39_col1\" class=\"data row39 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
       "                        <td id=\"T_6792bfc2_e02d_11ea_8da8_a01d48db95d2row41_col1\" class=\"data row41 col1\" >0.01</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x29e7aee3748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.regression import *\n",
    "s = setup(data, target = 'charges',categorical_features =['sex','smoker','region'],normalize = True,\n",
    "           polynomial_features = True, trigonometry_features = True, feature_interaction=True, \n",
    "           bin_numeric_features= ['age', 'bmi'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing all Machine learning alogithms( Regression problem)\n",
    "After using 'compare_models' it will compare with all machine learning models and will give you result in a sequence that which model yields the best accuracy. We can see from below out put that it has compared with 21 machine learning(regression) algorithms and we can see that gradient boosting outperformed all with around 82.5% accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row10_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row10_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row10_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row10_col5 {\n",
       "            background-color:  yellow;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row10_col6 {\n",
       "            background-color:  yellow;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row10_col7 {\n",
       "            background-color:  lightgrey;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row11_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row11_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row11_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row11_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row11_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row11_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row11_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row11_col7 {\n",
       "            background-color:  lightgrey;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row12_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row12_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row12_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row12_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row12_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row12_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row12_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row12_col7 {\n",
       "            background-color:  lightgrey;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row13_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row13_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row13_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row13_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row13_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row13_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row13_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row13_col7 {\n",
       "            background-color:  lightgrey;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row14_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row14_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row14_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row14_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row14_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row14_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row14_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row14_col7 {\n",
       "            background-color:  lightgrey;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row15_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row15_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row15_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row15_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row15_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row15_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row15_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row15_col7 {\n",
       "            background-color:  lightgrey;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row16_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row16_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row16_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row16_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row16_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row16_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row16_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row16_col7 {\n",
       "            background-color:  lightgrey;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row17_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row17_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row17_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row17_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row17_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row17_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row17_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row17_col7 {\n",
       "            background-color:  lightgrey;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row18_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row18_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row18_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row18_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row18_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row18_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row18_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row18_col7 {\n",
       "            background-color:  lightgrey;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row19_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row19_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row19_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row19_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row19_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row19_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row19_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row19_col7 {\n",
       "            background-color:  lightgrey;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row20_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row20_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row20_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row20_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row20_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row20_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row20_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row20_col7 {\n",
       "            background-color:  lightgrey;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row21_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row21_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row21_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row21_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row21_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row21_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row21_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_21328b12_e02e_11ea_917e_a01d48db95d2row21_col7 {\n",
       "            background-color:  lightgrey;\n",
       "            text-align:  left;\n",
       "        }</style><table id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>        <th class=\"col_heading level0 col7\" >TT (Sec)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row0_col0\" class=\"data row0 col0\" >Gradient Boosting Regressor</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row0_col1\" class=\"data row0 col1\" >2816</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row0_col2\" class=\"data row0 col2\" >2.435e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row0_col3\" class=\"data row0 col3\" >4897</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row0_col4\" class=\"data row0 col4\" >0.825</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row0_col5\" class=\"data row0 col5\" >0.4465</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row0_col6\" class=\"data row0 col6\" >0.3252</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row0_col7\" class=\"data row0 col7\" >0.5032</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row1_col0\" class=\"data row1 col0\" >Bayesian Ridge</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row1_col1\" class=\"data row1 col1\" >3074</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row1_col2\" class=\"data row1 col2\" >2.507e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row1_col3\" class=\"data row1 col3\" >4963</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row1_col4\" class=\"data row1 col4\" >0.8209</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row1_col5\" class=\"data row1 col5\" >0.4316</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row1_col6\" class=\"data row1 col6\" >0.3242</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row1_col7\" class=\"data row1 col7\" >0.0442</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row2_col0\" class=\"data row2 col0\" >CatBoost Regressor</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row2_col1\" class=\"data row2 col1\" >2841</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row2_col2\" class=\"data row2 col2\" >2.483e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row2_col3\" class=\"data row2 col3\" >4953</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row2_col4\" class=\"data row2 col4\" >0.8201</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row2_col5\" class=\"data row2 col5\" >0.4606</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row2_col6\" class=\"data row2 col6\" >0.3311</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row2_col7\" class=\"data row2 col7\" >7.762</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row3_col0\" class=\"data row3 col0\" >Ridge Regression</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row3_col1\" class=\"data row3 col1\" >3080</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row3_col2\" class=\"data row3 col2\" >2.519e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row3_col3\" class=\"data row3 col3\" >4974</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row3_col4\" class=\"data row3 col4\" >0.8199</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row3_col5\" class=\"data row3 col5\" >0.4327</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row3_col6\" class=\"data row3 col6\" >0.3239</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row3_col7\" class=\"data row3 col7\" >0.0133</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row4_col0\" class=\"data row4 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row4_col1\" class=\"data row4 col1\" >2997</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row4_col2\" class=\"data row4 col2\" >2.515e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row4_col3\" class=\"data row4 col3\" >4978</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row4_col4\" class=\"data row4 col4\" >0.8195</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row4_col5\" class=\"data row4 col5\" >0.5592</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row4_col6\" class=\"data row4 col6\" >0.3619</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row4_col7\" class=\"data row4 col7\" >0.5297</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row5_col0\" class=\"data row5 col0\" >Orthogonal Matching Pursuit</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row5_col1\" class=\"data row5 col1\" >3074</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row5_col2\" class=\"data row5 col2\" >2.536e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row5_col3\" class=\"data row5 col3\" >4998</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row5_col4\" class=\"data row5 col4\" >0.8193</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row5_col5\" class=\"data row5 col5\" >0.4261</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row5_col6\" class=\"data row5 col6\" >0.3232</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row5_col7\" class=\"data row5 col7\" >0.0138</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row6_col0\" class=\"data row6 col0\" >Lasso Regression</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row6_col1\" class=\"data row6 col1\" >3085</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row6_col2\" class=\"data row6 col2\" >2.529e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row6_col3\" class=\"data row6 col3\" >4983</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row6_col4\" class=\"data row6 col4\" >0.8192</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row6_col5\" class=\"data row6 col5\" >0.4327</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row6_col6\" class=\"data row6 col6\" >0.3238</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row6_col7\" class=\"data row6 col7\" >0.121</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row7_col0\" class=\"data row7 col0\" >Linear Regression</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row7_col1\" class=\"data row7 col1\" >3102</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row7_col2\" class=\"data row7 col2\" >2.542e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row7_col3\" class=\"data row7 col3\" >4997</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row7_col4\" class=\"data row7 col4\" >0.8182</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row7_col5\" class=\"data row7 col5\" >0.4353</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row7_col6\" class=\"data row7 col6\" >0.3179</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row7_col7\" class=\"data row7 col7\" >0.031</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row8_col0\" class=\"data row8 col0\" >Random Forest</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row8_col1\" class=\"data row8 col1\" >2875</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row8_col2\" class=\"data row8 col2\" >2.535e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row8_col3\" class=\"data row8 col3\" >5014</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row8_col4\" class=\"data row8 col4\" >0.8155</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row8_col5\" class=\"data row8 col5\" >0.4568</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row8_col6\" class=\"data row8 col6\" >0.3288</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row8_col7\" class=\"data row8 col7\" >0.8317</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row9_col0\" class=\"data row9 col0\" >TheilSen Regressor</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row9_col1\" class=\"data row9 col1\" >3028</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row9_col2\" class=\"data row9 col2\" >2.598e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row9_col3\" class=\"data row9 col3\" >5050</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row9_col4\" class=\"data row9 col4\" >0.8137</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row9_col5\" class=\"data row9 col5\" >0.4394</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row9_col6\" class=\"data row9 col6\" >0.3049</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row9_col7\" class=\"data row9 col7\" >15.49</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row10_col0\" class=\"data row10 col0\" >Passive Aggressive Regressor</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row10_col1\" class=\"data row10 col1\" >2014</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row10_col2\" class=\"data row10 col2\" >2.693e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row10_col3\" class=\"data row10 col3\" >5143</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row10_col4\" class=\"data row10 col4\" >0.809</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row10_col5\" class=\"data row10 col5\" >0.3883</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row10_col6\" class=\"data row10 col6\" >0.0816</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row10_col7\" class=\"data row10 col7\" >0.2803</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row11_col0\" class=\"data row11 col0\" >Huber Regressor</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row11_col1\" class=\"data row11 col1\" >2040</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row11_col2\" class=\"data row11 col2\" >2.689e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row11_col3\" class=\"data row11 col3\" >5137</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row11_col4\" class=\"data row11 col4\" >0.8087</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row11_col5\" class=\"data row11 col5\" >0.389</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row11_col6\" class=\"data row11 col6\" >0.0832</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row11_col7\" class=\"data row11 col7\" >0.1425</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row12_col0\" class=\"data row12 col0\" >Random Sample Consensus</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row12_col1\" class=\"data row12 col1\" >2405</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row12_col2\" class=\"data row12 col2\" >2.777e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row12_col3\" class=\"data row12 col3\" >5218</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row12_col4\" class=\"data row12 col4\" >0.8008</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row12_col5\" class=\"data row12 col5\" >0.4274</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row12_col6\" class=\"data row12 col6\" >0.1824</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row12_col7\" class=\"data row12 col7\" >1.209</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row13_col0\" class=\"data row13 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row13_col1\" class=\"data row13 col1\" >3066</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row13_col2\" class=\"data row13 col2\" >2.84e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row13_col3\" class=\"data row13 col3\" >5308</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row13_col4\" class=\"data row13 col4\" >0.7963</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row13_col5\" class=\"data row13 col5\" >0.5139</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row13_col6\" class=\"data row13 col6\" >0.3647</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row13_col7\" class=\"data row13 col7\" >0.5105</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row14_col0\" class=\"data row14 col0\" >Extra Trees Regressor</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row14_col1\" class=\"data row14 col1\" >2935</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row14_col2\" class=\"data row14 col2\" >3.034e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row14_col3\" class=\"data row14 col3\" >5474</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row14_col4\" class=\"data row14 col4\" >0.7794</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row14_col5\" class=\"data row14 col5\" >0.4737</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row14_col6\" class=\"data row14 col6\" >0.3242</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row14_col7\" class=\"data row14 col7\" >0.6802</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row15_col0\" class=\"data row15 col0\" >K Neighbors Regressor</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row15_col1\" class=\"data row15 col1\" >3293</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row15_col2\" class=\"data row15 col2\" >3.152e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row15_col3\" class=\"data row15 col3\" >5573</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row15_col4\" class=\"data row15 col4\" >0.7777</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row15_col5\" class=\"data row15 col5\" >0.4856</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row15_col6\" class=\"data row15 col6\" >0.3481</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row15_col7\" class=\"data row15 col7\" >0.0323</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row16_col0\" class=\"data row16 col0\" >AdaBoost Regressor</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row16_col1\" class=\"data row16 col1\" >4747</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row16_col2\" class=\"data row16 col2\" >3.306e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row16_col3\" class=\"data row16 col3\" >5716</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row16_col4\" class=\"data row16 col4\" >0.7557</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row16_col5\" class=\"data row16 col5\" >0.6751</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row16_col6\" class=\"data row16 col6\" >0.8381</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row16_col7\" class=\"data row16 col7\" >0.164</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row17_col0\" class=\"data row17 col0\" >Lasso Least Angle Regression</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row17_col1\" class=\"data row17 col1\" >3817</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row17_col2\" class=\"data row17 col2\" >3.73e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row17_col3\" class=\"data row17 col3\" >5764</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row17_col4\" class=\"data row17 col4\" >0.6658</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row17_col5\" class=\"data row17 col5\" >0.523</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row17_col6\" class=\"data row17 col6\" >0.4615</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row17_col7\" class=\"data row17 col7\" >0.027</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row18_col0\" class=\"data row18 col0\" >Decision Tree</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row18_col1\" class=\"data row18 col1\" >3455</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row18_col2\" class=\"data row18 col2\" >4.588e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row18_col3\" class=\"data row18 col3\" >6735</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row18_col4\" class=\"data row18 col4\" >0.6619</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row18_col5\" class=\"data row18 col5\" >0.5385</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row18_col6\" class=\"data row18 col6\" >0.3731</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row18_col7\" class=\"data row18 col7\" >0.0262</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row19_col0\" class=\"data row19 col0\" >Elastic Net</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row19_col1\" class=\"data row19 col1\" >5541</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row19_col2\" class=\"data row19 col2\" >5.152e+07</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row19_col3\" class=\"data row19 col3\" >7096</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row19_col4\" class=\"data row19 col4\" >0.6552</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row19_col5\" class=\"data row19 col5\" >0.6543</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row19_col6\" class=\"data row19 col6\" >0.7771</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row19_col7\" class=\"data row19 col7\" >0.0108</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row20_col0\" class=\"data row20 col0\" >Support Vector Machine</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row20_col1\" class=\"data row20 col1\" >8569</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row20_col2\" class=\"data row20 col2\" >1.687e+08</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row20_col3\" class=\"data row20 col3\" >1.28e+04</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row20_col4\" class=\"data row20 col4\" >-0.1098</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row20_col5\" class=\"data row20 col5\" >0.9362</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row20_col6\" class=\"data row20 col6\" >1.06</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row20_col7\" class=\"data row20 col7\" >0.1373</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row21_col0\" class=\"data row21 col0\" >Least Angle Regression</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row21_col1\" class=\"data row21 col1\" >1.165e+04</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row21_col2\" class=\"data row21 col2\" >3.829e+08</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row21_col3\" class=\"data row21 col3\" >1.508e+04</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row21_col4\" class=\"data row21 col4\" >-1.537</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row21_col5\" class=\"data row21 col5\" >0.9029</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row21_col6\" class=\"data row21 col6\" >1.557</td>\n",
       "                        <td id=\"T_21328b12_e02e_11ea_917e_a01d48db95d2row21_col7\" class=\"data row21 col7\" >0.0497</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x29e7ae2c828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',\n",
       "                          init=None, learning_rate=0.1, loss='ls', max_depth=3,\n",
       "                          max_features=None, max_leaf_nodes=None,\n",
       "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                          min_samples_leaf=1, min_samples_split=2,\n",
       "                          min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                          n_iter_no_change=None, presort='deprecated',\n",
       "                          random_state=2559, subsample=1.0, tol=0.0001,\n",
       "                          validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_models()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation\n",
    "Here with command \"create_model\" we applied k-fold cross validation with k=10(by default) to get a better accuracy rate and we can see that after performing 10 folds we received an accuracy of around 86.2%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_26d22408_e02d_11ea_8042_a01d48db95d2row10_col0 {\n",
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       "            background:  yellow;\n",
       "        }    #T_26d22408_e02d_11ea_8042_a01d48db95d2row10_col2 {\n",
       "            background:  yellow;\n",
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       "            background:  yellow;\n",
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       "            background:  yellow;\n",
       "        }</style><table id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row0_col0\" class=\"data row0 col0\" >2322</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row0_col1\" class=\"data row0 col1\" >1.686e+07</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row0_col2\" class=\"data row0 col2\" >4106</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row0_col3\" class=\"data row0 col3\" >0.8739</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row0_col4\" class=\"data row0 col4\" >0.3941</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row0_col5\" class=\"data row0 col5\" >0.3198</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row1_col0\" class=\"data row1 col0\" >2341</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row1_col1\" class=\"data row1 col1\" >1.715e+07</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row1_col2\" class=\"data row1 col2\" >4142</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row1_col3\" class=\"data row1 col3\" >0.8694</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row1_col4\" class=\"data row1 col4\" >0.3614</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row1_col5\" class=\"data row1 col5\" >0.3082</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row2_col0\" class=\"data row2 col0\" >2480</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row2_col1\" class=\"data row2 col1\" >2.665e+07</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row2_col2\" class=\"data row2 col2\" >5163</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row2_col3\" class=\"data row2 col3\" >0.8428</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row2_col4\" class=\"data row2 col4\" >0.4682</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row2_col5\" class=\"data row2 col5\" >0.2615</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row3_col0\" class=\"data row3 col0\" >2191</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row3_col1\" class=\"data row3 col1\" >1.889e+07</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row3_col2\" class=\"data row3 col2\" >4346</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row3_col3\" class=\"data row3 col3\" >0.8966</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row3_col4\" class=\"data row3 col4\" >0.4839</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row3_col5\" class=\"data row3 col5\" >0.2594</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row4_col0\" class=\"data row4 col0\" >2595</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row4_col1\" class=\"data row4 col1\" >2.12e+07</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row4_col2\" class=\"data row4 col2\" >4604</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row4_col3\" class=\"data row4 col3\" >0.8394</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row4_col4\" class=\"data row4 col4\" >0.3661</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row4_col5\" class=\"data row4 col5\" >0.2616</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row5_col0\" class=\"data row5 col0\" >2951</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row5_col1\" class=\"data row5 col1\" >2.559e+07</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row5_col2\" class=\"data row5 col2\" >5058</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row5_col3\" class=\"data row5 col3\" >0.8091</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row5_col4\" class=\"data row5 col4\" >0.421</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row5_col5\" class=\"data row5 col5\" >0.3383</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row6_col0\" class=\"data row6 col0\" >2188</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row6_col1\" class=\"data row6 col1\" >1.823e+07</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row6_col2\" class=\"data row6 col2\" >4270</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row6_col3\" class=\"data row6 col3\" >0.892</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row6_col4\" class=\"data row6 col4\" >0.4751</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row6_col5\" class=\"data row6 col5\" >0.2623</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row7_col0\" class=\"data row7 col0\" >3018</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row7_col1\" class=\"data row7 col1\" >2.676e+07</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row7_col2\" class=\"data row7 col2\" >5173</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row7_col3\" class=\"data row7 col3\" >0.8549</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row7_col4\" class=\"data row7 col4\" >0.5376</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row7_col5\" class=\"data row7 col5\" >0.3883</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row8_col0\" class=\"data row8 col0\" >1938</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row8_col1\" class=\"data row8 col1\" >1.124e+07</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row8_col2\" class=\"data row8 col2\" >3352</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row8_col3\" class=\"data row8 col3\" >0.9084</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row8_col4\" class=\"data row8 col4\" >0.3979</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row8_col5\" class=\"data row8 col5\" >0.3144</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row9_col0\" class=\"data row9 col0\" >2396</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row9_col1\" class=\"data row9 col1\" >1.9e+07</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row9_col2\" class=\"data row9 col2\" >4359</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row9_col3\" class=\"data row9 col3\" >0.8393</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row9_col4\" class=\"data row9 col4\" >0.4478</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row9_col5\" class=\"data row9 col5\" >0.3399</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row10_col0\" class=\"data row10 col0\" >2442</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row10_col1\" class=\"data row10 col1\" >2.016e+07</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row10_col2\" class=\"data row10 col2\" >4457</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row10_col3\" class=\"data row10 col3\" >0.8626</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row10_col4\" class=\"data row10 col4\" >0.4353</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row10_col5\" class=\"data row10 col5\" >0.3054</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row11_col0\" class=\"data row11 col0\" >319.8</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row11_col1\" class=\"data row11 col1\" >4.717e+06</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row11_col2\" class=\"data row11 col2\" >537.9</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row11_col3\" class=\"data row11 col3\" >0.0295</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row11_col4\" class=\"data row11 col4\" >0.054</td>\n",
       "                        <td id=\"T_26d22408_e02d_11ea_8042_a01d48db95d2row11_col5\" class=\"data row11 col5\" >0.0415</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x29e78c9d908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbr = create_model('gbr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing model\n",
    "We can see that the predicted accuracy for train set is around 91% and for test set it is 84%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(gbr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating pickle file\n",
    "Pickel file is created to store the model so that it can be used when calling in flask framework."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    }
   ],
   "source": [
    "save_model(gbr, 'store.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}