Insurance - Model Training Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": [
    "# Experiment 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycaret.regression import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Setup Succesfully Completed!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row0_col1\" class=\"data row0 col1\" >123</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row1_col0\" class=\"data row1 col0\" >Transform Target </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row1_col1\" class=\"data row1 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row2_col0\" class=\"data row2 col0\" >Transform Target Method</td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row2_col1\" class=\"data row2 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row3_col1\" class=\"data row3 col1\" >(1338, 7)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row4_col1\" class=\"data row4 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row5_col1\" class=\"data row5 col1\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row6_col1\" class=\"data row6 col1\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row9_col1\" class=\"data row9 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row10_col1\" class=\"data row10 col1\" >(1338, 7)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row11_col1\" class=\"data row11 col1\" >(936, 14)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row12_col1\" class=\"data row12 col1\" >(402, 14)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row13_col1\" class=\"data row13 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row14_col1\" class=\"data row14 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row15_col1\" class=\"data row15 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row16_col1\" class=\"data row16 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row18_col1\" class=\"data row18 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row19_col0\" class=\"data row19 col0\" >PCA </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row20_col1\" class=\"data row20 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row22_col1\" class=\"data row22 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row24_col1\" class=\"data row24 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row25_col1\" class=\"data row25 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row26_col1\" class=\"data row26 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row27_col1\" class=\"data row27 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row28_col1\" class=\"data row28 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row29_col1\" class=\"data row29 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row30_col1\" class=\"data row30 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row31_col1\" class=\"data row31 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row32_col1\" class=\"data row32 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row35_col1\" class=\"data row35 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row38_col1\" class=\"data row38 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row39_col1\" class=\"data row39 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
       "                        <td id=\"T_4a34d3c0_8afd_11ea_9671_84fdd13f47d7row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1e38f072748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = setup(data, target = 'charges', session_id = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4165.9659</td>\n",
       "      <td>3.330203e+07</td>\n",
       "      <td>5770.7909</td>\n",
       "      <td>0.8011</td>\n",
       "      <td>0.4683</td>\n",
       "      <td>0.4153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4503.7366</td>\n",
       "      <td>4.374648e+07</td>\n",
       "      <td>6614.1122</td>\n",
       "      <td>0.7456</td>\n",
       "      <td>0.5633</td>\n",
       "      <td>0.4217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3880.5528</td>\n",
       "      <td>3.179514e+07</td>\n",
       "      <td>5638.7179</td>\n",
       "      <td>0.5974</td>\n",
       "      <td>0.7645</td>\n",
       "      <td>0.4396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3747.6457</td>\n",
       "      <td>2.680530e+07</td>\n",
       "      <td>5177.3833</td>\n",
       "      <td>0.7762</td>\n",
       "      <td>0.5015</td>\n",
       "      <td>0.5175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4471.0419</td>\n",
       "      <td>4.341053e+07</td>\n",
       "      <td>6588.6670</td>\n",
       "      <td>0.6771</td>\n",
       "      <td>0.5224</td>\n",
       "      <td>0.3767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4182.7551</td>\n",
       "      <td>3.616633e+07</td>\n",
       "      <td>6013.8450</td>\n",
       "      <td>0.7674</td>\n",
       "      <td>0.7416</td>\n",
       "      <td>0.4320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4081.1022</td>\n",
       "      <td>3.919259e+07</td>\n",
       "      <td>6260.3984</td>\n",
       "      <td>0.7333</td>\n",
       "      <td>0.6434</td>\n",
       "      <td>0.4241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4928.1534</td>\n",
       "      <td>4.641504e+07</td>\n",
       "      <td>6812.8581</td>\n",
       "      <td>0.7448</td>\n",
       "      <td>0.5887</td>\n",
       "      <td>0.4137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4609.3147</td>\n",
       "      <td>4.037035e+07</td>\n",
       "      <td>6353.7670</td>\n",
       "      <td>0.7392</td>\n",
       "      <td>0.5686</td>\n",
       "      <td>0.5111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4665.8647</td>\n",
       "      <td>4.259679e+07</td>\n",
       "      <td>6526.6220</td>\n",
       "      <td>0.7256</td>\n",
       "      <td>0.8131</td>\n",
       "      <td>0.4802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>4323.6133</td>\n",
       "      <td>3.838006e+07</td>\n",
       "      <td>6175.7162</td>\n",
       "      <td>0.7308</td>\n",
       "      <td>0.6175</td>\n",
       "      <td>0.4432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>353.5472</td>\n",
       "      <td>5.908389e+06</td>\n",
       "      <td>490.4977</td>\n",
       "      <td>0.0543</td>\n",
       "      <td>0.1126</td>\n",
       "      <td>0.0431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MAE           MSE       RMSE      R2   RMSLE    MAPE\n",
       "0     4165.9659  3.330203e+07  5770.7909  0.8011  0.4683  0.4153\n",
       "1     4503.7366  4.374648e+07  6614.1122  0.7456  0.5633  0.4217\n",
       "2     3880.5528  3.179514e+07  5638.7179  0.5974  0.7645  0.4396\n",
       "3     3747.6457  2.680530e+07  5177.3833  0.7762  0.5015  0.5175\n",
       "4     4471.0419  4.341053e+07  6588.6670  0.6771  0.5224  0.3767\n",
       "5     4182.7551  3.616633e+07  6013.8450  0.7674  0.7416  0.4320\n",
       "6     4081.1022  3.919259e+07  6260.3984  0.7333  0.6434  0.4241\n",
       "7     4928.1534  4.641504e+07  6812.8581  0.7448  0.5887  0.4137\n",
       "8     4609.3147  4.037035e+07  6353.7670  0.7392  0.5686  0.5111\n",
       "9     4665.8647  4.259679e+07  6526.6220  0.7256  0.8131  0.4802\n",
       "Mean  4323.6133  3.838006e+07  6175.7162  0.7308  0.6175  0.4432\n",
       "SD     353.5472  5.908389e+06   490.4977  0.0543  0.1126  0.0431"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = create_model('lr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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(lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Setup Succesfully Completed!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row15_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row25_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row32_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row34_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row39_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }</style><table id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row0_col1\" class=\"data row0 col1\" >123</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row1_col0\" class=\"data row1 col0\" >Transform Target </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row1_col1\" class=\"data row1 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row2_col0\" class=\"data row2 col0\" >Transform Target Method</td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row2_col1\" class=\"data row2 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row3_col1\" class=\"data row3 col1\" >(1338, 7)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row4_col1\" class=\"data row4 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row5_col1\" class=\"data row5 col1\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row6_col1\" class=\"data row6 col1\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row9_col1\" class=\"data row9 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row10_col1\" class=\"data row10 col1\" >(1338, 7)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row11_col1\" class=\"data row11 col1\" >(936, 62)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row12_col1\" class=\"data row12 col1\" >(402, 62)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row13_col1\" class=\"data row13 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row14_col1\" class=\"data row14 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row15_col1\" class=\"data row15 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row16_col1\" class=\"data row16 col1\" >zscore</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row18_col1\" class=\"data row18 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row19_col0\" class=\"data row19 col0\" >PCA </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row20_col1\" class=\"data row20 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row22_col1\" class=\"data row22 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row24_col1\" class=\"data row24 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row25_col1\" class=\"data row25 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row26_col1\" class=\"data row26 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row27_col1\" class=\"data row27 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row28_col1\" class=\"data row28 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row29_col1\" class=\"data row29 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row30_col1\" class=\"data row30 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row31_col1\" class=\"data row31 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row32_col1\" class=\"data row32 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row33_col1\" class=\"data row33 col1\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row34_col1\" class=\"data row34 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row35_col1\" class=\"data row35 col1\" >0.1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row38_col1\" class=\"data row38 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row39_col1\" class=\"data row39 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
       "                        <td id=\"T_95dffd6e_8afd_11ea_8337_84fdd13f47d7row41_col1\" class=\"data row41 col1\" >0.01</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1e38f079b88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s2 = setup(data, target = 'charges', session_id = 123,\n",
    "           normalize = True,\n",
    "           polynomial_features = True, trigonometry_features = True, feature_interaction=True, \n",
    "           bin_numeric_features= ['age', 'bmi'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age_Power2', 'bmi_Power2', 'sex_female', 'children_0', 'children_1',\n",
       "       'children_2', 'children_3', 'children_4', 'children_5', 'smoker_yes',\n",
       "       'region_northeast', 'region_northwest', 'region_southeast',\n",
       "       'region_southwest', 'age_0.0', 'age_1.0', 'age_10.0', 'age_11.0',\n",
       "       'age_2.0', 'age_3.0', 'age_4.0', 'age_5.0', 'age_6.0', 'age_7.0',\n",
       "       'age_8.0', 'age_9.0', 'bmi_0.0', 'bmi_1.0', 'bmi_10.0', 'bmi_11.0',\n",
       "       'bmi_2.0', 'bmi_3.0', 'bmi_4.0', 'bmi_5.0', 'bmi_6.0', 'bmi_7.0',\n",
       "       'bmi_8.0', 'bmi_9.0', 'smoker_yes_multiply_bmi_5.0',\n",
       "       'region_southeast_multiply_age_Power2',\n",
       "       'bmi_Power2_multiply_smoker_yes', 'age_Power2_multiply_smoker_yes',\n",
       "       'children_0_multiply_bmi_Power2',\n",
       "       'region_southeast_multiply_bmi_Power2',\n",
       "       'children_0_multiply_smoker_yes', 'sex_female_multiply_bmi_Power2',\n",
       "       'region_southeast_multiply_smoker_yes',\n",
       "       'smoker_yes_multiply_children_0', 'smoker_yes_multiply_bmi_Power2',\n",
       "       'children_0_multiply_age_Power2', 'children_1_multiply_smoker_yes',\n",
       "       'sex_female_multiply_smoker_yes', 'smoker_yes_multiply_children_2',\n",
       "       'children_2_multiply_age_Power2', 'children_1_multiply_age_Power2',\n",
       "       'region_southwest_multiply_smoker_yes',\n",
       "       'sex_female_multiply_age_Power2', 'bmi_7.0_multiply_smoker_yes',\n",
       "       'bmi_Power2_multiply_age_Power2', 'bmi_6.0_multiply_smoker_yes',\n",
       "       'smoker_yes_multiply_age_Power2', 'smoker_yes_multiply_sex_female'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2[0].columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2476.5333</td>\n",
       "      <td>1.792665e+07</td>\n",
       "      <td>4233.9880</td>\n",
       "      <td>0.8929</td>\n",
       "      <td>0.3878</td>\n",
       "      <td>0.2842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3355.1345</td>\n",
       "      <td>3.404316e+07</td>\n",
       "      <td>5834.6515</td>\n",
       "      <td>0.8021</td>\n",
       "      <td>0.4613</td>\n",
       "      <td>0.3304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2919.7578</td>\n",
       "      <td>2.261994e+07</td>\n",
       "      <td>4756.0423</td>\n",
       "      <td>0.7136</td>\n",
       "      <td>0.5533</td>\n",
       "      <td>0.3790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2964.9649</td>\n",
       "      <td>2.283861e+07</td>\n",
       "      <td>4778.9761</td>\n",
       "      <td>0.8093</td>\n",
       "      <td>0.5456</td>\n",
       "      <td>0.4029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3097.0904</td>\n",
       "      <td>2.838265e+07</td>\n",
       "      <td>5327.5369</td>\n",
       "      <td>0.7889</td>\n",
       "      <td>0.5248</td>\n",
       "      <td>0.2831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2979.1330</td>\n",
       "      <td>2.035337e+07</td>\n",
       "      <td>4511.4710</td>\n",
       "      <td>0.8691</td>\n",
       "      <td>0.3543</td>\n",
       "      <td>0.2744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2687.7389</td>\n",
       "      <td>2.234181e+07</td>\n",
       "      <td>4726.7128</td>\n",
       "      <td>0.8480</td>\n",
       "      <td>0.4020</td>\n",
       "      <td>0.3279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3123.5060</td>\n",
       "      <td>2.697542e+07</td>\n",
       "      <td>5193.7863</td>\n",
       "      <td>0.8517</td>\n",
       "      <td>0.4707</td>\n",
       "      <td>0.3089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2813.0890</td>\n",
       "      <td>2.055905e+07</td>\n",
       "      <td>4534.2087</td>\n",
       "      <td>0.8672</td>\n",
       "      <td>0.3759</td>\n",
       "      <td>0.3151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3110.4590</td>\n",
       "      <td>2.651683e+07</td>\n",
       "      <td>5149.4496</td>\n",
       "      <td>0.8292</td>\n",
       "      <td>0.4661</td>\n",
       "      <td>0.3353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>2952.7407</td>\n",
       "      <td>2.425575e+07</td>\n",
       "      <td>4904.6823</td>\n",
       "      <td>0.8272</td>\n",
       "      <td>0.4542</td>\n",
       "      <td>0.3241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>235.6330</td>\n",
       "      <td>4.501474e+06</td>\n",
       "      <td>447.0354</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.0685</td>\n",
       "      <td>0.0393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MAE           MSE       RMSE      R2   RMSLE    MAPE\n",
       "0     2476.5333  1.792665e+07  4233.9880  0.8929  0.3878  0.2842\n",
       "1     3355.1345  3.404316e+07  5834.6515  0.8021  0.4613  0.3304\n",
       "2     2919.7578  2.261994e+07  4756.0423  0.7136  0.5533  0.3790\n",
       "3     2964.9649  2.283861e+07  4778.9761  0.8093  0.5456  0.4029\n",
       "4     3097.0904  2.838265e+07  5327.5369  0.7889  0.5248  0.2831\n",
       "5     2979.1330  2.035337e+07  4511.4710  0.8691  0.3543  0.2744\n",
       "6     2687.7389  2.234181e+07  4726.7128  0.8480  0.4020  0.3279\n",
       "7     3123.5060  2.697542e+07  5193.7863  0.8517  0.4707  0.3089\n",
       "8     2813.0890  2.055905e+07  4534.2087  0.8672  0.3759  0.3151\n",
       "9     3110.4590  2.651683e+07  5149.4496  0.8292  0.4661  0.3353\n",
       "Mean  2952.7407  2.425575e+07  4904.6823  0.8272  0.4542  0.3241\n",
       "SD     235.6330  4.501474e+06   447.0354  0.0490  0.0685  0.0393"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = create_model('lr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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(lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    }
   ],
   "source": [
    "save_model(lr, 'deployment_28042020')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Sucessfully Loaded\n"
     ]
    }
   ],
   "source": [
    "deployment_28042020 = load_model('deployment_28042020')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Pipeline(memory=None,\n",
       "          steps=[('dtypes',\n",
       "                  DataTypes_Auto_infer(categorical_features=[],\n",
       "                                       display_types=True, features_todrop=[],\n",
       "                                       ml_usecase='regression',\n",
       "                                       numerical_features=[], target='charges',\n",
       "                                       time_features=[])),\n",
       "                 ('imputer',\n",
       "                  Simple_Imputer(categorical_strategy='not_available',\n",
       "                                 numeric_strategy='mean',\n",
       "                                 target_variable=None)),\n",
       "                 ('new_levels1',\n",
       "                  New_Catagorical_Levels...\n",
       "                 ('dummy', Dummify(target='charges')),\n",
       "                 ('fix_perfect', Remove_100(target='charges')),\n",
       "                 ('clean_names', Clean_Colum_Names()),\n",
       "                 ('feature_select', Empty()), ('fix_multi', Empty()),\n",
       "                 ('dfs',\n",
       "                  DFS_Classic(interactions=['multiply'], ml_usecase='regression',\n",
       "                              random_state=123, subclass='binary',\n",
       "                              target='charges',\n",
       "                              top_features_to_pick_percentage=None)),\n",
       "                 ('pca', Empty())],\n",
       "          verbose=False),\n",
       " LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False),\n",
       " None]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deployment_28042020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75714.0\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "url = 'https://pycaret-insurance.herokuapp.com/predict_api'\n",
    "pred = requests.post(url,json={'age':55, 'sex':'male', 'bmi':59, 'children':1, 'smoker':'male', 'region':'northwest'})\n",
    "print(pred.json())"
   ]
  }
 ],
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