hospital_charges_prediction.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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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",
    "a = get_data('insurance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycaret.regression import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Setup Succesfully Completed!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
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       "        }    #T_91c9fff6_41e3_11eb_9ff9_809133c209a8row39_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }</style><table id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8\" ><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_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row0_col1\" class=\"data row0 col1\" >5228</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row1_col0\" class=\"data row1 col0\" >Transform Target </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row1_col1\" class=\"data row1 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row2_col0\" class=\"data row2 col0\" >Transform Target Method</td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row2_col1\" class=\"data row2 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row3_col1\" class=\"data row3 col1\" >(1338, 7)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row4_col1\" class=\"data row4 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row5_col1\" class=\"data row5 col1\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row6_col1\" class=\"data row6 col1\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row9_col1\" class=\"data row9 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row10_col1\" class=\"data row10 col1\" >(1338, 7)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row11_col1\" class=\"data row11 col1\" >(936, 61)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row12_col1\" class=\"data row12 col1\" >(402, 61)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row13_col1\" class=\"data row13 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row14_col1\" class=\"data row14 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row15_col1\" class=\"data row15 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row16_col1\" class=\"data row16 col1\" >zscore</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row18_col1\" class=\"data row18 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row19_col0\" class=\"data row19 col0\" >PCA </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row20_col1\" class=\"data row20 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row22_col1\" class=\"data row22 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row24_col1\" class=\"data row24 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row25_col1\" class=\"data row25 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row26_col1\" class=\"data row26 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row27_col1\" class=\"data row27 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row28_col1\" class=\"data row28 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row29_col1\" class=\"data row29 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row30_col1\" class=\"data row30 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row31_col1\" class=\"data row31 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row32_col1\" class=\"data row32 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row33_col1\" class=\"data row33 col1\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row34_col1\" class=\"data row34 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row35_col1\" class=\"data row35 col1\" >0.1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row38_col1\" class=\"data row38 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row39_col1\" class=\"data row39 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
       "                        <td id=\"T_91c9fff6_41e3_11eb_9ff9_809133c209a8row41_col1\" class=\"data row41 col1\" >0.01</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2644e8dd708>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = setup(a, target = 'charges',\n",
    "           normalize = True,\n",
    "           polynomial_features = True, trigonometry_features = True, feature_interaction=True, \n",
    "           bin_numeric_features= ['age', 'bmi'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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_sex_female',\n",
       "       'age_Power2_multiply_smoker_yes',\n",
       "       'region_southeast_multiply_bmi_Power2',\n",
       "       'smoker_yes_multiply_children_2', 'smoker_yes_multiply_bmi_6.0',\n",
       "       'children_0_multiply_age_Power2', 'children_2_multiply_age_Power2',\n",
       "       'bmi_Power2_multiply_smoker_yes', 'age_Power2_multiply_bmi_Power2',\n",
       "       'smoker_yes_multiply_age_Power2', 'bmi_7.0_multiply_smoker_yes',\n",
       "       'children_1_multiply_age_Power2', 'smoker_yes_multiply_bmi_Power2',\n",
       "       'children_0_multiply_bmi_Power2',\n",
       "       'region_northeast_multiply_smoker_yes',\n",
       "       'smoker_yes_multiply_children_0', 'bmi_6.0_multiply_smoker_yes',\n",
       "       'region_southeast_multiply_smoker_yes',\n",
       "       'bmi_Power2_multiply_age_Power2',\n",
       "       'region_southeast_multiply_age_Power2',\n",
       "       'sex_female_multiply_age_Power2',\n",
       "       'smoker_yes_multiply_region_southeast',\n",
       "       'sex_female_multiply_bmi_Power2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0].columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "      <td>0</td>\n",
       "      <td>3442.9251</td>\n",
       "      <td>3.548155e+07</td>\n",
       "      <td>5956.6392</td>\n",
       "      <td>0.8184</td>\n",
       "      <td>0.6490</td>\n",
       "      <td>0.4879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2422.6604</td>\n",
       "      <td>1.528984e+07</td>\n",
       "      <td>3910.2221</td>\n",
       "      <td>0.8817</td>\n",
       "      <td>0.3802</td>\n",
       "      <td>0.2832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2889.8884</td>\n",
       "      <td>1.918430e+07</td>\n",
       "      <td>4379.9891</td>\n",
       "      <td>0.9051</td>\n",
       "      <td>0.4156</td>\n",
       "      <td>0.3184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2763.7293</td>\n",
       "      <td>2.719817e+07</td>\n",
       "      <td>5215.1863</td>\n",
       "      <td>0.8105</td>\n",
       "      <td>0.4016</td>\n",
       "      <td>0.2803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2777.3475</td>\n",
       "      <td>2.165006e+07</td>\n",
       "      <td>4652.9620</td>\n",
       "      <td>0.8790</td>\n",
       "      <td>0.3675</td>\n",
       "      <td>0.2982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3116.9025</td>\n",
       "      <td>2.819339e+07</td>\n",
       "      <td>5309.7450</td>\n",
       "      <td>0.8286</td>\n",
       "      <td>0.3975</td>\n",
       "      <td>0.2410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3179.7589</td>\n",
       "      <td>2.413801e+07</td>\n",
       "      <td>4913.0451</td>\n",
       "      <td>0.7652</td>\n",
       "      <td>0.4572</td>\n",
       "      <td>0.3147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>3070.6889</td>\n",
       "      <td>2.435681e+07</td>\n",
       "      <td>4935.2615</td>\n",
       "      <td>0.8017</td>\n",
       "      <td>0.4225</td>\n",
       "      <td>0.3245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2870.0067</td>\n",
       "      <td>2.176377e+07</td>\n",
       "      <td>4665.1658</td>\n",
       "      <td>0.8513</td>\n",
       "      <td>0.4196</td>\n",
       "      <td>0.3703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>3038.2066</td>\n",
       "      <td>3.196191e+07</td>\n",
       "      <td>5653.4863</td>\n",
       "      <td>0.7726</td>\n",
       "      <td>0.5159</td>\n",
       "      <td>0.2406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mean</td>\n",
       "      <td>2957.2114</td>\n",
       "      <td>2.492178e+07</td>\n",
       "      <td>4959.1702</td>\n",
       "      <td>0.8314</td>\n",
       "      <td>0.4426</td>\n",
       "      <td>0.3159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SD</td>\n",
       "      <td>264.2067</td>\n",
       "      <td>5.694393e+06</td>\n",
       "      <td>573.0716</td>\n",
       "      <td>0.0447</td>\n",
       "      <td>0.0794</td>\n",
       "      <td>0.0682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MAE           MSE       RMSE      R2   RMSLE    MAPE\n",
       "0     3442.9251  3.548155e+07  5956.6392  0.8184  0.6490  0.4879\n",
       "1     2422.6604  1.528984e+07  3910.2221  0.8817  0.3802  0.2832\n",
       "2     2889.8884  1.918430e+07  4379.9891  0.9051  0.4156  0.3184\n",
       "3     2763.7293  2.719817e+07  5215.1863  0.8105  0.4016  0.2803\n",
       "4     2777.3475  2.165006e+07  4652.9620  0.8790  0.3675  0.2982\n",
       "5     3116.9025  2.819339e+07  5309.7450  0.8286  0.3975  0.2410\n",
       "6     3179.7589  2.413801e+07  4913.0451  0.7652  0.4572  0.3147\n",
       "7     3070.6889  2.435681e+07  4935.2615  0.8017  0.4225  0.3245\n",
       "8     2870.0067  2.176377e+07  4665.1658  0.8513  0.4196  0.3703\n",
       "9     3038.2066  3.196191e+07  5653.4863  0.7726  0.5159  0.2406\n",
       "Mean  2957.2114  2.492178e+07  4959.1702  0.8314  0.4426  0.3159\n",
       "SD     264.2067  5.694393e+06   573.0716  0.0447  0.0794  0.0682"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = create_model('lr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "870d877762984a2782a8e6ac5e732d73",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    }
   ],
   "source": [
    "save_model(lr, 'ahraz_major1_prediction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Sucessfully Loaded\n"
     ]
    }
   ],
   "source": [
    "ahraz_major1_prediction = load_model('ahraz_major1_prediction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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=5228, 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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ahraz_major1_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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