0005_PyCaret.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center;margin-bottom:50px;\">\n",
    "    <img src=\"img/LogoSingleRow.png\" style=\"max-width:600px;\">\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PyCaret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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_38efb7ea_bddb_11ea_9ce1_acde48001122\" ><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_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row0_col1\" class=\"data row0 col1\" >2895</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row1_col0\" class=\"data row1 col0\" >Target Type</td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row1_col1\" class=\"data row1 col1\" >Binary</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row2_col0\" class=\"data row2 col0\" >Label Encoded</td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row2_col1\" class=\"data row2 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row3_col1\" class=\"data row3 col1\" >(768, 9)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row4_col1\" class=\"data row4 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row5_col1\" class=\"data row5 col1\" >7</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row6_col1\" class=\"data row6 col1\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row9_col1\" class=\"data row9 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row10_col1\" class=\"data row10 col1\" >(768, 9)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row11_col1\" class=\"data row11 col1\" >(537, 24)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row12_col1\" class=\"data row12 col1\" >(231, 24)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row13_col1\" class=\"data row13 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row14_col1\" class=\"data row14 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row15_col1\" class=\"data row15 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row16_col1\" class=\"data row16 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row18_col1\" class=\"data row18 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row19_col0\" class=\"data row19 col0\" >PCA </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row20_col1\" class=\"data row20 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row22_col1\" class=\"data row22 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row24_col1\" class=\"data row24 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row25_col1\" class=\"data row25 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row26_col1\" class=\"data row26 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row27_col1\" class=\"data row27 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row28_col1\" class=\"data row28 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row29_col1\" class=\"data row29 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row30_col1\" class=\"data row30 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row31_col1\" class=\"data row31 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row32_col1\" class=\"data row32 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row35_col1\" class=\"data row35 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row38_col1\" class=\"data row38 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row39_col1\" class=\"data row39 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
       "                        <td id=\"T_38efb7ea_bddb_11ea_9ce1_acde48001122row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9f5c68da10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.classification import * \n",
    "from pycaret.datasets import get_data\n",
    "\n",
    "diabetes = get_data('diabetes')\n",
    "exp_clf = setup(diabetes, target='Class variable')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "\n",
    "## Finding optimal model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122 th {\n",
       "          text-align: left;\n",
       "    }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row0_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row0_col1 {\n",
       "            background-color:  yellow;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row0_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row0_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row0_col4 {\n",
       "            background-color:  yellow;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row0_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row0_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row1_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row1_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row1_col2 {\n",
       "            background-color:  yellow;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row1_col3 {\n",
       "            background-color:  yellow;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row1_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row1_col5 {\n",
       "            background-color:  yellow;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row1_col6 {\n",
       "            background-color:  yellow;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row2_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row2_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row2_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row2_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row2_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row2_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row2_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row3_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row3_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row3_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row3_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row3_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row3_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row3_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row4_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row4_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row4_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row4_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row4_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row4_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row4_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row5_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row5_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row5_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row5_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row5_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row5_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row5_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row6_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row6_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row6_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row6_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row6_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row6_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row6_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row7_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row7_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row7_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row7_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row7_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row7_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row7_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row8_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row8_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row8_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row8_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row8_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row8_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row8_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row9_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row9_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row9_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row9_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row9_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row9_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row9_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row10_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row10_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row10_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row10_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row10_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row10_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row10_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row11_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row11_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row11_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row11_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row11_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row11_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row11_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row12_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row12_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row12_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row12_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row12_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row12_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row12_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row13_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row13_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row13_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row13_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row13_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row13_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row13_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row14_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row14_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row14_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row14_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row14_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row14_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_5a9d4d58_bddb_11ea_9ce1_acde48001122row14_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }</style><table id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >Accuracy</th>        <th class=\"col_heading level0 col2\" >AUC</th>        <th class=\"col_heading level0 col3\" >Recall</th>        <th class=\"col_heading level0 col4\" >Prec.</th>        <th class=\"col_heading level0 col5\" >F1</th>        <th class=\"col_heading level0 col6\" >Kappa</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row0_col0\" class=\"data row0 col0\" >Logistic Regression</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row0_col1\" class=\"data row0 col1\" >0.763700</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row0_col2\" class=\"data row0 col2\" >0.810400</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row0_col3\" class=\"data row0 col3\" >0.557600</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row0_col4\" class=\"data row0 col4\" >0.702500</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row0_col5\" class=\"data row0 col5\" >0.618700</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row0_col6\" class=\"data row0 col6\" >0.452100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row1_col0\" class=\"data row1 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row1_col1\" class=\"data row1 col1\" >0.760000</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row1_col2\" class=\"data row1 col2\" >0.824000</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row1_col3\" class=\"data row1 col3\" >0.617000</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row1_col4\" class=\"data row1 col4\" >0.666800</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row1_col5\" class=\"data row1 col5\" >0.635500</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row1_col6\" class=\"data row1 col6\" >0.458900</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row2_col0\" class=\"data row2 col0\" >Linear Discriminant Analysis</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row2_col1\" class=\"data row2 col1\" >0.756400</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row2_col2\" class=\"data row2 col2\" >0.814100</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row2_col3\" class=\"data row2 col3\" >0.541800</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row2_col4\" class=\"data row2 col4\" >0.684100</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row2_col5\" class=\"data row2 col5\" >0.601900</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row2_col6\" class=\"data row2 col6\" >0.431900</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row3_col0\" class=\"data row3 col0\" >Ridge Classifier</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row3_col1\" class=\"data row3 col1\" >0.752600</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row3_col2\" class=\"data row3 col2\" >0.000000</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row3_col3\" class=\"data row3 col3\" >0.531000</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row3_col4\" class=\"data row3 col4\" >0.681300</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row3_col5\" class=\"data row3 col5\" >0.594800</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row3_col6\" class=\"data row3 col6\" >0.422400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row4_col0\" class=\"data row4 col0\" >Gradient Boosting Classifier</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row4_col1\" class=\"data row4 col1\" >0.745200</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row4_col2\" class=\"data row4 col2\" >0.805400</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row4_col3\" class=\"data row4 col3\" >0.574000</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row4_col4\" class=\"data row4 col4\" >0.652600</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row4_col5\" class=\"data row4 col5\" >0.607200</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row4_col6\" class=\"data row4 col6\" >0.421100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row5_col0\" class=\"data row5 col0\" >Random Forest Classifier</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row5_col1\" class=\"data row5 col1\" >0.745100</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row5_col2\" class=\"data row5 col2\" >0.775300</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row5_col3\" class=\"data row5 col3\" >0.503800</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row5_col4\" class=\"data row5 col4\" >0.684000</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row5_col5\" class=\"data row5 col5\" >0.576300</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row5_col6\" class=\"data row5 col6\" >0.401700</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row6_col0\" class=\"data row6 col0\" >Ada Boost Classifier</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row6_col1\" class=\"data row6 col1\" >0.741300</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row6_col2\" class=\"data row6 col2\" >0.790100</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row6_col3\" class=\"data row6 col3\" >0.568100</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row6_col4\" class=\"data row6 col4\" >0.648200</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row6_col5\" class=\"data row6 col5\" >0.602300</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row6_col6\" class=\"data row6 col6\" >0.412800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row7_col0\" class=\"data row7 col0\" >Extra Trees Classifier</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row7_col1\" class=\"data row7 col1\" >0.735800</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row7_col2\" class=\"data row7 col2\" >0.787100</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row7_col3\" class=\"data row7 col3\" >0.503800</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row7_col4\" class=\"data row7 col4\" >0.650700</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row7_col5\" class=\"data row7 col5\" >0.562300</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row7_col6\" class=\"data row7 col6\" >0.381000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row8_col0\" class=\"data row8 col0\" >CatBoost Classifier</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row8_col1\" class=\"data row8 col1\" >0.732100</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row8_col2\" class=\"data row8 col2\" >0.801200</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row8_col3\" class=\"data row8 col3\" >0.579800</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row8_col4\" class=\"data row8 col4\" >0.622500</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row8_col5\" class=\"data row8 col5\" >0.595000</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row8_col6\" class=\"data row8 col6\" >0.397100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row9_col0\" class=\"data row9 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row9_col1\" class=\"data row9 col1\" >0.724600</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row9_col2\" class=\"data row9 col2\" >0.782800</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row9_col3\" class=\"data row9 col3\" >0.574300</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row9_col4\" class=\"data row9 col4\" >0.611400</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row9_col5\" class=\"data row9 col5\" >0.587400</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row9_col6\" class=\"data row9 col6\" >0.383000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row10_col0\" class=\"data row10 col0\" >K Neighbors Classifier</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row10_col1\" class=\"data row10 col1\" >0.722900</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row10_col2\" class=\"data row10 col2\" >0.764800</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row10_col3\" class=\"data row10 col3\" >0.530700</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row10_col4\" class=\"data row10 col4\" >0.621900</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row10_col5\" class=\"data row10 col5\" >0.571200</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row10_col6\" class=\"data row10 col6\" >0.368800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row11_col0\" class=\"data row11 col0\" >Decision Tree Classifier</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row11_col1\" class=\"data row11 col1\" >0.690800</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row11_col2\" class=\"data row11 col2\" >0.658600</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row11_col3\" class=\"data row11 col3\" >0.551500</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row11_col4\" class=\"data row11 col4\" >0.564700</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row11_col5\" class=\"data row11 col5\" >0.555700</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row11_col6\" class=\"data row11 col6\" >0.319400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row12_col0\" class=\"data row12 col0\" >Naive Bayes</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row12_col1\" class=\"data row12 col1\" >0.657500</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row12_col2\" class=\"data row12 col2\" >0.732600</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row12_col3\" class=\"data row12 col3\" >0.176900</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row12_col4\" class=\"data row12 col4\" >0.509200</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row12_col5\" class=\"data row12 col5\" >0.253500</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row12_col6\" class=\"data row12 col6\" >0.104900</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row13_col0\" class=\"data row13 col0\" >Quadratic Discriminant Analysis</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row13_col1\" class=\"data row13 col1\" >0.584500</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row13_col2\" class=\"data row13 col2\" >0.624800</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row13_col3\" class=\"data row13 col3\" >0.433000</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row13_col4\" class=\"data row13 col4\" >0.384300</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row13_col5\" class=\"data row13 col5\" >0.366500</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row13_col6\" class=\"data row13 col6\" >0.096300</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row14_col0\" class=\"data row14 col0\" >SVM - Linear Kernel</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row14_col1\" class=\"data row14 col1\" >0.536500</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row14_col2\" class=\"data row14 col2\" >0.000000</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row14_col3\" class=\"data row14 col3\" >0.460200</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row14_col4\" class=\"data row14 col4\" >0.340600</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row14_col5\" class=\"data row14 col5\" >0.336800</td>\n",
       "                        <td id=\"T_5a9d4d58_bddb_11ea_9ce1_acde48001122row14_col6\" class=\"data row14 col6\" >0.031300</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9f3e211dd0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Prec.</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.7778</td>\n",
       "      <td>0.8466</td>\n",
       "      <td>0.7368</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.7000</td>\n",
       "      <td>0.5242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.6852</td>\n",
       "      <td>0.7368</td>\n",
       "      <td>0.3684</td>\n",
       "      <td>0.5833</td>\n",
       "      <td>0.4516</td>\n",
       "      <td>0.2463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.7778</td>\n",
       "      <td>0.8090</td>\n",
       "      <td>0.5789</td>\n",
       "      <td>0.7333</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.4882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.7037</td>\n",
       "      <td>0.7789</td>\n",
       "      <td>0.5789</td>\n",
       "      <td>0.5789</td>\n",
       "      <td>0.5789</td>\n",
       "      <td>0.3504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.7963</td>\n",
       "      <td>0.8962</td>\n",
       "      <td>0.6842</td>\n",
       "      <td>0.7222</td>\n",
       "      <td>0.7027</td>\n",
       "      <td>0.5479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.6852</td>\n",
       "      <td>0.7429</td>\n",
       "      <td>0.4211</td>\n",
       "      <td>0.5714</td>\n",
       "      <td>0.4848</td>\n",
       "      <td>0.2656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.7778</td>\n",
       "      <td>0.8195</td>\n",
       "      <td>0.5789</td>\n",
       "      <td>0.7333</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.4882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.7925</td>\n",
       "      <td>0.8413</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.7059</td>\n",
       "      <td>0.6857</td>\n",
       "      <td>0.5310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.8302</td>\n",
       "      <td>0.8698</td>\n",
       "      <td>0.7778</td>\n",
       "      <td>0.7368</td>\n",
       "      <td>0.7568</td>\n",
       "      <td>0.6265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.7736</td>\n",
       "      <td>0.8984</td>\n",
       "      <td>0.7778</td>\n",
       "      <td>0.6364</td>\n",
       "      <td>0.7000</td>\n",
       "      <td>0.5211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.7600</td>\n",
       "      <td>0.8240</td>\n",
       "      <td>0.6170</td>\n",
       "      <td>0.6668</td>\n",
       "      <td>0.6355</td>\n",
       "      <td>0.4589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.0477</td>\n",
       "      <td>0.0549</td>\n",
       "      <td>0.1333</td>\n",
       "      <td>0.0656</td>\n",
       "      <td>0.0949</td>\n",
       "      <td>0.1206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Accuracy     AUC  Recall   Prec.      F1   Kappa\n",
       "0       0.7778  0.8466  0.7368  0.6667  0.7000  0.5242\n",
       "1       0.6852  0.7368  0.3684  0.5833  0.4516  0.2463\n",
       "2       0.7778  0.8090  0.5789  0.7333  0.6471  0.4882\n",
       "3       0.7037  0.7789  0.5789  0.5789  0.5789  0.3504\n",
       "4       0.7963  0.8962  0.6842  0.7222  0.7027  0.5479\n",
       "5       0.6852  0.7429  0.4211  0.5714  0.4848  0.2656\n",
       "6       0.7778  0.8195  0.5789  0.7333  0.6471  0.4882\n",
       "7       0.7925  0.8413  0.6667  0.7059  0.6857  0.5310\n",
       "8       0.8302  0.8698  0.7778  0.7368  0.7568  0.6265\n",
       "9       0.7736  0.8984  0.7778  0.6364  0.7000  0.5211\n",
       "Mean    0.7600  0.8240  0.6170  0.6668  0.6355  0.4589\n",
       "SD      0.0477  0.0549  0.1333  0.0656  0.0949  0.1206"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb = create_model('xgboost')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(xgb, 'confusion_matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAggAAAGACAYAAADI95pLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3deVxU9f7H8fcwLAqjIgFuibmElkSKSy6puZBbpGWpWdatXDL3wFIrtTKjrMwltayr2WL60zKrey0tzdxXDLypmRuaCokKAwjMzPn9YUzScU1hEl7Px4PHwznL93wOx8N5z/d854zFMAxDAAAAZ/HydAEAAOCfh4AAAABMCAgAAMCEgAAAAEwICAAAwISAAAAATAgIuGw7duxQgwYNlJiY6J6Wlpamdu3aaeXKle5pCxcu1P33369OnTqpXbt2evTRR7V9+3b3/N69e6tNmzbq0qWL7r77bnXs2FHTpk0rsK2tW7fq8ccfV5cuXRQTE6N+/fpp9+7dkqQNGzborrvuuur716VLF6Wnp8vpdGrAgAFq3769PvroI/f0K+F0OjV79mzde++96tKlizp16qSJEycqNzdXkjRy5Ei9//77V2M33L777juNHz9ekvTzzz+rXbt2uvfeezV37lz39L/jueeeU1JSkiTp2Wef1dq1a69KvRs2bFBkZKS6dOni/mnXrp2eeOIJnThx4qps42JWrlypyZMnF8m2gH8sA/gb5s2bZ7Ru3do4efKkkZeXZ/Tu3duYPn26e/4bb7xh9OzZ0zh06JB72tq1a43bbrvNOHz4sGEYhvHQQw8Z//3vf93zT506ZTRv3tzYvHmzYRiGsXHjRqNVq1ZGYmKie5kvvvjCaNy4sXH8+HFj/fr1RufOnQttHw8fPmxEREQYDofjqrX53HPPGYMHDzbS09MNwzCMzMxMY8CAAUZcXJxhGIbxzDPPGO+9995V295fTZ061Rg9evRVaat169bGTz/9dFXaOtu5jqvD4TAGDBhgvP7661d9e+cyZcoU44UXXiiSbQH/VN6eDii4NvXs2VNbtmzR6NGjFRYWprJly+qJJ56QJP3+++/64IMPtGzZMoWGhrrXadq0qUaOHKns7OxztpmZmSlJKl++vCRpypQpevLJJxUREeFe5u6775afn5+cTmeBdfft26cXX3xRmZmZSk1NVZ06dfTWW2/Jz89PU6ZM0bJly+Tj46Py5cvrlVdeUWho6Hmn165dWytXrlSfPn3kcDh07733aurUqYqOjta6desUFBSk//u//9O8efPkcrkUGBio559/XjVr1tTIkSN18uRJJScn64477tCIESPcNR46dEhffvmlVq9eLZvNJkny9/fXCy+8oK1bt5p+HwsXLtT8+fOVl5enU6dOqW/fvurVq5dSU1P1zDPPuN9Nt2rVSsOGDTvv9M8++0zffPONOnfurHnz5snpdOr06dNq3ry5vvnmG73zzjtKTU3V2LFjtXfvXnl5ealnz556+OGHlZCQ4O7hSE1NVbNmzTRhwgRNmjRJKSkpiouL02uvvabXX39dDz74oDp06KDly5dr2rRpcrlcCggI0KhRoxQZGampU6fq8OHDSk1N1eHDh1WhQgVNnDixwP+R87Hb7UpLS1NUVJQkKSMjQy+//LJ2796tvLw8NW3aVE8//bS8vb118803q2/fvvrxxx+VlZWlp556Snfeeack6e2339bXX38tq9Wq6tWr6/nnn1dISIh69+6tcuXKae/everUqZM+/fRTOZ1OlSlTRsOHD79ofUCx5OmEgmtXZmamER0dbbRu3dqw2+3u6cuWLTPuueeei67/0EMPGa1btzbuvvtuo1OnTkbdunWNESNGGC6XyzAMw6hXr57xyy+/nHf9s99pxsfHG4sXLzYMwzByc3ONu+66y1i6dKnx22+/GVFRUUZOTo5hGIbx/vvvG8uWLTvvdMMwjPDwcOP48eNGcnKyUa9ePff28qdv2LDB6NWrl5GVlWUYhmH8+OOPRocOHQzDONMD8Mgjj5yz3qVLlxrdunW74O8kvwfBbrcb3bt3N9LS0gzDMIxt27a5a5k2bZrx/PPPG4Zx5hgMGzbMSE9PP+/0RYsWGf369TMMo+A747OnDxw40Hj11VcNwzCM9PR0o3Pnzsb+/fuN4cOHG+vXrzcMwzDsdrtx2223uXt0zu5ByO8N2rNnj9GsWTPj4MGDhmGc6TVq3ry5kZGRYUyZMsVo27atkZGRYRiGYfTv39+YPHmy6Xewfv1645ZbbnH/v2jSpInRtWtX45133jFyc3MNwzCMkSNHGnPnzjUM40zvQlxcnPHuu++6j9OMGTMMwzCMn3/+2WjQoIFx/PhxY+HChUaPHj2MzMxM9+/isccec9c/atQodw30IAD0IOAK7Nu3T5mZmcrJydGOHTvUuHFjSZLxl6d32+12Pfjgg5KkrKwsdezYUU899ZQk6emnn1aHDh0knRnH0K9fP7377rvq37+/vLy85HK5LqmWESNGaM2aNZo1a5b279+vlJQUZWVlqUKFCqpTp47uuecetWzZUi1btlTTpk3lcrnOOf1SrFy5UgcOHFDPnj3d09LT03Xy5ElJUoMGDc653uXsT0BAgGbOnKkffvhB+/fv186dO5WVlSVJatGihfr166cjR46oWbNmio2NVZkyZc47/VKsXbvW3dtRpkwZffXVV5Kk+Ph4rVq1SjNnztTevXuVk5PjruNc1q9fryZNmqhq1aqSzvQaBQUFuccqNG7c2N17cvPNN+vUqVPnbCcsLExffPGFJGnRokWaNGmSOnbsKB8fH0lnjkFiYqIWLlwoSTp9+nSB9R966CFJUp06dRQeHq5NmzZp1apVuvfee+Xv7y9JevjhhzVz5kz3+I+GDRte0u8KKCkICPhb0tLSNHjwYI0aNUo5OTl66qmn9PnnnyskJESRkZHat2+fTpw4ofLly8tms7n/2E+dOvW8A82CgoJ01113afXq1erfv7/q1aun7du3Kzw8vMByL7zwgqKjo2W1Wt3TnnrqKTmdTnXs2FF33HGHjhw5IsMw5OXlpY8++kiJiYlat26dJkyYoBYtWujpp58+7/SLcblc6tKli/uC6nK5lJKSonLlykmS+wL0V5GRkdq7d6/sdrv7IilJx44d0/PPP68pU6a4px09elQ9evRQ9+7d1aBBA3Xo0EErVqxwt/Pdd99p3bp1Wr9+ve6//37NmjXrvNMvhbe3tywWi/t1cnKyypcvr8cee0y1a9dWixYt1LFjR23fvt0UAP/6uzm7HelMYHQ4HJKkUqVKuadbLJYLtpWvW7du2r59u4YOHaoFCxbI29tbLpdLkydPVs2aNSWdCWhnb/fs/xsul0tWq9VUm8vlctclnf+4ASUVn2LAZXM6nRo+fLhat26tu+66S926dVOLFi00fPhwOZ1OVahQQQ8//LCGDh2q3377zb3e4cOHtXXrVnl5nfu/XV5entasWaPIyEhJ0oABAzRt2jT3u09J7vvpfw0Nq1ev1sCBA9WpUydJ0vbt2+V0OrVz507dddddqlmzpvr3769//etfSkxMPO/0S3H77bfr66+/VkpKiiRp3rx5euSRRy66XoUKFRQTE6PRo0fLbrdLOtO7Mm7cOAUGBha4eCYlJSkoKEhPPvmkbr/9dnc4cDqdev311zV9+nS1a9dOzz77rGrVqqVffvnlvNMvRdOmTbVo0SJJZ+7vP/LII9q/f78SExMVFxenO++8U0ePHtXBgwfdvSBWq7XABTa/ndWrVys5OVmStG7dOh05ckS33nrrJdVxPnFxcTpy5Ig+/vhjSWeOwZw5c2QYhnJzczVgwAB99NFH7uUXL14s6cwnbvbt26dGjRqpRYsWWrRokbsH5MMPP1SjRo3k6+tr2t659g0oaehBwGV77bXXlJ2drWeeecY9bcyYMerevbvefPNNjRgxQsOHD9eSJUsUGxur7OxsZWRkqFy5curUqZP7dkN+WzNmzJDFYlF2draaNGniHuzYsGFDjR8/Xi+//LKysrKUl5ensLAwzZ07V8HBwfr111/d7QwfPlwDBw6Uv7+/bDabGjVqpIMHD+r+++9Xx44d1a1bN/n7+6tUqVJ67rnnVKdOnXNOvxS33367+vbtq8cee0wWi0U2m03Tpk0zvXM+l7Fjx2r69Onq2bOnrFarcnNz1a5dOw0ePLjAcs2bN9fChQvVoUMHWSwWNW7cWEFBQTpw4IAeeeQRjRw5UnfddZd8fX1Vu3Ztde7cWadOnTrn9PzbBRcyZswYjRs3TjExMTIMQ/3791dERIT69eune+65R/7+/qpQoYKioqJ04MABNW3aVNHR0RoxYoTGjRvnbqdWrVoaO3asBg0aJKfTqVKlSmnmzJmXfKvjfMqWLau4uDi98sor6ty5s5599lm9/PLLiomJUV5enpo1a6Y+ffq4l9+6dasWLFggl8ulSZMmqVy5crrvvvt05MgR3X///XK5XKpWrZpef/31c26vSZMmiouL00svvaTnn3/+imoHrlUW41L6+ADgGlG7dm33p00A/H3cYgAAACb0IAAAABN6EAAAKCa2b9+u3r17m6Z///336tatm3r06KEFCxZcUlsMUgQAoBiYNWuWlixZotKlSxeYnpeXp1deeUULFy5U6dKl9cADD6h169YKCQm5YHtXNSC4XC5lZmbKx8fnkkZ0AwDgSYZhKC8vTwEBAef9CPbV5HA4TI+Kv1RWq1Xe3ue/bIeFhWnq1Kmm57n8+uuvCgsLcz+rpUGDBtq8ebM6dux4we1d1YCQmZnp/qY9AACuFeHh4Vf8cdyLcTgc2rRqjXzL2S6+8DlYrVZFREScNyS0b99ehw4dMk232+0F9i0gIMD9LJYLuaoBIf8xqOWfGSXr8eNXs2kUgorr18rZ905Pl4FLYJ31rY42aebpMnCJKq5fK+O7PhdfEB6X12K6du/e7b5+FSan0ynfcjatefwFnU65vGtkqdDr1Pz9sXI6nRfsRTgXm83m/jI86cyb+UsJQ1c1IOTfVrAePy7rH0+Zwz+Xn5+fnKd+93QZuARWPz/OqWuIn5+fDOdJT5eBS2D540maRXlb/HTKcWUfKbq/vTVr1tSBAwd08uRJ+fv7a/PmzXr88ccvuh6DFAEAKIa+/PJLZWVlqUePHho5cqQef/xxGYahbt26qUKFChddn4AAAEAxcf3117s/xhgTE+Oe3qZNG7Vp0+ay2uI5CAAAwISAAAAATAgIAADAhIAAAABMCAgAAMCEgAAAAEwICAAAwISAAAAATAgIAADAhIAAAABMCAgAAMCEgAAAAEwICAAAwISAAAAATAgIAADAhIAAAABMCAgAAMCEgAAAAEwICAAAwISAAAAATAgIAADAhIAAAABMCAgAAMCEgAAAAEwICAAAwISAAAAATAgIAADAhIAAAABMCAgAAMCEgAAAAEy8PV0AAAAlSSNJxmWuYymMQi6CHgQAAGBCQAAAACYEBAAAYEJAAAAAJgQEAABgQkAAAAAmBAQAAGBCQAAAACYEBAAAYEJAAAAAJgQEAABgQkAAAAAmBAQAAGBCQAAAACYEBAAAYEJAAAAAJgQEAABgQkAAAAAmBAQAAGBCQAAAACYEBAAAYEJAAAAAJgQEAABgQkAAAAAmBAQAAIoBl8ulMWPGqEePHurdu7cOHDhQYP7777+ve++9V926ddOyZcsu2p53YRUKAACKzvLly5Wbm6v58+crISFB8fHxmjFjhiQpPT1dH374ob799ltlZ2era9euio6OvmB79CAAAFAMbNmyRS1atJAk1atXT0lJSe55pUuXVuXKlZWdna3s7GxZLJaLtkcPAgAAxYDdbpfNZnO/tlqtcjgc8vY+c6mvVKmSOnfuLKfTqf79+1+0PXoQAAAoBmw2mzIzM92vXS6XOxysWrVKKSkp+u6777Ry5UotX75cP/300wXbIyAAAFAMREVFadWqVZKkhIQEhYeHu+eVK1dOpUqVkq+vr/z8/FSmTBmlp6dfsD1uMQAAUAxER0drzZo16tmzpwzD0IQJEzR79myFhYWpbdu2Wrt2rbp37y4vLy9FRUWpefPmF2yPgAAAQDHg5eWlF198scC0mjVruv89ZMgQDRky5NLbu2qVAQCAYoOAAAAATLjFAABAEapR4bSslqzLWscZelq/F1I950MPAgAAMCEgAAAAEwICAAAwKXFjEEq1baOyI0fK4uervJ9/1onYETLs9gLLeNepo/IvvShL2TKS06UTz4xUXmKi5OurwJdekF/z5jIyM3V62XKlv/GmLGXKKGThggJt+NSpo1PjX5b93VlFuXvFytfHMvTc/1KU4zJ0S1k/zapXWWV9rAWWSUw/raGJR5We55LVIk2/tZIaBJaWJDX+Ya+ynS75ep155vgD15dTXK1g97q5LkOtVu/TfZXLKvas6fh7ruTcKvfiC/Jrcpt7OWvFinIeS1FK9J3yrlNHoUsWy7F/v3t+2oAn5fh1b1HtWon0+bojGvfJbnl5WRRk89G7gyJVs1KAJCk5NVtNR6xWwpRWCi7r6+FKUVguqQdh+/bt6t27d2HXUui8goJU/s03lNavn461vEPOAwdVbvSoAstYSpVSyCcfKWPGDKW076j0tyYraNoUSVLZwYPkXeV6HWsbrZT2HeUVGqqARx6WkZ6ulDs7uH8y532q3I2bZP/3bE/sZrGQmuNQn22/aUGj6/W/trVUPcBXo39OKbBMlsOljusOKq7Wddp8Rw09Gx6ih7celiRlOlz6NTNXW++oqS1//MT9JQQ8lXRU+7LyimyfirMrPbdOjRnrPn+OP9ZHRk6OTgwbJknya9hAWZ8vLnCOEQ4KV3aOU73fTNCiUQ21bXJL3dW4gobO2iFJmvv9IbUatVa/peV4uEoUtosGhFmzZum5555TTs61/5/Br1VL5W7fLse+/ZIk+9wP5X9P178s00qOAwd0+vsVkqTT336rtCcGSJJ8Im9R1pIl0h+/i9PffCP/zp0LrG+94QaVHTpEaUOGSg5HIe9R8bUsNVMNA0vrRpufJOmJG8rrk0OnZBjGWcvYVSPAR50qlJEkxVS0aV6D6yVJG09my+btpU7rD6jeil/1VNJRZTtd7nU/Sj6pU3lOdapgE67clZ5bZys/8VXZ352lvB3/kyT5NmwgnxtvVOjS/yj0qy9VqmOHwt0ZyOkyZBiGTv0RoO3ZDpXy8dJvx0/ri/VHtfSF2y7SAoqDiwaEsLAwTZ06tShqKXTelSvL+dtv7tfOI0fkVbasLGd9+5V3jepypqaq/OsTFfqfrxX86SeS9cydmNxtCfKPiZHF31/y8ZF/167yqhBaYBvlnnla9tlzCmwHly85O09VS/u4X19fykfpDpcyHH9e5Hfbc1XRz1t9E37TbT/sVft1B+X4I0DYHS7dERyg+Q2ran3L6krOztOzf/RAJKaf1tS9aZp5a+Wi3ali7ErPrXylWt8ha5Uqsr//b/c0IytbWV98oZROdylt2HCVj39FPpGRhb9TJZittLdmPBmp5k+vVZV/LdPbX+9X/CM3qfJ1pbRodEOFVyFYlwQXDQjt27d3fxvUNc/LSzLOMd3pdP/T4uOjUm3aKPPjj5XSqbPs/56j4A8/kHx9lfH2dOXt3q3QJV8o5NN5ytm8Rcr9s4vaWrmSSrVqKft77xfBzhRvLsPQub6t3HrWd5jnGYb+m2JXn2qB2tCqhgZVD1LMhoPKcboUU7GMPoiqoiBfq0pZvTTyxmAtPpKhU3lOPbr1N82OqqIAb8boXjVXeG7ls/Xtq4xpb0uuP4PgydHPKvODuZLLJceePcpe8qVKR7crzL0p8RL3p+ulT3drx9utdHhOtEZ3v1H3xW8u0IOH4q9E/YV0Hj4sa4UK7tfWihXlOnFSRnb2n8scPSbHL78od1uCpDPdoLJa5R0WJq/AQGW8866OtYtWarf75Dp1qsDAqdKdOyt76Tcyzvq6Tfw9YaV99FvOn+Hr8Ok8lffxKnBRr1zKRzfZ/HRbeX9J0t2VyshpSHuz8vTl0QytOv7ncTAk+Vikb1PsOpHnVO8th9Rg5a/68miGJu9N09idBcc34PJc6bklnRnH4Fu/nrK//OrPhr28VGbIYFkCAv6cZrHI4PZdofpmW6qa3xTkHpQ4sNMNSjqYoeMZjNkpSUpUQDj9wyr5RtWXd/UbJEkBvR9S9rffFlxmxQp5Vw2Tzy23SJJ8b7tNMgw5kpNV+s5olX81XpJk8fdXmb59lPX55+51/Zo0Uc7q1UWyL8VddKhNG9Ky9Yv9zHiPd/af0N0VyxRYpkOoTfuycrXl5JmL0KrjmbJIqu7vo8PZeXp6xzFlO11yGobe+vW47q9STvdXKadfo290D1yMqVhGQ2sE6YU6oX8tAZfhSs8tSfJt1Ei527cXCBVyuVQ6OloBDz0oSbJWqaLSnToq++v/FPo+lWRRNcrphx3HdezEmfNv8Yajqh7qzycWSphicu/g0riOH9eJp2IV9O47svj4yHHggNKGDpdPZKTKv/6aUu7sIFdqqn5/vI/KT3hZFn9/Gbk5Ot6nn5STo8xP58u3fn1V+H65ZLUq85N5Bf5QeVe/QY7kQ57bwWIk1M9b79WvrB6bDynXZahGgK/m1K+izSez1T/hN225o6YqlvLWosZVNeinI8pyGvLzsuj/GlVVKauX+t1QXnuz8tToh71yGNIdwf56PpyPMhaWKz23pDPnj/Mc58/xwUNUPn6CAu6/X7J66eS4F+TYs6eI97BkaXNrsOLuqanWz66Tr7dFQWV8tfi5Rp4uC0XMYlzFm0o5OTlKSkpScJ9+sqbQZftPd/3hZDm71PV0GbgE1i926FCVqp4uA5fo+sPJMr6M8XQZuAS5dy5UUlKSIiIi5OfnV6jbupJrpDM0VL+/926R1JmvRN1iAAAAl4aAAAAATAgIAADAhIAAAABMCAgAAMCEgAAAAEwICAAAwISAAAAATAgIAADAhIAAAABMCAgAAMCEgAAAAEwICAAAwISAAAAATAgIAADAhIAAAABMCAgAAMCEgAAAAEwICAAAwISAAAAATAgIAADAhIAAAABMvD1dAAAAJUnILTb5nTp9WevklLPp90Kq53zoQQAAACYEBAAAYEJAAAAAJgQEAABgQkAAAAAmBAQAAGBCQAAAACYEBAAAYEJAAAAAJgQEAABgQkAAAAAmBAQAAGBCQAAAACYEBAAAYEJAAAAAJgQEAABg4u3pAgAAwJVzuVwaN26cdu3aJV9fX40fP17VqlVzz//hhx/09ttvS5JuvvlmjR07VhaL5bzt0YMAAEAxsHz5cuXm5mr+/PmKjY1VfHy8e57dbtfEiRM1c+ZMLViwQFWqVNGJEycu2B4BAQCAYmDLli1q0aKFJKlevXpKSkpyz9u2bZvCw8P16quvqlevXgoODlZQUNAF2+MWAwAAxYDdbpfNZnO/tlqtcjgc8vb21okTJ7RhwwYtXrxY/v7+evDBB1WvXj1Vr179vO3RgwAAQDFgs9mUmZnpfu1yueTtfaYfIDAwULfccotCQkIUEBCghg0b6ueff75gewQEAACKgaioKK1atUqSlJCQoPDwcPe8iIgI7d69W2lpaXI4HNq+fbtq1ap1wfa4xQAAQDEQHR2tNWvWqGfPnjIMQxMmTNDs2bMVFhamtm3bKjY2Vn369JEkdejQoUCAOBcCAgAAxYCXl5defPHFAtNq1qzp/nfnzp3VuXPnS2/vqlUGAACKDQICAAAwISAAAAATAgIAADAhIAAAABMCAgAAMCEgAAAAEwICAAAwISAAAAATAgIAADDhUcsAABQhy22BsuS4Lm8dv8BCqub86EEAAAAmBAQAAGBCQAAAACYEBAAAYEJAAAAAJgQEAABgQkAAAAAmBAQAAGBCQAAAACYEBAAAYEJAAAAAJgQEAABgQkAAAAAmBAQAAGBCQAAAACbehdFoxfVr5efnVxhN4yqzfrHD0yXgEl1/ONnTJeAyWGK+9HQJuBQ5OZ6u4B+LHgQAAGBSKD0Ia6u3kXHk98JoGldRG2OXvrfU9nQZuAQcq2tLG2OXnF3qeroMXIoFWz1dwT8WPQgAAMCEgAAAAEwICAAAwISAAAAATAgIAADAhIAAAABMCAgAAMCEgAAAAEwICAAAwISAAAAATAgIAADAhIAAAABMCAgAAMCEgAAAAEwICAAAwISAAAAATLw9XQAAACWJpXpZWZyuy1vHWraQqjk/ehAAAIAJAQEAAJgQEAAAgAkBAQAAmBAQAACACQEBAACYEBAAAIAJAQEAAJgQEAAAgAkBAQCAYsDlcmnMmDHq0aOHevfurQMHDpxzmT59+mjevHkXbY+AAABAMbB8+XLl5uZq/vz5io2NVXx8vGmZt956S6dOnbqk9ggIAAAUA1u2bFGLFi0kSfXq1VNSUlKB+UuXLpXFYlHLli0vqT0CAgAAxYDdbpfNZnO/tlqtcjgckqTdu3frq6++0tChQy+5Pb7NEQCAYsBmsykzM9P92uVyydv7zGV+8eLFOnbsmB555BEdPnxYPj4+qlKlygV7EwgIAAAUA1FRUVqxYoU6deqkhIQEhYeHu+c9/fTT7n9PnTpVwcHBF73VQEAAAKAYiI6O1po1a9SzZ08ZhqEJEyZo9uzZCgsLU9u2bS+7PQICAADFgJeXl1588cUC02rWrGlabvDgwZfW3lWpCgAAFCsEBAAAYEJAAAAAJgQEAABgQkAAAAAmBAQAAGBCQAAAACYEBAAAYEJAAAAAJgQEAABgQkAAAAAmBAQAAGBCQAAAACYEBAAAYEJAAAAAJgQEAABgQkAAAAAm3p4uAACAEqVGLckr6/LWcflLmYVTzvnQgwAAAEwICAAAwISAAAAATAgIAADAhIAAAABMCAgAAMCEgAAAAEwICAAAwISAAAAATAgIAADAhIAAAABMCAgAAMCEgAAAAEwICAAAwISAAAAATAgIAADAhIAAAABMCAgAAMCEgAAAAEwICAAAwMTb0wUUtes6tVLNV2Jl8fNV5k+79PPjo+XMyCywTHDXdqrxwhAZLpccaae0s+9zyt6b7J7vd31FNVy/QBtv7aK84yckSYF33KYb33hGFqZfIvkAABjVSURBVG9v5R0/qV+GvSz7T7uKdN+Ko6txvCQpYtFU5f6Wot2DX5J3uTKqv/LDAvNtt4Rrz4jXlDxpTmHvUrF1JcfKWtamm95/Wf51asji5aUjHyzWwddmSTpzbtWa+LQsPt5yZZ/W7iHjlbEp0RO7WOwZhqHHtv2miLJ+iq0VLEmqsHSXri/156Uitlawel1fTqk5Dv1r22EdzMqTl8WiGbdWUrMgf0+VjkJw0R4El8ulMWPGqEePHurdu7cOHDhQFHUVCp/g8rpp9itK7DZYG+p0UPbeZNWMjyuwjFcpP9X9aKIS7x2kTfW76vcvv9eNU55zz6/Yu4uiVn0svyoV3NOsZW265bOp2jPiNW289W7tGjBOdRdMlsXXp8j2rTi6GsdLksJG9FFgi4bu145TGdpUv6v758j7C3Vy9RYdmvpRkexXcXSlx6rGS0OVc+iYNt4So02N7lOVAT1Vtkk9WXx8FDF/knb2fU6b6nXR/vEzdPOHEz2xi8Xezxk5il53QIuOpLun7bLnKMjHqi131HT/9Lq+nCRpcOJR3R7kr8Q2tfRBVBX13HxIWQ6Xp8pHIbhoQFi+fLlyc3M1f/58xcbGKj4+vijqKhRBd96u9E2Jyt5zJuQcnjFPFR+MKbCMxWqVLBZ5lysjSbLaAuQ6nSNJ8q0UquCu7ZTQ/vEC6/jfeIMcpzJ04vv1kqSsXXvlTLerXNP6hb1LxdqVHi9JCmzVWNd1aKHDMz895zZK1wzTDc8N0P96Py3D4SikPSn+rvRY/TL0Ze2Je1WS5FcpRF5+vnKcypCRl6c1VVrKnvCzJKl0japy/NFrh6trxr40PR5WXvdVLuueti4tW1aL1Gr1PtVf8ate2pUqp2HI4TL09bEM9alWXpJUr1wp1Qrw1Tcpdk+Vj0Jw0VsMW7ZsUYsWLSRJ9erVU1JSUqEXVVhKVa2onOSj7tc5h47Ku1wZWcsEuLtCnZlZ2vXEWDVY+6nyjp+UxeqlLc0fkCTlHklRUrfBpnazdu+TNcBfQdHNlbZsjco0vEUBdWvJr1JI0exYMXWlx8u3UqhunPystnfooyr9e5xzGzVeHq5DUz9STvKRwt+hYuxKj5UkGU6nbv5wokLua6/fP1+mrF37zkx3OOQTep0ab/1cPsHlldRjWNHuXAkxJbKSJGlZ6p8XeYdhqG1IgCbcVEF5hqGY9QdV1ttLPaqUk8uQQvz+vIRcX8pHh07nFXndKDwX7UGw2+2y2Wzu11arVY5r9Z2Wl5cMwzBNNpx/dosFRITrhjEDteHmTlpTpYX2vzxTEYumXrBZZ0amErsOVLXR/dUo4QtVfLiLTny/Xq5cTpYrcgXHy+Ltrbrz3tCe4a8o92jqOZv3u76igtrfruTJcwttF0qMq3Ru/a/3CK0ObiLvoHKqPmage3peynGtub6lNjftoZtmv6LSN95QaLuCP/WpVl6Tb6mkAG8vBfpYNbzmdVp8NEMuGbL8ZVlDhqyWv07FteyiAcFmsykz88+BRi6XS97e1+bYxtMHj8ivcqj7tV+VCspLOylXVrZ7WlD723VqzVb3ILdDb38sW8SN8rmu/PkbtljktGdqW+uHtaleF/0yZLz8w29wd7fi77mS41W2ya0qXaOqar05Uo22LVblJ3oqtEcn1Zk13r1u6B/vVJ32ggPpcPmu9NwKuvN2+VY6s74zM0vH5n2tMlE3y1rWpuCu7dxt2Lf9T/btO2W7JbyI9qxk+yj5pH46ddr92pDkY7Eo1NdbhqS0XKd73m+nHbq+FOOuipOLBoSoqCitWrVKkpSQkKDw8Gv3xEz7drXKNblVpWtVkyRVfqKnfv/iuwLL2Lf+T4GtGskn9DpJUkjXdsred8j9aYVzMgzd+p9ZKtMgQpIU2r2jXKdz+RTDFbqS43Vq9RatDbvDPRDxt5mfKmX+f7Sz758DGANbNVbad+uLboeKsSs9t0K7d1T1sWd6DCy+Pgrt3vHMmB6nSzf9e4LKNYuSJAXcXEv+dWoofcP2Ity7kispI0fj/hh3kO106e19aepepay8vSzqVMGmWQfO/F386dRp/ZyRo1bBfIqhOLloV0B0dLTWrFmjnj17yjAMTZgwoSjqKhR5qWn6+dFRilg4RV6+Psr+9aD+9/AzKtMgQnXeG69N9bvqxIr1OjjxfUWt/FCu3Dw50k4pscuTF217R69Y1Zn1kiy+Pso9kqqful58HVxYYR4vSSp9YzWd3n+4kPeiZLjSY7UnNl61Z76gxolfSpJSP19+5taPYSix60Dd+NboMx9zzMnV/3rFKefwMU/ubokxJjxEQxKPqt6KX5VnSN0ql9HjYYGSpGm3VFK/7b/p1hW/yiJpTlQVlfOxerZgXFUW41w3Dv+mnJwcJSUlKT1miIwjv1+tZlFI2hi79L2ltqfLwCXgWF1b2hi75OxS19Nl4BI4FmxVUlKSIiIi5OfnV6jbyr9G1g34Sn5eWZe3rstfOzLvKpI68/EkRQAAYEJAAAAAJgQEAABgQkAAAAAmBAQAAGBybT7xCACAa5Sl/I2y+Fzek3YteT5SET/TjR4EAABgQkAAAAAm3GIAAKAYcLlcGjdunHbt2iVfX1+NHz9e1apVc8+fM2eOvv76a0lSq1atNGjQoAu2Rw8CAADFwPLly5Wbm6v58+crNjZW8fHx7nnJyclasmSJPv30U82fP1+rV6/Wzp07L9gePQgAABQDW7ZsUYsWLSRJ9erVU1JSkntexYoV9d5778lqPfN9GQ6H46KPbKYHAQCAYsBut8tms7lfW61WORwOSZKPj4+CgoJkGIZeffVV3XzzzapevfoF2yMgAABQDNhsNmVm/vlZSJfLJW/vP28U5OTkKC4uTpmZmRo7duxF2yMgAABQDERFRWnVqlWSpISEBIWHh7vnGYahJ598UrVr19aLL77ovtVwIYxBAACgGIiOjtaaNWvUs2dPGYahCRMmaPbs2QoLC5PL5dLGjRuVm5urH3/8UZL01FNPqX79+udtj4AAAEAx4OXlpRdffLHAtJo1a7r/nZiYeHntXZWqAABAsUJAAAAAJgQEAABgQkAAAAAmBAQAAGBCQAAAACYEBAAAYEJAAAAAJgQEAABgQkAAAAAmBAQAAGBCQAAAACYEBAAAYEJAAAAAJgQEAABgQkAAAAAmBAQAAGBCQAAAACYEBAAAYEJAAAAAJgQEAABgQkAAAAAmBAQAAGBCQAAAACbeni4AAIASJbC25HeZ6+RIOlQYxZwfPQgAAMCEgAAAAEwICAAAwISAAAAATAgIAADAhIAAAABMCAgAAMCEgAAAAEwICAAAwISAAAAATAgIAADAhIAAAABMCAgAAMCEgAAAAEwICAAAwISAAAAATAgIAADAhIAAAABMCAgAAMCEgAAAAEwICAAAwISAAAAATAgIAADAhIAAAABMCAgAAMCEgAAAAEwICAAAwISAAAAATAgIAADAhIAAAABMCAgAABQDLpdLY8aMUY8ePdS7d28dOHCgwPwFCxbo3nvvVffu3bVixYqLtuddWIUCAICis3z5cuXm5mr+/PlKSEhQfHy8ZsyYIUlKTU3Vhx9+qEWLFiknJ0e9evVS8+bN5evre972rmpAMAxDktRw19ILbhT/DDk5OWp++idPl4FLwLG6tuTk5EgLtnq6DFyC3NxcSX9ev4pCXl7hrLNlyxa1aNFCklSvXj0lJSW55/3000+qX7++fH195evrq7CwMO3cuVORkZHnbe+qBoS8P/Zg9+7dV7NZAAAKVV5enkqVKlWo27BarbJardq1y3lF65+P3W6XzWYrsLzD4ZC3t7fsdrvKlCnjnhcQECC73X7B7V3VgBAQEKDw8HD5+PjIYrFczaYBALjqDMNQXl6eAgICCn1b3t7eioiIkNP59wOCt/f5L9s2m02ZmZnu1y6Xy738X+dlZmYWCAznrPdvVXkeXl5eF90gAAD/JIXdc3A2b2/vC17kr0RUVJRWrFihTp06KSEhQeHh4e55kZGReuutt5STk6Pc3Fz9+uuvBeafi8UoyhsvAACgULhcLo0bN067d++WYRiaMGGCVq1apbCwMLVt21YLFizQ/PnzZRiG+vfvr/bt21+wPQICAAAw4TkIAADAhIAAAABMCAgAAMCEgIBib8+ePXI4HJ4uA3/hcrk8XQIKAcPaig8CwlXydz/XisL13//+VzNmzNCOHTsICf8Qs2fP1qlTp+Tl5UVIKEbS0tIkiWfgFCMEhKvA5XLJarXKMAxt375dR48e9XRJJZ5hGJo0aZLatm2ryMhIffHFF4SEfwC73a5vv/1WM2fO1MmTJwkJxURubq7Wrl2rhQsXavPmzXI6nRzXYoCAcIWcTqe8vLxkGIaGDBmi+Ph4zZo1S8uXL/d0aSWaxWLRrl27FBsbqwceeEBhYWFavHgxIcFDXC6X3n//fW3atEk5OTny9vbWG2+8QUgoBgzDkK+vrywWi9566y199tlnslqtHNdigIBwhfJ7Dt5//301aNBAc+bMUe3atZWQkKBvvvnG0+WVSPnfCTJz5kyVKVNGQ4YMUa9evVStWjV9+eWXSkhI4JZQEZs+fbq2b9+u+vXrq2fPnnrooYcUFBSkN998k5BwDXO5XO5bCrt371ajRo103XXXafHixZLOPF0X1y6O3t909h+zzZs3a/78+fLz85Ofn5/atm2rSpUqacuWLTp+/LgHqyx5XC6XfHx8lJaWpkOHDmnChAmqVq2aBg8erF69eikkJETfffcdvQhFLCYmRvv379fIkSNVt25dVahQQffcc49CQkI0fvx495gEXFvyg93w4cNVo0YNvf7664qMjNTu3bs1adIkLVu2jDB+DSucB0IXc06n091zsGvXLoWHh+uZZ57RJ598ohtvvFENGzZUx44dlZOTo+uuu87T5ZYYhmHIy8tLKSkpGjx4sKpXr67c3Fy9+eabio+P17/+9S/NmTNHWVlZ8vPz83S5JUrVqlXl7e2tPXv2uAezVatWTZ06ddKyZcvcX7mLa4NhGO6eg23btum///2vHnzwQVmtVjVs2FA+Pj5atGiRmjZtesFvH8Q/G49a/ptcLpf69++voKAgJSQkaNSoUTpx4oTmzZun4cOHq2nTpp4usUTKyMjQ8OHD3bcUnnvuOYWHh2vs2LGaPHmyevbsqUqVKnm6zBIpLS1NycnJeuWVV/Too4+6nwOfl5cnHx8fD1eHS+Vyudzjrn7//XeFhIRo+fLlGjNmjGbOnKnIyEg5nU45nU75+vp6ulxcAeu4cePGebqIa0VycrICAgLk5eWl+Ph4XXfddRo3bpzq1KmjN954Q71791aVKlUUHBysihUrerrcEiN/oKh05mLjcrlUoUIFffzxx4qJidHy5cu1ceNGjR07lm8b9aDSpUurYsWKCg0N1RtvvKGKFSuqRo0avMO8huT30rlcLg0ZMkRbt27VxIkTdffdd6tt27bq16+fmjVrpooVK3JciwFuMVyidevWyW63q2rVqpKkSpUqud/1NGrUSG3atFFiYqK6du3qyTJLnPyPmB47dkwrV65UWFiYateurR9//FHdunWTy+VSeHi4+vXr5+lS8YeWLVvKx8fHfS7h2pF/W2HUqFGqXr26YmNjtX79eg0cOFCLFy/W6NGjZbfbPVwlrhYCwiXKv2XwwQcfqFatWvL399fhw4e1fPlylS1bVt9++63ojCl6Xl5eSk1N1dNPP63w8HCFh4fr1ltv1dKlS7VkyRKtXbtW77zzjsLCwjxdKs7CLbhry9ljDiQpJCREXbp0kSQ1adJE3bt318GDB3Xfffedc3lcmxg2fBF/HYGbmZmpH3/8UZUrV5a/v78SExP13nvv6ZlnnlFUVJSHqiyZ8j9J8tlnn+nmm2/Ws88+q/r162vjxo2y2Wzq2LGj5syZo5o1a3q4UuDalX+xNwxD27Ztk91uV3p6uubNmye73a6tW7dq5cqVBcYbEA6KB3oQLiB/8JTL5dJLL72kunXr6sknn9ScOXO0adMm3XbbbWrWrJnS09NVtmxZT5dbYpw9SEo6M0Ley8tLWVlZ8vf315YtW1SpUiXepQJX6OxwMHDgQJ0+fVqBgYFq166dlixZotdff127du3SiBEjFBkZ6elycZUREC4gf4zB4MGD1ahRI4WGhurbb79V8+bN9fXXX+urr75SREQEA9+KUH44SElJ0ccff6zatWvr9OnT2rBhg3x8fJSVlaX169dzuwe4CvLDwbPPPqsGDRro8ccf18KFC7V27VqNGTNGFSpUUFpamkJCQjxdKgoBtxjOYfHixZo3b54k6ejRo7Lb7WrRooU++eQTbdq0SdOmTdOAAQM0aNAglS1blu60IpIfDtLS0tS3b19Vr15dS5cu1b59+3T33XcrMDBQLpdLL7zwgmrUqOHpcoFr1tkPgjt69Kh27Nih5ORkSdJ9992nnJwc7dixQ1arVcHBwZ4qE4WMgHAOXbt21QMPPKA333xTFStW1K233qqNGzfqiSee0NChQ3XixAmlpaWpcuXKni61xMgPBydPntSuXbt03333qWvXrkpNTVVgYKBCQkLUtWtXDRo0SNWrV/d0ucA16+yPMq5bt05paWmaNm2a7Ha7pk2bpgMHDmj//v3uj3LzBqn44kFJZ8l/QmK+AQMGyGKxaPr06dqxY4eWLl2qlStXKi4uTq1atfJgpSXTiRMn1L9/f91555364IMPFBgYqLlz5yohIUGLFi3Sq6++Kn9/f/5gAX/T2WMOhgwZ4n7t7++vQYMGadiwYbLb7Zo4caL7gUg876D4ogfhD/mfp88fkDh58mRNnjxZ/v7+6tevn+rWratOnTopPj6ecOABLpdLS5culcPhULt27TRkyBBlZmZq+fLlmjx5soYNG6aAgADCAXAF8sPB9OnTFRYWpmnTpmnSpEmy2+1KSkrSa6+9pmrVqmn9+vWSRDgo5ggIf8h/Et/AgQMVEBCgxo0by9fXV+PHj1fp0qXVu3dv3XTTTapbt66HKy2ZvLy81L59e91555369NNPFRERobfffls2m01TpkxRrVq1PF0iUCwcP35cmzZtkiSlp6fL29tbjRo1Um5urmrWrKm4uDglJia6v1MDxVeJv8Vw9gM9jhw5opdeeknTp0+XJO3Zs0fvvvuuXnvtNe3atUu1a9f2ZKnQmef5L1myRHv37tWjjz7KeAPgKsgf4zNw4EA99NBDqlq1qiZPnqxGjRopMDBQ//73vzVs2DA1adJEkpSbm8v3LJQAJboHwel0FuiSDg0Nlc1m05tvvilJ8vPzU3p6uk6ePEk4+IcICgpSTEyMbrzxRp49AVyh/PeH+T2oMTExGjlypPsbUVevXq158+apb9++7nAgiXBQQpTYHoT8noP8Lx2pUaOG9uzZo/79+2vevHlKT09XamqqBgwYoDZt2ni6XPwFg6OAK3N27+l7772nrl27Kjg4WD/88IOeeuopTZ8+XZUrV9a0adPUtGlTtWzZUkFBQR6uGkWpRAaEs0+MuLg41a9fXx07dlT37t3VsWNHDRo0SL/88otsNptuuOEGzxYLAFfZ2U8jXbFihb7//nv5+vrqySefVHBwsF599VV98803+v7777Vy5UqtWLFCsbGx9NqVMCUuIOSfGPmmTZumhg0b6sMPP1S3bt2UlpamiIgI1alTx4NVAkDhcrlcGj58uEJDQxUZGalffvlFJ06cUHR0tJYsWaIHHnhADRo0kCRlZ2erdOnSHq4YRa1EjUHIfwCIYRgaPXq0Fi9erLS0NL366qtq1qyZ6tatq7lz56qEZSYAJdCECRMUEhKiwYMHa+3atTp58qR27typd955R3fffbcaNGjgfqIi4aBkKjHfxXD2Peu4uDiVLl1aXbt21S233KLff/9ddrtdsbGxio2N1U033eThagGgcFWtWlXJyckaO3as2rVrp2PHjqlNmzaKiopy30o4u7cVJU+JCAhnPwRp48aNOnz4sBwOhw4cOKCaNWtq1KhRKlWqlNq1a8dXAwMoEWJiYpSZmal9+/YpODhYc+fOVVxcHOMM4FbsxyCc/ejQAQMGKDQ0VCkpKVq5cqUiIiI0adIkVa1a1dNlAkCRy87O1ldffaWvvvpKjz76qO644w5Pl4R/kGIfEPK9/fbbOnTokF555RUZhqFhw4Zp1apVqlKlihYuXKhSpUp5ukQAKHKGYSgjI4OeA5iUiFsMGRkZOn36tI4fP66dO3eqTp06iomJUYcOHRQREUE4AFBiWSwWwgHOqcT0IJw6dUqfffaZDh06pBo1auiLL77Q0KFD1bx5c0+XBgDAP06JGaJarlw5denSReXLl9d//vMf3X///WrevDkfaQQA4BxKxC2GfEFBQXrooYfk7++vXbt2uW83AACAgkpMD0K+wMBAdenSRTfccIOCg4M9XQ4AAP9IJWYMwl/xZT8AAJxfiQ0IAADg/ErcLQYAAHBxBAQAAGBCQAAAACYEBAAAYEJAAAAAJv8PoZnc/NvPho4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(xgb, 'class_report')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "\n",
    "## Model interpretation with SHAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x684 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interpret_model(xgb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "\n",
    "## Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Prec.</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Extreme Gradient Boosting</td>\n",
       "      <td>0.7792</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.6543</td>\n",
       "      <td>0.6974</td>\n",
       "      <td>0.6752</td>\n",
       "      <td>0.5082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Model  Accuracy    AUC  Recall   Prec.      F1   Kappa\n",
       "0  Extreme Gradient Boosting    0.7792  0.839  0.6543  0.6974  0.6752  0.5082"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = predict_model(xgb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "\n",
    "## Model saving and loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
       "              learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
       "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=-1,\n",
       "              nthread=None, objective='binary:logistic', random_state=2895,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "              silent=None, subsample=1, verbosity=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalize_model(xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    }
   ],
   "source": [
    "save_model(xgb, 'diabetes_xgboost')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Sucessfully Loaded\n"
     ]
    }
   ],
   "source": [
    "model = load_model('diabetes_xgboost')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "\n",
    "## Model deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "deploy_model(\n",
    "    model, \n",
    "    model_name='xgboost_aws', \n",
    "    platform='aws', \n",
    "    authentication={'bucket': 'pycaret-test'}\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}