PyCaret 2 Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PyCaret 2 Classification Example\n",
    "This notebook is created using PyCaret 2.0. Last updated : 28-07-2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pycaret-nightly-0.39\n"
     ]
    }
   ],
   "source": [
    "# check version\n",
    "from pycaret.utils import version\n",
    "version()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data Repository"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Dataset</th>\n",
       "      <th>Data Types</th>\n",
       "      <th>Default Task</th>\n",
       "      <th>Target Variable</th>\n",
       "      <th># Instances</th>\n",
       "      <th># Attributes</th>\n",
       "      <th>Missing Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>anomaly</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Anomaly Detection</td>\n",
       "      <td>None</td>\n",
       "      <td>1000</td>\n",
       "      <td>10</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>france</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Association Rule Mining</td>\n",
       "      <td>InvoiceNo, Description</td>\n",
       "      <td>8557</td>\n",
       "      <td>8</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>germany</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Association Rule Mining</td>\n",
       "      <td>InvoiceNo, Description</td>\n",
       "      <td>9495</td>\n",
       "      <td>8</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bank</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>deposit</td>\n",
       "      <td>45211</td>\n",
       "      <td>17</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blood</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Class</td>\n",
       "      <td>748</td>\n",
       "      <td>5</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cancer</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Class</td>\n",
       "      <td>683</td>\n",
       "      <td>10</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>credit</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>default</td>\n",
       "      <td>24000</td>\n",
       "      <td>24</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>diabetes</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Class variable</td>\n",
       "      <td>768</td>\n",
       "      <td>9</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>electrical_grid</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>stabf</td>\n",
       "      <td>10000</td>\n",
       "      <td>14</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>employee</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>left</td>\n",
       "      <td>14999</td>\n",
       "      <td>10</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>heart</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>DEATH</td>\n",
       "      <td>200</td>\n",
       "      <td>16</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>heart_disease</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Disease</td>\n",
       "      <td>270</td>\n",
       "      <td>14</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>hepatitis</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Class</td>\n",
       "      <td>154</td>\n",
       "      <td>32</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>income</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>income &gt;50K</td>\n",
       "      <td>32561</td>\n",
       "      <td>14</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>juice</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>1070</td>\n",
       "      <td>15</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>nba</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>TARGET_5Yrs</td>\n",
       "      <td>1340</td>\n",
       "      <td>21</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>wine</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>type</td>\n",
       "      <td>6498</td>\n",
       "      <td>13</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>telescope</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Class</td>\n",
       "      <td>19020</td>\n",
       "      <td>11</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>glass</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Multiclass)</td>\n",
       "      <td>Type</td>\n",
       "      <td>214</td>\n",
       "      <td>10</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>iris</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Multiclass)</td>\n",
       "      <td>species</td>\n",
       "      <td>150</td>\n",
       "      <td>5</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>poker</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Multiclass)</td>\n",
       "      <td>CLASS</td>\n",
       "      <td>100000</td>\n",
       "      <td>11</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>questions</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Multiclass)</td>\n",
       "      <td>Next_Question</td>\n",
       "      <td>499</td>\n",
       "      <td>4</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>satellite</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Multiclass)</td>\n",
       "      <td>Class</td>\n",
       "      <td>6435</td>\n",
       "      <td>37</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>asia_gdp</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>11</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>elections</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>3195</td>\n",
       "      <td>54</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>facebook</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>7050</td>\n",
       "      <td>12</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ipl</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>153</td>\n",
       "      <td>25</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>jewellery</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>505</td>\n",
       "      <td>4</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>mice</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>1080</td>\n",
       "      <td>82</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>migration</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>233</td>\n",
       "      <td>12</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>perfume</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>20</td>\n",
       "      <td>29</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>pokemon</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>800</td>\n",
       "      <td>13</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>population</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>255</td>\n",
       "      <td>56</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>public_health</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>224</td>\n",
       "      <td>21</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>seeds</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>210</td>\n",
       "      <td>7</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>wholesale</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>440</td>\n",
       "      <td>8</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>tweets</td>\n",
       "      <td>Text</td>\n",
       "      <td>NLP</td>\n",
       "      <td>tweet</td>\n",
       "      <td>8594</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>amazon</td>\n",
       "      <td>Text</td>\n",
       "      <td>NLP / Classification</td>\n",
       "      <td>reviewText</td>\n",
       "      <td>20000</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>kiva</td>\n",
       "      <td>Text</td>\n",
       "      <td>NLP / Classification</td>\n",
       "      <td>en</td>\n",
       "      <td>6818</td>\n",
       "      <td>7</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>spx</td>\n",
       "      <td>Text</td>\n",
       "      <td>NLP / Regression</td>\n",
       "      <td>text</td>\n",
       "      <td>874</td>\n",
       "      <td>4</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>wikipedia</td>\n",
       "      <td>Text</td>\n",
       "      <td>NLP / Classification</td>\n",
       "      <td>Text</td>\n",
       "      <td>500</td>\n",
       "      <td>3</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>automobile</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>price</td>\n",
       "      <td>202</td>\n",
       "      <td>26</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>bike</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>cnt</td>\n",
       "      <td>17379</td>\n",
       "      <td>15</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>boston</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>medv</td>\n",
       "      <td>506</td>\n",
       "      <td>14</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>concrete</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>strength</td>\n",
       "      <td>1030</td>\n",
       "      <td>9</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>diamond</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Price</td>\n",
       "      <td>6000</td>\n",
       "      <td>8</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>energy</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Heating Load / Cooling Load</td>\n",
       "      <td>768</td>\n",
       "      <td>10</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>forest</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>area</td>\n",
       "      <td>517</td>\n",
       "      <td>13</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>gold</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Gold_T+22</td>\n",
       "      <td>2558</td>\n",
       "      <td>121</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>house</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>1461</td>\n",
       "      <td>81</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>insurance</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>charges</td>\n",
       "      <td>1338</td>\n",
       "      <td>7</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>parkinsons</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>PPE</td>\n",
       "      <td>5875</td>\n",
       "      <td>22</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>traffic</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>traffic_volume</td>\n",
       "      <td>48204</td>\n",
       "      <td>8</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Dataset    Data Types                 Default Task  \\\n",
       "0           anomaly  Multivariate            Anomaly Detection   \n",
       "1            france  Multivariate      Association Rule Mining   \n",
       "2           germany  Multivariate      Association Rule Mining   \n",
       "3              bank  Multivariate      Classification (Binary)   \n",
       "4             blood  Multivariate      Classification (Binary)   \n",
       "5            cancer  Multivariate      Classification (Binary)   \n",
       "6            credit  Multivariate      Classification (Binary)   \n",
       "7          diabetes  Multivariate      Classification (Binary)   \n",
       "8   electrical_grid  Multivariate      Classification (Binary)   \n",
       "9          employee  Multivariate      Classification (Binary)   \n",
       "10            heart  Multivariate      Classification (Binary)   \n",
       "11    heart_disease  Multivariate      Classification (Binary)   \n",
       "12        hepatitis  Multivariate      Classification (Binary)   \n",
       "13           income  Multivariate      Classification (Binary)   \n",
       "14            juice  Multivariate      Classification (Binary)   \n",
       "15              nba  Multivariate      Classification (Binary)   \n",
       "16             wine  Multivariate      Classification (Binary)   \n",
       "17        telescope  Multivariate      Classification (Binary)   \n",
       "18            glass  Multivariate  Classification (Multiclass)   \n",
       "19             iris  Multivariate  Classification (Multiclass)   \n",
       "20            poker  Multivariate  Classification (Multiclass)   \n",
       "21        questions  Multivariate  Classification (Multiclass)   \n",
       "22        satellite  Multivariate  Classification (Multiclass)   \n",
       "23         asia_gdp  Multivariate                   Clustering   \n",
       "24        elections  Multivariate                   Clustering   \n",
       "25         facebook  Multivariate                   Clustering   \n",
       "26              ipl  Multivariate                   Clustering   \n",
       "27        jewellery  Multivariate                   Clustering   \n",
       "28             mice  Multivariate                   Clustering   \n",
       "29        migration  Multivariate                   Clustering   \n",
       "30          perfume  Multivariate                   Clustering   \n",
       "31          pokemon  Multivariate                   Clustering   \n",
       "32       population  Multivariate                   Clustering   \n",
       "33    public_health  Multivariate                   Clustering   \n",
       "34            seeds  Multivariate                   Clustering   \n",
       "35        wholesale  Multivariate                   Clustering   \n",
       "36           tweets          Text                          NLP   \n",
       "37           amazon          Text         NLP / Classification   \n",
       "38             kiva          Text         NLP / Classification   \n",
       "39              spx          Text             NLP / Regression   \n",
       "40        wikipedia          Text         NLP / Classification   \n",
       "41       automobile  Multivariate                   Regression   \n",
       "42             bike  Multivariate                   Regression   \n",
       "43           boston  Multivariate                   Regression   \n",
       "44         concrete  Multivariate                   Regression   \n",
       "45          diamond  Multivariate                   Regression   \n",
       "46           energy  Multivariate                   Regression   \n",
       "47           forest  Multivariate                   Regression   \n",
       "48             gold  Multivariate                   Regression   \n",
       "49            house  Multivariate                   Regression   \n",
       "50        insurance  Multivariate                   Regression   \n",
       "51       parkinsons  Multivariate                   Regression   \n",
       "52          traffic  Multivariate                   Regression   \n",
       "\n",
       "                Target Variable  # Instances  # Attributes Missing Values  \n",
       "0                          None         1000            10              N  \n",
       "1        InvoiceNo, Description         8557             8              N  \n",
       "2        InvoiceNo, Description         9495             8              N  \n",
       "3                       deposit        45211            17              N  \n",
       "4                         Class          748             5              N  \n",
       "5                         Class          683            10              N  \n",
       "6                       default        24000            24              N  \n",
       "7                Class variable          768             9              N  \n",
       "8                         stabf        10000            14              N  \n",
       "9                          left        14999            10              N  \n",
       "10                        DEATH          200            16              N  \n",
       "11                      Disease          270            14              N  \n",
       "12                        Class          154            32              Y  \n",
       "13                  income >50K        32561            14              Y  \n",
       "14                     Purchase         1070            15              N  \n",
       "15                  TARGET_5Yrs         1340            21              N  \n",
       "16                         type         6498            13              N  \n",
       "17                        Class        19020            11              N  \n",
       "18                         Type          214            10              N  \n",
       "19                      species          150             5              N  \n",
       "20                        CLASS       100000            11              N  \n",
       "21                Next_Question          499             4              N  \n",
       "22                        Class         6435            37              N  \n",
       "23                         None           40            11              N  \n",
       "24                         None         3195            54              Y  \n",
       "25                         None         7050            12              N  \n",
       "26                         None          153            25              N  \n",
       "27                         None          505             4              N  \n",
       "28                         None         1080            82              Y  \n",
       "29                         None          233            12              N  \n",
       "30                         None           20            29              N  \n",
       "31                         None          800            13              Y  \n",
       "32                         None          255            56              Y  \n",
       "33                         None          224            21              N  \n",
       "34                         None          210             7              N  \n",
       "35                         None          440             8              N  \n",
       "36                        tweet         8594             2              N  \n",
       "37                   reviewText        20000             2              N  \n",
       "38                           en         6818             7              N  \n",
       "39                         text          874             4              N  \n",
       "40                         Text          500             3              N  \n",
       "41                        price          202            26              Y  \n",
       "42                          cnt        17379            15              N  \n",
       "43                         medv          506            14              N  \n",
       "44                     strength         1030             9              N  \n",
       "45                        Price         6000             8              N  \n",
       "46  Heating Load / Cooling Load          768            10              N  \n",
       "47                         area          517            13              N  \n",
       "48                    Gold_T+22         2558           121              N  \n",
       "49                    SalePrice         1461            81              Y  \n",
       "50                      charges         1338             7              N  \n",
       "51                          PPE         5875            22              N  \n",
       "52               traffic_volume        48204             8              N  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.datasets import get_data\n",
    "index = get_data('index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>WeekofPurchase</th>\n",
       "      <th>StoreID</th>\n",
       "      <th>PriceCH</th>\n",
       "      <th>PriceMM</th>\n",
       "      <th>DiscCH</th>\n",
       "      <th>DiscMM</th>\n",
       "      <th>SpecialCH</th>\n",
       "      <th>SpecialMM</th>\n",
       "      <th>LoyalCH</th>\n",
       "      <th>SalePriceMM</th>\n",
       "      <th>SalePriceCH</th>\n",
       "      <th>PriceDiff</th>\n",
       "      <th>Store7</th>\n",
       "      <th>PctDiscMM</th>\n",
       "      <th>PctDiscCH</th>\n",
       "      <th>ListPriceDiff</th>\n",
       "      <th>STORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CH</td>\n",
       "      <td>237</td>\n",
       "      <td>1</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.24</td>\n",
       "      <td>No</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CH</td>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.75</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>No</td>\n",
       "      <td>0.150754</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CH</td>\n",
       "      <td>245</td>\n",
       "      <td>1</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.09</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>2.09</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.40</td>\n",
       "      <td>No</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.091398</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>MM</td>\n",
       "      <td>227</td>\n",
       "      <td>1</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>No</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>CH</td>\n",
       "      <td>228</td>\n",
       "      <td>7</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.956535</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id Purchase  WeekofPurchase  StoreID  PriceCH  PriceMM  DiscCH  DiscMM  \\\n",
       "0   1       CH             237        1     1.75     1.99    0.00     0.0   \n",
       "1   2       CH             239        1     1.75     1.99    0.00     0.3   \n",
       "2   3       CH             245        1     1.86     2.09    0.17     0.0   \n",
       "3   4       MM             227        1     1.69     1.69    0.00     0.0   \n",
       "4   5       CH             228        7     1.69     1.69    0.00     0.0   \n",
       "\n",
       "   SpecialCH  SpecialMM   LoyalCH  SalePriceMM  SalePriceCH  PriceDiff Store7  \\\n",
       "0          0          0  0.500000         1.99         1.75       0.24     No   \n",
       "1          0          1  0.600000         1.69         1.75      -0.06     No   \n",
       "2          0          0  0.680000         2.09         1.69       0.40     No   \n",
       "3          0          0  0.400000         1.69         1.69       0.00     No   \n",
       "4          0          0  0.956535         1.69         1.69       0.00    Yes   \n",
       "\n",
       "   PctDiscMM  PctDiscCH  ListPriceDiff  STORE  \n",
       "0   0.000000   0.000000           0.24      1  \n",
       "1   0.150754   0.000000           0.24      1  \n",
       "2   0.000000   0.091398           0.23      1  \n",
       "3   0.000000   0.000000           0.00      1  \n",
       "4   0.000000   0.000000           0.00      0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = get_data('juice')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Initialize Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup Succesfully Completed!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83da\" ><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_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow0_col1\" class=\"data row0 col1\" >123</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow1_col0\" class=\"data row1 col0\" >Target Type</td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow1_col1\" class=\"data row1 col1\" >Binary</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow2_col0\" class=\"data row2 col0\" >Label Encoded</td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow2_col1\" class=\"data row2 col1\" >CH: 0, MM: 1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow3_col1\" class=\"data row3 col1\" >(1070, 19)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow4_col1\" class=\"data row4 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow5_col1\" class=\"data row5 col1\" >13</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow6_col1\" class=\"data row6 col1\" >5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow8_col1\" class=\"data row8 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow9_col1\" class=\"data row9 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow10_col1\" class=\"data row10 col1\" >(1070, 19)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow11_col1\" class=\"data row11 col1\" >(748, 28)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow12_col1\" class=\"data row12 col1\" >(322, 28)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow13_col1\" class=\"data row13 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow14_col1\" class=\"data row14 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow15_col1\" class=\"data row15 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow16_col1\" class=\"data row16 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow18_col1\" class=\"data row18 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow19_col0\" class=\"data row19 col0\" >PCA </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow19_col1\" class=\"data row19 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow20_col1\" class=\"data row20 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow22_col1\" class=\"data row22 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow24_col1\" class=\"data row24 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow25_col1\" class=\"data row25 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow26_col1\" class=\"data row26 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow27_col1\" class=\"data row27 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow28_col1\" class=\"data row28 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow29_col1\" class=\"data row29 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow30_col1\" class=\"data row30 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow31_col1\" class=\"data row31 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow32_col1\" class=\"data row32 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow35_col1\" class=\"data row35 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow38_col1\" class=\"data row38 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow39_col1\" class=\"data row39 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow42_col0\" class=\"data row42 col0\" >Fix Imbalance</td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow42_col1\" class=\"data row42 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83dalevel0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow43_col0\" class=\"data row43 col0\" >Fix Imbalance Method</td>\n",
       "                        <td id=\"T_c6cced64_d1c1_11ea_b44e_482ae32b83darow43_col1\" class=\"data row43 col1\" >SMOTE</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1b005434470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.classification import *\n",
    "clf1 = setup(data, target = 'Purchase', session_id=123, log_experiment=False, experiment_name='bank1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Compare Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            background-color:  lightgrey;\n",
       "            text-align:  left;\n",
       "        }</style><table id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83da\" ><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>        <th class=\"col_heading level0 col7\" >MCC</th>        <th class=\"col_heading level0 col8\" >TT (Sec)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow0_col0\" class=\"data row0 col0\" >Logistic Regression</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow0_col1\" class=\"data row0 col1\" >0.8263</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow0_col2\" class=\"data row0 col2\" >0.8959</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.7262</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.8139</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.7644</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow0_col6\" class=\"data row0 col6\" >0.6280</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow0_col7\" class=\"data row0 col7\" >0.6338</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow0_col8\" class=\"data row0 col8\" >0.0429</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow1_col0\" class=\"data row1 col0\" >Linear Discriminant Analysis</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow1_col1\" class=\"data row1 col1\" >0.8263</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow1_col2\" class=\"data row1 col2\" >0.8938</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.7536</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.7938</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.7713</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow1_col6\" class=\"data row1 col6\" >0.6317</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow1_col7\" class=\"data row1 col7\" >0.6342</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow1_col8\" class=\"data row1 col8\" >0.0089</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow2_col0\" class=\"data row2 col0\" >Ridge Classifier</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow2_col1\" class=\"data row2 col1\" >0.8236</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow2_col2\" class=\"data row2 col2\" >0.0000</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.7499</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.7920</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.7680</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow2_col6\" class=\"data row2 col6\" >0.6262</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow2_col7\" class=\"data row2 col7\" >0.6292</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow2_col8\" class=\"data row2 col8\" >0.0045</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow3_col0\" class=\"data row3 col0\" >Ada Boost Classifier</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow3_col1\" class=\"data row3 col1\" >0.8075</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow3_col2\" class=\"data row3 col2\" >0.8637</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.7053</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.7837</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.7398</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow3_col6\" class=\"data row3 col6\" >0.5881</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow3_col7\" class=\"data row3 col7\" >0.5924</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow3_col8\" class=\"data row3 col8\" >0.0780</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow4_col0\" class=\"data row4 col0\" >Gradient Boosting Classifier</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow4_col1\" class=\"data row4 col1\" >0.8062</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow4_col2\" class=\"data row4 col2\" >0.8869</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.7363</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.7651</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.7479</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow4_col6\" class=\"data row4 col6\" >0.5909</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow4_col7\" class=\"data row4 col7\" >0.5939</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow4_col8\" class=\"data row4 col8\" >0.1205</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow5_col0\" class=\"data row5 col0\" >CatBoost Classifier</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow5_col1\" class=\"data row5 col1\" >0.8049</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow5_col2\" class=\"data row5 col2\" >0.8932</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.7326</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.7629</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.7457</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow5_col6\" class=\"data row5 col6\" >0.5878</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow5_col7\" class=\"data row5 col7\" >0.5899</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow5_col8\" class=\"data row5 col8\" >3.5314</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow6_col0\" class=\"data row6 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow6_col1\" class=\"data row6 col1\" >0.7914</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow6_col2\" class=\"data row6 col2\" >0.8716</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.7294</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.7367</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.7309</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow6_col6\" class=\"data row6 col6\" >0.5609</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow6_col7\" class=\"data row6 col7\" >0.5633</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow6_col8\" class=\"data row6 col8\" >0.0675</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow7_col0\" class=\"data row7 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow7_col1\" class=\"data row7 col1\" >0.7861</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow7_col2\" class=\"data row7 col2\" >0.8806</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.7053</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.7393</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.7195</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow7_col6\" class=\"data row7 col6\" >0.5471</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow7_col7\" class=\"data row7 col7\" >0.5497</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow7_col8\" class=\"data row7 col8\" >0.0898</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow8_col0\" class=\"data row8 col0\" >Quadratic Discriminant Analysis</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow8_col1\" class=\"data row8 col1\" >0.7621</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow8_col2\" class=\"data row8 col2\" >0.8240</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.6267</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.7397</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.6678</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow8_col6\" class=\"data row8 col6\" >0.4863</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow8_col7\" class=\"data row8 col7\" >0.5000</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow8_col8\" class=\"data row8 col8\" >0.0060</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow9_col0\" class=\"data row9 col0\" >Random Forest Classifier</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow9_col1\" class=\"data row9 col1\" >0.7608</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow9_col2\" class=\"data row9 col2\" >0.8397</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.6674</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.7124</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.6848</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow9_col6\" class=\"data row9 col6\" >0.4928</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow9_col7\" class=\"data row9 col7\" >0.4974</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow9_col8\" class=\"data row9 col8\" >0.1142</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow10_col0\" class=\"data row10 col0\" >Decision Tree Classifier</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow10_col1\" class=\"data row10 col1\" >0.7594</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow10_col2\" class=\"data row10 col2\" >0.7519</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.6911</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.6970</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.6907</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow10_col6\" class=\"data row10 col6\" >0.4943</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow10_col7\" class=\"data row10 col7\" >0.4975</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow10_col8\" class=\"data row10 col8\" >0.0051</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow11_col0\" class=\"data row11 col0\" >Extra Trees Classifier</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow11_col1\" class=\"data row11 col1\" >0.7433</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow11_col2\" class=\"data row11 col2\" >0.8205</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.6708</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.6758</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.6698</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow11_col6\" class=\"data row11 col6\" >0.4605</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow11_col7\" class=\"data row11 col7\" >0.4638</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow11_col8\" class=\"data row11 col8\" >0.1503</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83dalevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow12_col0\" class=\"data row12 col0\" >K Neighbors Classifier</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow12_col1\" class=\"data row12 col1\" >0.7231</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow12_col2\" class=\"data row12 col2\" >0.7683</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow12_col3\" class=\"data row12 col3\" >0.6062</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow12_col4\" class=\"data row12 col4\" >0.6600</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow12_col5\" class=\"data row12 col5\" >0.6287</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow12_col6\" class=\"data row12 col6\" >0.4094</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow12_col7\" class=\"data row12 col7\" >0.4129</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow12_col8\" class=\"data row12 col8\" >0.0054</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83dalevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow13_col0\" class=\"data row13 col0\" >Naive Bayes</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow13_col1\" class=\"data row13 col1\" >0.7140</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow13_col2\" class=\"data row13 col2\" >0.7952</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow13_col3\" class=\"data row13 col3\" >0.7466</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow13_col4\" class=\"data row13 col4\" >0.6100</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow13_col5\" class=\"data row13 col5\" >0.6708</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow13_col6\" class=\"data row13 col6\" >0.4227</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow13_col7\" class=\"data row13 col7\" >0.4301</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow13_col8\" class=\"data row13 col8\" >0.0032</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83dalevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow14_col0\" class=\"data row14 col0\" >SVM - Linear Kernel</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow14_col1\" class=\"data row14 col1\" >0.5267</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow14_col2\" class=\"data row14 col2\" >0.0000</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow14_col3\" class=\"data row14 col3\" >0.4200</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow14_col4\" class=\"data row14 col4\" >0.2409</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow14_col5\" class=\"data row14 col5\" >0.2561</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow14_col6\" class=\"data row14 col6\" >0.0204</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow14_col7\" class=\"data row14 col7\" >0.0299</td>\n",
       "                        <td id=\"T_ed71bd46_d1c1_11ea_ac4d_482ae32b83darow14_col8\" class=\"data row14 col8\" >0.0074</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1b0055e7438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top5 = compare_models(n_select=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_01cec812_d1c2_11ea_9f4f_482ae32b83darow10_col0 {\n",
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       "            background:  yellow;\n",
       "        }    #T_01cec812_d1c2_11ea_9f4f_482ae32b83darow10_col5 {\n",
       "            background:  yellow;\n",
       "        }    #T_01cec812_d1c2_11ea_9f4f_482ae32b83darow10_col6 {\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow0_col0\" class=\"data row0 col0\" >0.7467</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow0_col1\" class=\"data row0 col1\" >0.8426</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow0_col2\" class=\"data row0 col2\" >0.6897</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.6667</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.6780</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.4693</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow0_col6\" class=\"data row0 col6\" >0.4695</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow1_col0\" class=\"data row1 col0\" >0.8267</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow1_col1\" class=\"data row1 col1\" >0.9475</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow1_col2\" class=\"data row1 col2\" >0.7241</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.8077</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.7636</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.6274</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow1_col6\" class=\"data row1 col6\" >0.6298</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow2_col0\" class=\"data row2 col0\" >0.7600</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow2_col1\" class=\"data row2 col1\" >0.8201</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow2_col2\" class=\"data row2 col2\" >0.6552</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.7037</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.6786</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.4875</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow2_col6\" class=\"data row2 col6\" >0.4883</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow3_col0\" class=\"data row3 col0\" >0.8133</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow3_col1\" class=\"data row3 col1\" >0.9070</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow3_col2\" class=\"data row3 col2\" >0.8276</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.7273</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.7742</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.6162</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow3_col6\" class=\"data row3 col6\" >0.6200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow4_col0\" class=\"data row4 col0\" >0.7867</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow4_col1\" class=\"data row4 col1\" >0.8973</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow4_col2\" class=\"data row4 col2\" >0.8621</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.6757</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.7576</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.5720</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow4_col6\" class=\"data row4 col6\" >0.5856</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow5_col0\" class=\"data row5 col0\" >0.8133</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow5_col1\" class=\"data row5 col1\" >0.8906</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow5_col2\" class=\"data row5 col2\" >0.7241</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.7778</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.7500</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.6014</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow5_col6\" class=\"data row5 col6\" >0.6023</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow6_col0\" class=\"data row6 col0\" >0.8133</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow6_col1\" class=\"data row6 col1\" >0.8833</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow6_col2\" class=\"data row6 col2\" >0.8333</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.7353</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.7812</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.6196</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow6_col6\" class=\"data row6 col6\" >0.6233</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow7_col0\" class=\"data row7 col0\" >0.8400</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow7_col1\" class=\"data row7 col1\" >0.9222</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow7_col2\" class=\"data row7 col2\" >0.8000</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.8000</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.8000</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.6667</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow7_col6\" class=\"data row7 col6\" >0.6667</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow8_col0\" class=\"data row8 col0\" >0.7973</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow8_col1\" class=\"data row8 col1\" >0.8759</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow8_col2\" class=\"data row8 col2\" >0.6897</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.7692</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.7273</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.5667</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow8_col6\" class=\"data row8 col6\" >0.5689</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow9_col0\" class=\"data row9 col0\" >0.8514</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow9_col1\" class=\"data row9 col1\" >0.9111</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow9_col2\" class=\"data row9 col2\" >0.7586</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.8462</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.8000</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.6823</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow9_col6\" class=\"data row9 col6\" >0.6849</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow10_col0\" class=\"data row10 col0\" >0.8049</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow10_col1\" class=\"data row10 col1\" >0.8898</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow10_col2\" class=\"data row10 col2\" >0.7564</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.7509</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.7510</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.5909</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow10_col6\" class=\"data row10 col6\" >0.5939</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow11_col0\" class=\"data row11 col0\" >0.0314</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow11_col1\" class=\"data row11 col1\" >0.0354</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow11_col2\" class=\"data row11 col2\" >0.0673</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0562</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0420</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0659</td>\n",
       "                        <td id=\"T_01cec812_d1c2_11ea_9f4f_482ae32b83darow11_col6\" class=\"data row11 col6\" >0.0662</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1b0086e94e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top5_tuned = [tune_model(i) for i in top5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = create_model('lr', fold = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = create_model('dt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = create_model('rf', fold = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "models(type='ensemble').index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ensembled_models = compare_models(whitelist = models(type='ensemble').index.tolist(), fold = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Tune Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tuned_lr = tune_model(lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tuned_rf = tune_model(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Ensemble Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bagged_dt = ensemble_model(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "boosted_dt = ensemble_model(dt, method = 'Boosting')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Blend Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "blender = blend_models(estimator_list = [boosted_dt, bagged_dt], method = 'soft')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Stack Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stacker = stack_models(estimator_list = [boosted_dt,bagged_dt,tuned_rf], meta_model=rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Analyze Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_model(rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_model(rf, plot = 'confusion_matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_model(rf, plot = 'boundary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluate_model(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. Interpret Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "catboost = create_model('catboost', cross_validation=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "interpret_model(catboost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "interpret_model(catboost, plot = 'correlation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "interpret_model(catboost, plot = 'reason', observation = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11. AutoML()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best = automl(optimize = 'Recall')\n",
    "best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 12. Predict Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_holdouts = predict_model(lr)\n",
    "pred_holdouts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = data.copy()\n",
    "new_data.drop(['deposit'], axis=1, inplace=True)\n",
    "predict_new = predict_model(lr, data=new_data)\n",
    "predict_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 13. Save / Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_model(lr, model_name='best-model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_bestmodel = load_model('best-model')\n",
    "print(loaded_bestmodel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import set_config\n",
    "set_config(display='diagram')\n",
    "loaded_bestmodel[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import set_config\n",
    "set_config(display='text')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14. Deploy Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "deploy_model(lr, model_name = 'best-aws', authentication = {'bucket' : 'pycaret-test'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 15. Get Config / Set Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = get_config('X_train')\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_config('seed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycaret.classification import set_config\n",
    "set_config('seed', 999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_config('seed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 16. Get System Logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_system_logs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 17. MLFlow UI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mlflow ui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to generate csv file with experiment logs\n",
    "get_logs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# End\n",
    "Thank you. For more information / tutorials on PyCaret, please visit https://www.pycaret.org"
   ]
  }
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