PyCaret- Predicting House Price.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             1460 non-null   int64  \n",
      " 1   MSSubClass     1460 non-null   int64  \n",
      " 2   MSZoning       1460 non-null   object \n",
      " 3   LotFrontage    1201 non-null   float64\n",
      " 4   LotArea        1460 non-null   int64  \n",
      " 5   Street         1460 non-null   object \n",
      " 6   Alley          91 non-null     object \n",
      " 7   LotShape       1460 non-null   object \n",
      " 8   LandContour    1460 non-null   object \n",
      " 9   Utilities      1460 non-null   object \n",
      " 10  LotConfig      1460 non-null   object \n",
      " 11  LandSlope      1460 non-null   object \n",
      " 12  Neighborhood   1460 non-null   object \n",
      " 13  Condition1     1460 non-null   object \n",
      " 14  Condition2     1460 non-null   object \n",
      " 15  BldgType       1460 non-null   object \n",
      " 16  HouseStyle     1460 non-null   object \n",
      " 17  OverallQual    1460 non-null   int64  \n",
      " 18  OverallCond    1460 non-null   int64  \n",
      " 19  YearBuilt      1460 non-null   int64  \n",
      " 20  YearRemodAdd   1460 non-null   int64  \n",
      " 21  RoofStyle      1460 non-null   object \n",
      " 22  RoofMatl       1460 non-null   object \n",
      " 23  Exterior1st    1460 non-null   object \n",
      " 24  Exterior2nd    1460 non-null   object \n",
      " 25  MasVnrType     1452 non-null   object \n",
      " 26  MasVnrArea     1452 non-null   float64\n",
      " 27  ExterQual      1460 non-null   object \n",
      " 28  ExterCond      1460 non-null   object \n",
      " 29  Foundation     1460 non-null   object \n",
      " 30  BsmtQual       1423 non-null   object \n",
      " 31  BsmtCond       1423 non-null   object \n",
      " 32  BsmtExposure   1422 non-null   object \n",
      " 33  BsmtFinType1   1423 non-null   object \n",
      " 34  BsmtFinSF1     1460 non-null   int64  \n",
      " 35  BsmtFinType2   1422 non-null   object \n",
      " 36  BsmtFinSF2     1460 non-null   int64  \n",
      " 37  BsmtUnfSF      1460 non-null   int64  \n",
      " 38  TotalBsmtSF    1460 non-null   int64  \n",
      " 39  Heating        1460 non-null   object \n",
      " 40  HeatingQC      1460 non-null   object \n",
      " 41  CentralAir     1460 non-null   object \n",
      " 42  Electrical     1459 non-null   object \n",
      " 43  1stFlrSF       1460 non-null   int64  \n",
      " 44  2ndFlrSF       1460 non-null   int64  \n",
      " 45  LowQualFinSF   1460 non-null   int64  \n",
      " 46  GrLivArea      1460 non-null   int64  \n",
      " 47  BsmtFullBath   1460 non-null   int64  \n",
      " 48  BsmtHalfBath   1460 non-null   int64  \n",
      " 49  FullBath       1460 non-null   int64  \n",
      " 50  HalfBath       1460 non-null   int64  \n",
      " 51  BedroomAbvGr   1460 non-null   int64  \n",
      " 52  KitchenAbvGr   1460 non-null   int64  \n",
      " 53  KitchenQual    1460 non-null   object \n",
      " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
      " 55  Functional     1460 non-null   object \n",
      " 56  Fireplaces     1460 non-null   int64  \n",
      " 57  FireplaceQu    770 non-null    object \n",
      " 58  GarageType     1379 non-null   object \n",
      " 59  GarageYrBlt    1379 non-null   float64\n",
      " 60  GarageFinish   1379 non-null   object \n",
      " 61  GarageCars     1460 non-null   int64  \n",
      " 62  GarageArea     1460 non-null   int64  \n",
      " 63  GarageQual     1379 non-null   object \n",
      " 64  GarageCond     1379 non-null   object \n",
      " 65  PavedDrive     1460 non-null   object \n",
      " 66  WoodDeckSF     1460 non-null   int64  \n",
      " 67  OpenPorchSF    1460 non-null   int64  \n",
      " 68  EnclosedPorch  1460 non-null   int64  \n",
      " 69  3SsnPorch      1460 non-null   int64  \n",
      " 70  ScreenPorch    1460 non-null   int64  \n",
      " 71  PoolArea       1460 non-null   int64  \n",
      " 72  PoolQC         7 non-null      object \n",
      " 73  Fence          281 non-null    object \n",
      " 74  MiscFeature    54 non-null     object \n",
      " 75  MiscVal        1460 non-null   int64  \n",
      " 76  MoSold         1460 non-null   int64  \n",
      " 77  YrSold         1460 non-null   int64  \n",
      " 78  SaleType       1460 non-null   object \n",
      " 79  SaleCondition  1460 non-null   object \n",
      " 80  SalePrice      1460 non-null   int64  \n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1459 entries, 0 to 1458\n",
      "Data columns (total 80 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             1459 non-null   int64  \n",
      " 1   MSSubClass     1459 non-null   int64  \n",
      " 2   MSZoning       1455 non-null   object \n",
      " 3   LotFrontage    1232 non-null   float64\n",
      " 4   LotArea        1459 non-null   int64  \n",
      " 5   Street         1459 non-null   object \n",
      " 6   Alley          107 non-null    object \n",
      " 7   LotShape       1459 non-null   object \n",
      " 8   LandContour    1459 non-null   object \n",
      " 9   Utilities      1457 non-null   object \n",
      " 10  LotConfig      1459 non-null   object \n",
      " 11  LandSlope      1459 non-null   object \n",
      " 12  Neighborhood   1459 non-null   object \n",
      " 13  Condition1     1459 non-null   object \n",
      " 14  Condition2     1459 non-null   object \n",
      " 15  BldgType       1459 non-null   object \n",
      " 16  HouseStyle     1459 non-null   object \n",
      " 17  OverallQual    1459 non-null   int64  \n",
      " 18  OverallCond    1459 non-null   int64  \n",
      " 19  YearBuilt      1459 non-null   int64  \n",
      " 20  YearRemodAdd   1459 non-null   int64  \n",
      " 21  RoofStyle      1459 non-null   object \n",
      " 22  RoofMatl       1459 non-null   object \n",
      " 23  Exterior1st    1458 non-null   object \n",
      " 24  Exterior2nd    1458 non-null   object \n",
      " 25  MasVnrType     1443 non-null   object \n",
      " 26  MasVnrArea     1444 non-null   float64\n",
      " 27  ExterQual      1459 non-null   object \n",
      " 28  ExterCond      1459 non-null   object \n",
      " 29  Foundation     1459 non-null   object \n",
      " 30  BsmtQual       1415 non-null   object \n",
      " 31  BsmtCond       1414 non-null   object \n",
      " 32  BsmtExposure   1415 non-null   object \n",
      " 33  BsmtFinType1   1417 non-null   object \n",
      " 34  BsmtFinSF1     1458 non-null   float64\n",
      " 35  BsmtFinType2   1417 non-null   object \n",
      " 36  BsmtFinSF2     1458 non-null   float64\n",
      " 37  BsmtUnfSF      1458 non-null   float64\n",
      " 38  TotalBsmtSF    1458 non-null   float64\n",
      " 39  Heating        1459 non-null   object \n",
      " 40  HeatingQC      1459 non-null   object \n",
      " 41  CentralAir     1459 non-null   object \n",
      " 42  Electrical     1459 non-null   object \n",
      " 43  1stFlrSF       1459 non-null   int64  \n",
      " 44  2ndFlrSF       1459 non-null   int64  \n",
      " 45  LowQualFinSF   1459 non-null   int64  \n",
      " 46  GrLivArea      1459 non-null   int64  \n",
      " 47  BsmtFullBath   1457 non-null   float64\n",
      " 48  BsmtHalfBath   1457 non-null   float64\n",
      " 49  FullBath       1459 non-null   int64  \n",
      " 50  HalfBath       1459 non-null   int64  \n",
      " 51  BedroomAbvGr   1459 non-null   int64  \n",
      " 52  KitchenAbvGr   1459 non-null   int64  \n",
      " 53  KitchenQual    1458 non-null   object \n",
      " 54  TotRmsAbvGrd   1459 non-null   int64  \n",
      " 55  Functional     1457 non-null   object \n",
      " 56  Fireplaces     1459 non-null   int64  \n",
      " 57  FireplaceQu    729 non-null    object \n",
      " 58  GarageType     1383 non-null   object \n",
      " 59  GarageYrBlt    1381 non-null   float64\n",
      " 60  GarageFinish   1381 non-null   object \n",
      " 61  GarageCars     1458 non-null   float64\n",
      " 62  GarageArea     1458 non-null   float64\n",
      " 63  GarageQual     1381 non-null   object \n",
      " 64  GarageCond     1381 non-null   object \n",
      " 65  PavedDrive     1459 non-null   object \n",
      " 66  WoodDeckSF     1459 non-null   int64  \n",
      " 67  OpenPorchSF    1459 non-null   int64  \n",
      " 68  EnclosedPorch  1459 non-null   int64  \n",
      " 69  3SsnPorch      1459 non-null   int64  \n",
      " 70  ScreenPorch    1459 non-null   int64  \n",
      " 71  PoolArea       1459 non-null   int64  \n",
      " 72  PoolQC         3 non-null      object \n",
      " 73  Fence          290 non-null    object \n",
      " 74  MiscFeature    51 non-null     object \n",
      " 75  MiscVal        1459 non-null   int64  \n",
      " 76  MoSold         1459 non-null   int64  \n",
      " 77  YrSold         1459 non-null   int64  \n",
      " 78  SaleType       1458 non-null   object \n",
      " 79  SaleCondition  1459 non-null   object \n",
      "dtypes: float64(11), int64(26), object(43)\n",
      "memory usage: 912.0+ KB\n",
      "None\n",
      "(1460, 81)\n",
      "(1459, 80)\n",
      "(1459, 2)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#Importing the data and checking the list of variables and rows\n",
    "train=pd.read_csv(\"train_hp.csv\", encoding=\"Latin-1\")\n",
    "test=pd.read_csv(\"test_hp.csv\", encoding=\"Latin-1\")\n",
    "sample_submission=pd.read_csv(\"hp_sample_submission.csv\", encoding=\"Latin-1\")\n",
    "\n",
    "print(train.info())\n",
    "print(test.info())\n",
    "\n",
    "print(train.shape)\n",
    "print(test.shape)\n",
    "print(sample_submission.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Requirement already satisfied: bleach in c:\\users\\yvkch\\anaconda4\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (3.1.0)\n",
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      "Requirement already satisfied: mistune<2,>=0.8.1 in c:\\users\\yvkch\\anaconda4\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.8.4)\n",
      "Requirement already satisfied: webencodings in c:\\users\\yvkch\\anaconda4\\lib\\site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.5.1)\n"
     ]
    }
   ],
   "source": [
    "#Installing PyCaret\n",
    "!pip install pycaret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicing the house price is a regression problem\n",
    "#Importing regression from PyCaret to build several regression models\n",
    "\n",
    "from pycaret import regression\n",
    "from pycaret.regression import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Setup Succesfully Completed!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_f2234582_d103_11ea_bdec_30243236180brow1_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_f2234582_d103_11ea_bdec_30243236180brow4_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_f2234582_d103_11ea_bdec_30243236180brow15_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_f2234582_d103_11ea_bdec_30243236180brow17_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_f2234582_d103_11ea_bdec_30243236180brow22_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_f2234582_d103_11ea_bdec_30243236180brow28_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_f2234582_d103_11ea_bdec_30243236180brow37_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_f2234582_d103_11ea_bdec_30243236180brow39_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }</style><table id=\"T_f2234582_d103_11ea_bdec_30243236180b\" ><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_f2234582_d103_11ea_bdec_30243236180blevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow0_col1\" class=\"data row0 col1\" >371</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow1_col0\" class=\"data row1 col0\" >Transform Target </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow1_col1\" class=\"data row1 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow2_col0\" class=\"data row2 col0\" >Transform Target Method</td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow2_col1\" class=\"data row2 col1\" >box-cox</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow3_col1\" class=\"data row3 col1\" >(1460, 81)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow4_col1\" class=\"data row4 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow5_col1\" class=\"data row5 col1\" >21</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow6_col1\" class=\"data row6 col1\" >58</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow8_col1\" class=\"data row8 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow9_col1\" class=\"data row9 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow10_col1\" class=\"data row10 col1\" >(1460, 81)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow11_col1\" class=\"data row11 col1\" >(1021, 940)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow12_col1\" class=\"data row12 col1\" >(439, 940)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow13_col1\" class=\"data row13 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow14_col1\" class=\"data row14 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow15_col1\" class=\"data row15 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow16_col1\" class=\"data row16 col1\" >zscore</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow17_col1\" class=\"data row17 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow18_col1\" class=\"data row18 col1\" >yeo-johnson</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow19_col0\" class=\"data row19 col0\" >PCA </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow19_col1\" class=\"data row19 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow20_col1\" class=\"data row20 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow22_col1\" class=\"data row22 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow24_col1\" class=\"data row24 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow25_col1\" class=\"data row25 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow26_col1\" class=\"data row26 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow27_col1\" class=\"data row27 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow28_col1\" class=\"data row28 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow29_col1\" class=\"data row29 col1\" >0.600000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow30_col1\" class=\"data row30 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow31_col1\" class=\"data row31 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow32_col1\" class=\"data row32 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow35_col1\" class=\"data row35 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow37_col1\" class=\"data row37 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow38_col1\" class=\"data row38 col1\" >0.800000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow39_col1\" class=\"data row39 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f2234582_d103_11ea_bdec_30243236180blevel0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
       "                        <td id=\"T_f2234582_d103_11ea_bdec_30243236180brow41_col1\" class=\"data row41 col1\" >0.010000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x11cb5cb3dc8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Setting up the PyCaret with several parameters\n",
    "#numeric_imputation= mean (Mean value is used to impute the missing values for numeric variables)\n",
    "#ignore_features= [var] (Forcing the algorithm to remove these features while training the model)\n",
    "#normalization= True (is a technique often applied as part of data preparation for machine learning. The goal of normalization is to rescale the values of numeric columns in the dataset without distorting differences in the ranges of values or losing information.)\n",
    "#transformation= True (Transformation is a more radical technique. Transformation changes the shape of the distribution such that the transformed data can be represented by a normal or approximate normal distribution)\n",
    "#transform_target= True (This is similar to transformation, but applied to target variable)\n",
    "#feature_interaction= True (It is often seen in machine learning experiments when two features combined through an arithmetic operation becomes more significant in explaining variances in the data, than the same two features separately)\n",
    "#feature_selection= True (Feature Importance is a process used to select features in the dataset that contributes the most in predicting the target variable. Working with selected features instead of all the features reduces the risk of over-fitting, improves accuracy, and decreases the training time. In PyCaret, this can be achieved using feature_selection parameter)\n",
    "#remove_multicollinearity=True (Multicollinearity (also called collinearity) is a phenomenon in which one feature variable in the dataset is highly linearly correlated with another feature variable in the same dataset. Multicollinearity increases the variance of the coefficients, thus making them unstable and noisy for linear models. One such way to deal with Multicollinearity is to drop one of the two features that are highly correlated with each other. This can be achieved in PyCaret using remove_multicollinearity parameter within setup)\n",
    "#multicollinearity_threshold (Threshold used for dropping the correlated features. Only comes into effect when remove_multicollinearity is set to True)\n",
    "#ignore_low_variance= True (Sometimes a dataset may have a categorical feature with multiple levels, where distribution of such levels are skewed and one level may dominate over other levels. This means there is not much variation in the information provided by such feature. Such features are eliminated when this is set as True)\n",
    "\n",
    "reg = setup(data = train, \n",
    "             target = 'SalePrice',\n",
    "             numeric_imputation = 'mean',\n",
    "             categorical_features = ['MSZoning','Exterior1st','Exterior2nd','KitchenQual','Functional','SaleType',\n",
    "                                     'Street','LotShape','LandContour','LotConfig','LandSlope','Neighborhood',   \n",
    "                                     'Condition1','Condition2','BldgType','HouseStyle','RoofStyle','RoofMatl',    \n",
    "                                     'MasVnrType','ExterQual','ExterCond','Foundation','BsmtQual','BsmtCond',   \n",
    "                                     'BsmtExposure','BsmtFinType1','BsmtFinType2','Heating','HeatingQC','CentralAir',   \n",
    "                                     'Electrical','GarageType','GarageFinish','GarageQual','GarageCond','PavedDrive',\n",
    "                                     'SaleCondition']  , \n",
    "             ignore_features = ['Alley','PoolQC','MiscFeature','Fence','FireplaceQu','Utilities'],\n",
    "             normalize = True,\n",
    "             transformation= True,\n",
    "             transform_target= True,\n",
    "             feature_interaction= True,\n",
    "             feature_selection= True,\n",
    "             remove_multicollinearity=True,\n",
    "             multicollinearity_threshold=0.6,\n",
    "             ignore_low_variance= True,\n",
    "             silent = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "            : ;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
       "        }</style><table id=\"T_f76a467a_d106_11ea_9ed9_30243236180b\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow0_col0\" class=\"data row0 col0\" >CatBoost Regressor</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow0_col1\" class=\"data row0 col1\" >15231.393800</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow0_col2\" class=\"data row0 col2\" >795245207.685800</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow0_col3\" class=\"data row0 col3\" >26659.734200</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow0_col4\" class=\"data row0 col4\" >0.886800</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow0_col5\" class=\"data row0 col5\" >0.126200</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow0_col6\" class=\"data row0 col6\" >0.088100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow1_col0\" class=\"data row1 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow1_col1\" class=\"data row1 col1\" >17514.543200</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow1_col2\" class=\"data row1 col2\" >880581857.563600</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow1_col3\" class=\"data row1 col3\" >28960.843200</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow1_col4\" class=\"data row1 col4\" >0.868000</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow1_col5\" class=\"data row1 col5\" >0.142000</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow1_col6\" class=\"data row1 col6\" >0.101300</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow2_col0\" class=\"data row2 col0\" >Gradient Boosting Regressor</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow2_col1\" class=\"data row2 col1\" >17429.269000</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow2_col2\" class=\"data row2 col2\" >882218880.117200</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow2_col3\" class=\"data row2 col3\" >28963.013100</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow2_col4\" class=\"data row2 col4\" >0.867400</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow2_col5\" class=\"data row2 col5\" >0.143400</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow2_col6\" class=\"data row2 col6\" >0.101600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow3_col0\" class=\"data row3 col0\" >Extra Trees Regressor</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow3_col1\" class=\"data row3 col1\" >17953.002800</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow3_col2\" class=\"data row3 col2\" >874537388.316100</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow3_col3\" class=\"data row3 col3\" >28932.553400</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow3_col4\" class=\"data row3 col4\" >0.867000</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow3_col5\" class=\"data row3 col5\" >0.141900</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow3_col6\" class=\"data row3 col6\" >0.102000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow4_col0\" class=\"data row4 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow4_col1\" class=\"data row4 col1\" >17673.422100</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow4_col2\" class=\"data row4 col2\" >942710095.856000</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow4_col3\" class=\"data row4 col3\" >29764.791300</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow4_col4\" class=\"data row4 col4\" >0.859800</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow4_col5\" class=\"data row4 col5\" >0.147700</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow4_col6\" class=\"data row4 col6\" >0.103400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow5_col0\" class=\"data row5 col0\" >Random Forest</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow5_col1\" class=\"data row5 col1\" >19213.899000</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow5_col2\" class=\"data row5 col2\" >967274627.625700</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow5_col3\" class=\"data row5 col3\" >30546.854000</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow5_col4\" class=\"data row5 col4\" >0.852000</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow5_col5\" class=\"data row5 col5\" >0.153800</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow5_col6\" class=\"data row5 col6\" >0.110100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow6_col0\" class=\"data row6 col0\" >Bayesian Ridge</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow6_col1\" class=\"data row6 col1\" >15201.220400</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow6_col2\" class=\"data row6 col2\" >1301646700.805500</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow6_col3\" class=\"data row6 col3\" >28630.750500</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow6_col4\" class=\"data row6 col4\" >0.829400</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow6_col5\" class=\"data row6 col5\" >0.124900</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow6_col6\" class=\"data row6 col6\" >0.090100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow7_col0\" class=\"data row7 col0\" >Support Vector Machine</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow7_col1\" class=\"data row7 col1\" >18627.731700</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow7_col2\" class=\"data row7 col2\" >1323918515.205100</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow7_col3\" class=\"data row7 col3\" >34257.928700</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow7_col4\" class=\"data row7 col4\" >0.812200</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow7_col5\" class=\"data row7 col5\" >0.153700</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow7_col6\" class=\"data row7 col6\" >0.104400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow8_col0\" class=\"data row8 col0\" >K Neighbors Regressor</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow8_col1\" class=\"data row8 col1\" >21664.217900</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow8_col2\" class=\"data row8 col2\" >1366489000.083300</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow8_col3\" class=\"data row8 col3\" >36005.199800</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow8_col4\" class=\"data row8 col4\" >0.793100</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow8_col5\" class=\"data row8 col5\" >0.170900</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow8_col6\" class=\"data row8 col6\" >0.123000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow9_col0\" class=\"data row9 col0\" >AdaBoost Regressor</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow9_col1\" class=\"data row9 col1\" >25437.452400</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow9_col2\" class=\"data row9 col2\" >1501925297.058400</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow9_col3\" class=\"data row9 col3\" >38369.126100</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow9_col4\" class=\"data row9 col4\" >0.763800</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow9_col5\" class=\"data row9 col5\" >0.185800</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow9_col6\" class=\"data row9 col6\" >0.140600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow10_col0\" class=\"data row10 col0\" >Passive Aggressive Regressor</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow10_col1\" class=\"data row10 col1\" >17735.411000</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow10_col2\" class=\"data row10 col2\" >2165286597.780300</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow10_col3\" class=\"data row10 col3\" >33704.320000</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow10_col4\" class=\"data row10 col4\" >0.723800</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow10_col5\" class=\"data row10 col5\" >0.144100</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow10_col6\" class=\"data row10 col6\" >0.107600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow11_col0\" class=\"data row11 col0\" >Decision Tree</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow11_col1\" class=\"data row11 col1\" >27495.126700</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow11_col2\" class=\"data row11 col2\" >1787407708.035300</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow11_col3\" class=\"data row11 col3\" >41815.187800</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow11_col4\" class=\"data row11 col4\" >0.716800</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow11_col5\" class=\"data row11 col5\" >0.221100</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow11_col6\" class=\"data row11 col6\" >0.158000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow12_col0\" class=\"data row12 col0\" >Huber Regressor</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow12_col1\" class=\"data row12 col1\" >17799.970400</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow12_col2\" class=\"data row12 col2\" >3497528991.434800</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow12_col3\" class=\"data row12 col3\" >38486.418300</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow12_col4\" class=\"data row12 col4\" >0.566700</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow12_col5\" class=\"data row12 col5\" >0.141500</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow12_col6\" class=\"data row12 col6\" >0.106700</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow13_col0\" class=\"data row13 col0\" >Elastic Net</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow13_col1\" class=\"data row13 col1\" >45429.907500</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow13_col2\" class=\"data row13 col2\" >4914403764.160200</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow13_col3\" class=\"data row13 col3\" >69155.207400</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow13_col4\" class=\"data row13 col4\" >0.249100</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow13_col5\" class=\"data row13 col5\" >0.326100</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow13_col6\" class=\"data row13 col6\" >0.254900</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow14_col0\" class=\"data row14 col0\" >Orthogonal Matching Pursuit</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow14_col1\" class=\"data row14 col1\" >18327.351900</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow14_col2\" class=\"data row14 col2\" >6817460362.926300</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow14_col3\" class=\"data row14 col3\" >47722.232400</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow14_col4\" class=\"data row14 col4\" >0.175600</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow14_col5\" class=\"data row14 col5\" >0.145800</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow14_col6\" class=\"data row14 col6\" >0.108400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow15_col0\" class=\"data row15 col0\" >Lasso Regression</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow15_col1\" class=\"data row15 col1\" >56489.942200</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow15_col2\" class=\"data row15 col2\" >6741629181.491400</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow15_col3\" class=\"data row15 col3\" >81289.549600</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow15_col4\" class=\"data row15 col4\" >-0.043400</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow15_col5\" class=\"data row15 col5\" >0.402500</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow15_col6\" class=\"data row15 col6\" >0.327300</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow16_col0\" class=\"data row16 col0\" >Lasso Least Angle Regression</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow16_col1\" class=\"data row16 col1\" >56489.942200</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow16_col2\" class=\"data row16 col2\" >6741629181.491400</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow16_col3\" class=\"data row16 col3\" >81289.549600</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow16_col4\" class=\"data row16 col4\" >-0.043400</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow16_col5\" class=\"data row16 col5\" >0.402500</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow16_col6\" class=\"data row16 col6\" >0.327300</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow17_col0\" class=\"data row17 col0\" >Ridge Regression</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow17_col1\" class=\"data row17 col1\" >20998.129200</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow17_col2\" class=\"data row17 col2\" >10419509326.305599</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow17_col3\" class=\"data row17 col3\" >54332.054900</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow17_col4\" class=\"data row17 col4\" >-0.248300</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow17_col5\" class=\"data row17 col5\" >0.158800</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow17_col6\" class=\"data row17 col6\" >0.126000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow18_col0\" class=\"data row18 col0\" >TheilSen Regressor</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow18_col1\" class=\"data row18 col1\" >50609.578500</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow18_col2\" class=\"data row18 col2\" >18655810235.844898</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow18_col3\" class=\"data row18 col3\" >124889.546800</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow18_col4\" class=\"data row18 col4\" >-2.566200</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow18_col5\" class=\"data row18 col5\" >0.401200</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow18_col6\" class=\"data row18 col6\" >0.326500</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow19_col0\" class=\"data row19 col0\" >Linear Regression</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow19_col1\" class=\"data row19 col1\" >50710.545300</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow19_col2\" class=\"data row19 col2\" >18919252921.596298</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow19_col3\" class=\"data row19 col3\" >125597.076700</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow19_col4\" class=\"data row19 col4\" >-2.617100</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow19_col5\" class=\"data row19 col5\" >0.401400</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow19_col6\" class=\"data row19 col6\" >0.327000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow20_col0\" class=\"data row20 col0\" >Random Sample Consensus</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow20_col1\" class=\"data row20 col1\" >116428.309900</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow20_col2\" class=\"data row20 col2\" >1176173437776.228516</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow20_col3\" class=\"data row20 col3\" >664965.905300</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow20_col4\" class=\"data row20 col4\" >-240.276000</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow20_col5\" class=\"data row20 col5\" >0.511500</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow20_col6\" class=\"data row20 col6\" >0.901200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f76a467a_d106_11ea_9ed9_30243236180blevel0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow21_col0\" class=\"data row21 col0\" >Least Angle Regression</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow21_col1\" class=\"data row21 col1\" >379509580160709008507380978071633920.000000</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow21_col2\" class=\"data row21 col2\" >146907771812997832182282208604011438299446280846168253041761061207625695232.000000</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow21_col3\" class=\"data row21 col3\" >3832858878988797099851321860023123968.000000</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow21_col4\" class=\"data row21 col4\" >-27929526178084125309186870432186974605098482059485740734431100928.000000</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow21_col5\" class=\"data row21 col5\" >8.687500</td>\n",
       "                        <td id=\"T_f76a467a_d106_11ea_9ed9_30243236180brow21_col6\" class=\"data row21 col6\" >1896602552762950488996601397248.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x11cb0019fc8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This step compares all the Regression Models available with in PyCaret\n",
    "#Best model against every Regression model comparison statistics is highlighted \n",
    "compare_models()\n",
    "\n",
    "#Best model is Catboost Regressor when we compare models against RMSE (Root Mean Squared Error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16556.7164</td>\n",
       "      <td>1.585107e+09</td>\n",
       "      <td>39813.3991</td>\n",
       "      <td>0.8190</td>\n",
       "      <td>0.1324</td>\n",
       "      <td>0.0841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15708.7211</td>\n",
       "      <td>6.288615e+08</td>\n",
       "      <td>25077.1108</td>\n",
       "      <td>0.8848</td>\n",
       "      <td>0.1118</td>\n",
       "      <td>0.0822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11701.2057</td>\n",
       "      <td>2.511652e+08</td>\n",
       "      <td>15848.1920</td>\n",
       "      <td>0.9376</td>\n",
       "      <td>0.1081</td>\n",
       "      <td>0.0784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15837.1146</td>\n",
       "      <td>7.764220e+08</td>\n",
       "      <td>27864.3508</td>\n",
       "      <td>0.8844</td>\n",
       "      <td>0.1273</td>\n",
       "      <td>0.0882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14852.3698</td>\n",
       "      <td>6.011265e+08</td>\n",
       "      <td>24517.8820</td>\n",
       "      <td>0.9144</td>\n",
       "      <td>0.1149</td>\n",
       "      <td>0.0854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17816.4347</td>\n",
       "      <td>1.920168e+09</td>\n",
       "      <td>43819.7250</td>\n",
       "      <td>0.7750</td>\n",
       "      <td>0.1952</td>\n",
       "      <td>0.1228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19401.5530</td>\n",
       "      <td>1.081522e+09</td>\n",
       "      <td>32886.5017</td>\n",
       "      <td>0.8798</td>\n",
       "      <td>0.1362</td>\n",
       "      <td>0.1004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13349.8795</td>\n",
       "      <td>4.470703e+08</td>\n",
       "      <td>21144.0365</td>\n",
       "      <td>0.9046</td>\n",
       "      <td>0.1141</td>\n",
       "      <td>0.0748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15686.5843</td>\n",
       "      <td>4.599371e+08</td>\n",
       "      <td>21446.1444</td>\n",
       "      <td>0.9070</td>\n",
       "      <td>0.1167</td>\n",
       "      <td>0.0863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11403.3585</td>\n",
       "      <td>2.010724e+08</td>\n",
       "      <td>14179.9996</td>\n",
       "      <td>0.9618</td>\n",
       "      <td>0.1054</td>\n",
       "      <td>0.0788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>15231.3938</td>\n",
       "      <td>7.952452e+08</td>\n",
       "      <td>26659.7342</td>\n",
       "      <td>0.8868</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.0881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>2396.4749</td>\n",
       "      <td>5.403452e+08</td>\n",
       "      <td>9192.5938</td>\n",
       "      <td>0.0518</td>\n",
       "      <td>0.0250</td>\n",
       "      <td>0.0133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MAE           MSE        RMSE      R2   RMSLE    MAPE\n",
       "0     16556.7164  1.585107e+09  39813.3991  0.8190  0.1324  0.0841\n",
       "1     15708.7211  6.288615e+08  25077.1108  0.8848  0.1118  0.0822\n",
       "2     11701.2057  2.511652e+08  15848.1920  0.9376  0.1081  0.0784\n",
       "3     15837.1146  7.764220e+08  27864.3508  0.8844  0.1273  0.0882\n",
       "4     14852.3698  6.011265e+08  24517.8820  0.9144  0.1149  0.0854\n",
       "5     17816.4347  1.920168e+09  43819.7250  0.7750  0.1952  0.1228\n",
       "6     19401.5530  1.081522e+09  32886.5017  0.8798  0.1362  0.1004\n",
       "7     13349.8795  4.470703e+08  21144.0365  0.9046  0.1141  0.0748\n",
       "8     15686.5843  4.599371e+08  21446.1444  0.9070  0.1167  0.0863\n",
       "9     11403.3585  2.010724e+08  14179.9996  0.9618  0.1054  0.0788\n",
       "Mean  15231.3938  7.952452e+08  26659.7342  0.8868  0.1262  0.0881\n",
       "SD     2396.4749  5.403452e+08   9192.5938  0.0518  0.0250  0.0133"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating the catboost model as it was the best among all the models when compared against RMSE\n",
    "catboost  = create_model('catboost') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16375.1195</td>\n",
       "      <td>8.836976e+08</td>\n",
       "      <td>29727.0516</td>\n",
       "      <td>0.8991</td>\n",
       "      <td>0.1210</td>\n",
       "      <td>0.0865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17452.5416</td>\n",
       "      <td>8.250871e+08</td>\n",
       "      <td>28724.3290</td>\n",
       "      <td>0.8488</td>\n",
       "      <td>0.1220</td>\n",
       "      <td>0.0879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12245.6921</td>\n",
       "      <td>2.714532e+08</td>\n",
       "      <td>16475.8371</td>\n",
       "      <td>0.9326</td>\n",
       "      <td>0.1154</td>\n",
       "      <td>0.0839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17085.0663</td>\n",
       "      <td>1.050165e+09</td>\n",
       "      <td>32406.2544</td>\n",
       "      <td>0.8437</td>\n",
       "      <td>0.1364</td>\n",
       "      <td>0.0906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14934.3639</td>\n",
       "      <td>5.307974e+08</td>\n",
       "      <td>23039.0405</td>\n",
       "      <td>0.9244</td>\n",
       "      <td>0.1130</td>\n",
       "      <td>0.0837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17342.7493</td>\n",
       "      <td>1.518352e+09</td>\n",
       "      <td>38966.0321</td>\n",
       "      <td>0.8221</td>\n",
       "      <td>0.1965</td>\n",
       "      <td>0.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20231.0259</td>\n",
       "      <td>1.232456e+09</td>\n",
       "      <td>35106.3491</td>\n",
       "      <td>0.8630</td>\n",
       "      <td>0.1398</td>\n",
       "      <td>0.1004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14171.1162</td>\n",
       "      <td>4.980045e+08</td>\n",
       "      <td>22316.0137</td>\n",
       "      <td>0.8938</td>\n",
       "      <td>0.1176</td>\n",
       "      <td>0.0811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14859.5334</td>\n",
       "      <td>4.568956e+08</td>\n",
       "      <td>21375.1154</td>\n",
       "      <td>0.9076</td>\n",
       "      <td>0.1193</td>\n",
       "      <td>0.0837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11982.9430</td>\n",
       "      <td>2.153105e+08</td>\n",
       "      <td>14673.4615</td>\n",
       "      <td>0.9591</td>\n",
       "      <td>0.1026</td>\n",
       "      <td>0.0794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>15668.0151</td>\n",
       "      <td>7.482219e+08</td>\n",
       "      <td>26280.9484</td>\n",
       "      <td>0.8894</td>\n",
       "      <td>0.1284</td>\n",
       "      <td>0.0902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>2409.2117</td>\n",
       "      <td>4.060875e+08</td>\n",
       "      <td>7585.0906</td>\n",
       "      <td>0.0416</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>0.0129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MAE           MSE        RMSE      R2   RMSLE    MAPE\n",
       "0     16375.1195  8.836976e+08  29727.0516  0.8991  0.1210  0.0865\n",
       "1     17452.5416  8.250871e+08  28724.3290  0.8488  0.1220  0.0879\n",
       "2     12245.6921  2.714532e+08  16475.8371  0.9326  0.1154  0.0839\n",
       "3     17085.0663  1.050165e+09  32406.2544  0.8437  0.1364  0.0906\n",
       "4     14934.3639  5.307974e+08  23039.0405  0.9244  0.1130  0.0837\n",
       "5     17342.7493  1.518352e+09  38966.0321  0.8221  0.1965  0.1250\n",
       "6     20231.0259  1.232456e+09  35106.3491  0.8630  0.1398  0.1004\n",
       "7     14171.1162  4.980045e+08  22316.0137  0.8938  0.1176  0.0811\n",
       "8     14859.5334  4.568956e+08  21375.1154  0.9076  0.1193  0.0837\n",
       "9     11982.9430  2.153105e+08  14673.4615  0.9591  0.1026  0.0794\n",
       "Mean  15668.0151  7.482219e+08  26280.9484  0.8894  0.1284  0.0902\n",
       "SD     2409.2117  4.060875e+08   7585.0906  0.0416  0.0249  0.0129"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Hyperparamter Optimization\n",
    "#Tuning the hyper paramters of the catboost model built above\n",
    "tuned_cb = tune_model('catboost')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Inpreting the significant featurs of the model\n",
    "#Looking at the features, it looks like most significant features are the ones that are formed using feature interaction (where multiple features are combined to form a single feature)\n",
    "\n",
    "interpret_model(tuned_cb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>133771.5096</td>\n",
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       "      <td>1462</td>\n",
       "      <td>160968.0533</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>198534.2972</td>\n",
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       "      <th>3</th>\n",
       "      <td>1464</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "     Id    SalePrice\n",
       "0  1461  133771.5096\n",
       "1  1462  160968.0533\n",
       "2  1463  198534.2972\n",
       "3  1464  210454.5722\n",
       "4  1465  183709.9214"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predicting the model on unseen data for submission to Kaggle\n",
    "#This model has a score of 0.14486 when submitted in Kaggle\n",
    "\n",
    "predictions = predict_model(tuned_cb, data = test)\n",
    "sample_submission['SalePrice'] = predictions['Label']\n",
    "sample_submission.to_csv('submission_house_price_pycaret.csv',index=False)\n",
    "sample_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<catboost.core.CatBoostRegressor object at 0x0000011C822CE848>\n"
     ]
    }
   ],
   "source": [
    "#Finalizing the model\n",
    "#The finalize_model() function fits the model onto the complete dataset including the test/hold-out sample (30% in this case). The purpose of this function is to train the model on the complete dataset before it is deployed in production\n",
    "\n",
    "final_catboost = finalize_model(tuned_cb)\n",
    "\n",
    "#Final Tuned Catboost Regression parameters for deployment\n",
    "print(final_catboost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    }
   ],
   "source": [
    "#save_model() allows you to save the model along with entire transformation pipeline for later use.\n",
    "save_model(final_catboost,'Final CatBoost Model 27Jul2020')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Sucessfully Loaded\n"
     ]
    }
   ],
   "source": [
    "#Loading the saved model\n",
    "saved_final_catboost = load_model('Final CatBoost Model 27Jul2020')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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