XGBoost And ElasticNet on Ames Housing Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparision of a Regularised Linear Model(ElasticNet) and XGBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would like to model the price of a house, we know that the price depends on the location of the house, square footage of a house, year built, year renovated, number of bedrooms, number of garages, etc. So those factors contribute to the pattern — premium location would typically lead to a higher price. However, all houses within the same area and have same square footage do not have the exact same price. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Therefore, to start, we are going to implement **regularized linear regression(ElasticNet)model** for house pricing data.And then compare its performance with our with our market leader **XGBoost model**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is an excellent house prices data set can be found [here.](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data) , which can be downloaded as I did, and saved in the same floder where this notebook exists. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from scipy.stats import norm, skew\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import ElasticNetCV, ElasticNet\n",
    "from xgboost import XGBRegressor, plot_importance \n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x))\n",
    "df = pd.read_csv('train.csv')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.000</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.000</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.000</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.000</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.000</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL       65.000     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL       80.000     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL       68.000    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL       60.000     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL       84.000    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities    ...     PoolArea PoolQC Fence MiscFeature MiscVal  \\\n",
       "0         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "1         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "2         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "3         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "4         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "\n",
       "  MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0      2   2008        WD         Normal     208500  \n",
       "1      5   2007        WD         Normal     181500  \n",
       "2      9   2008        WD         Normal     223500  \n",
       "3      2   2006        WD        Abnorml     140000  \n",
       "4     12   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
      "Id               1460 non-null int64\n",
      "MSSubClass       1460 non-null int64\n",
      "MSZoning         1460 non-null object\n",
      "LotFrontage      1201 non-null float64\n",
      "LotArea          1460 non-null int64\n",
      "Street           1460 non-null object\n",
      "Alley            91 non-null object\n",
      "LotShape         1460 non-null object\n",
      "LandContour      1460 non-null object\n",
      "Utilities        1460 non-null object\n",
      "LotConfig        1460 non-null object\n",
      "LandSlope        1460 non-null object\n",
      "Neighborhood     1460 non-null object\n",
      "Condition1       1460 non-null object\n",
      "Condition2       1460 non-null object\n",
      "BldgType         1460 non-null object\n",
      "HouseStyle       1460 non-null object\n",
      "OverallQual      1460 non-null int64\n",
      "OverallCond      1460 non-null int64\n",
      "YearBuilt        1460 non-null int64\n",
      "YearRemodAdd     1460 non-null int64\n",
      "RoofStyle        1460 non-null object\n",
      "RoofMatl         1460 non-null object\n",
      "Exterior1st      1460 non-null object\n",
      "Exterior2nd      1460 non-null object\n",
      "MasVnrType       1452 non-null object\n",
      "MasVnrArea       1452 non-null float64\n",
      "ExterQual        1460 non-null object\n",
      "ExterCond        1460 non-null object\n",
      "Foundation       1460 non-null object\n",
      "BsmtQual         1423 non-null object\n",
      "BsmtCond         1423 non-null object\n",
      "BsmtExposure     1422 non-null object\n",
      "BsmtFinType1     1423 non-null object\n",
      "BsmtFinSF1       1460 non-null int64\n",
      "BsmtFinType2     1422 non-null object\n",
      "BsmtFinSF2       1460 non-null int64\n",
      "BsmtUnfSF        1460 non-null int64\n",
      "TotalBsmtSF      1460 non-null int64\n",
      "Heating          1460 non-null object\n",
      "HeatingQC        1460 non-null object\n",
      "CentralAir       1460 non-null object\n",
      "Electrical       1459 non-null object\n",
      "1stFlrSF         1460 non-null int64\n",
      "2ndFlrSF         1460 non-null int64\n",
      "LowQualFinSF     1460 non-null int64\n",
      "GrLivArea        1460 non-null int64\n",
      "BsmtFullBath     1460 non-null int64\n",
      "BsmtHalfBath     1460 non-null int64\n",
      "FullBath         1460 non-null int64\n",
      "HalfBath         1460 non-null int64\n",
      "BedroomAbvGr     1460 non-null int64\n",
      "KitchenAbvGr     1460 non-null int64\n",
      "KitchenQual      1460 non-null object\n",
      "TotRmsAbvGrd     1460 non-null int64\n",
      "Functional       1460 non-null object\n",
      "Fireplaces       1460 non-null int64\n",
      "FireplaceQu      770 non-null object\n",
      "GarageType       1379 non-null object\n",
      "GarageYrBlt      1379 non-null float64\n",
      "GarageFinish     1379 non-null object\n",
      "GarageCars       1460 non-null int64\n",
      "GarageArea       1460 non-null int64\n",
      "GarageQual       1379 non-null object\n",
      "GarageCond       1379 non-null object\n",
      "PavedDrive       1460 non-null object\n",
      "WoodDeckSF       1460 non-null int64\n",
      "OpenPorchSF      1460 non-null int64\n",
      "EnclosedPorch    1460 non-null int64\n",
      "3SsnPorch        1460 non-null int64\n",
      "ScreenPorch      1460 non-null int64\n",
      "PoolArea         1460 non-null int64\n",
      "PoolQC           7 non-null object\n",
      "Fence            281 non-null object\n",
      "MiscFeature      54 non-null object\n",
      "MiscVal          1460 non-null int64\n",
      "MoSold           1460 non-null int64\n",
      "YrSold           1460 non-null int64\n",
      "SaleType         1460 non-null object\n",
      "SaleCondition    1460 non-null object\n",
      "SalePrice        1460 non-null int64\n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see one of the categorical variable say `SaleCondition`, which we are later converting to one-hot-encoding along with all other categorical variables, as **XGBoost** alogrithm and **ElasticNet** accept only numerical variables/features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Normal', 'Abnorml', 'Partial', 'AdjLand', 'Alloca', 'Family'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SaleCondition'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Normal     1198\n",
       "Partial     125\n",
       "Abnorml     101\n",
       "Family       20\n",
       "Alloca       12\n",
       "AdjLand       4\n",
       "Name: SaleCondition, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SaleCondition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PoolQC         0.995\n",
       "MiscFeature    0.963\n",
       "Alley          0.938\n",
       "Fence          0.808\n",
       "FireplaceQu    0.473\n",
       "LotFrontage    0.177\n",
       "GarageCond     0.055\n",
       "GarageType     0.055\n",
       "GarageYrBlt    0.055\n",
       "GarageFinish   0.055\n",
       "GarageQual     0.055\n",
       "BsmtExposure   0.026\n",
       "BsmtFinType2   0.026\n",
       "BsmtFinType1   0.025\n",
       "BsmtCond       0.025\n",
       "BsmtQual       0.025\n",
       "MasVnrArea     0.005\n",
       "MasVnrType     0.005\n",
       "Electrical     0.001\n",
       "Utilities      0.000\n",
       "YearRemodAdd   0.000\n",
       "MSSubClass     0.000\n",
       "Foundation     0.000\n",
       "ExterCond      0.000\n",
       "ExterQual      0.000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.isnull().sum() / len(df)).sort_values(ascending=False)[0:25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The good news is that we have many features to play with (81), the bad news is that 19 features have missing values, and 4 of them have over 80% missing values. For any feature, if it is missing 80% of values, it can’t be that important, therefore, I decided to remove these 4 features along with ID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['PoolQC', 'MiscFeature', 'Alley', 'Fence', 'Id'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 76)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Features\n",
    "\n",
    "#### Target feature distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 180921.20 and sigma = 79415.29\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python365\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Sale Price distribution')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['SalePrice'] , fit=norm)\n",
    "\n",
    "# Get the fitted parameters used by the function\n",
    "(mu, sigma) = norm.fit(df['SalePrice'])\n",
    "\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "#Now plot the distribution\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Sale Price distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Get also the QQ-plot\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(df['SalePrice'], plot=plt)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target feature - SalePrice is right skewed. As linear models like normally distributed data , we will transform SalePrice and make it more normally distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python365\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 12.02 and sigma = 0.40\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.log1p(df['SalePrice']) , fit=norm)\n",
    "\n",
    "# Get the fitted parameters used by the function\n",
    "(mu, sigma) = norm.fit(np.log1p(df['SalePrice']))\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "#Now plot the distribution\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('log(Sale Price+1) distribution')\n",
    "\n",
    "#Get also the QQ-plot\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(np.log1p(df['SalePrice']), plot=plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation between numeric features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'Pearson Correlation Heatmap')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.set_option('precision',2)\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(df.drop(['SalePrice'],axis=1).corr(), square=True)\n",
    "plt.suptitle(\"Pearson Correlation Heatmap\")\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There exists strong correlations between some of the features. For example, GarageYrBlt and YearBuilt, TotRmsAbvGrd and GrLivArea, GarageArea and GarageCars are strongly correlated. They actually express more or less the same thing. I will let **ElasticNetCV** to help reduce redundancy later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation between SalePrice and the other numeric features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_with_sale_price = df.corr()[\"SalePrice\"].sort_values(ascending=False)\n",
    "plt.figure(figsize=(14,6))\n",
    "corr_with_sale_price.drop(\"SalePrice\").plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation of `SalePrice` with `OverallQual` is the greatest (around 0.8). Also `GrLivArea` presents a correlation of over 0.7, and `GarageCars` presents a correlation of over 0.6. Let’s look at these 4 features in more detail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df[['SalePrice', 'OverallQual', 'GrLivArea', 'GarageCars']])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Log transform features that have a highly skewed distribution (skew > 0.75)\n",
    "\n",
    "\n",
    "- Dummy coding categorical features\n",
    "\n",
    "\n",
    "- Fill NaN with the mean of the column\n",
    "\n",
    "\n",
    "- Train and test sets split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"SalePrice\"] = np.log1p(df[\"SalePrice\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>...</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.000</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>12.248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.000</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>12.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.000</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>12.317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.000</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>11.849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.000</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>12.429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea Street LotShape LandContour  \\\n",
       "0          60       RL       65.000     8450   Pave      Reg         Lvl   \n",
       "1          20       RL       80.000     9600   Pave      Reg         Lvl   \n",
       "2          60       RL       68.000    11250   Pave      IR1         Lvl   \n",
       "3          70       RL       60.000     9550   Pave      IR1         Lvl   \n",
       "4          60       RL       84.000    14260   Pave      IR1         Lvl   \n",
       "\n",
       "  Utilities LotConfig LandSlope    ...    EnclosedPorch 3SsnPorch ScreenPorch  \\\n",
       "0    AllPub    Inside       Gtl    ...                0         0           0   \n",
       "1    AllPub       FR2       Gtl    ...                0         0           0   \n",
       "2    AllPub    Inside       Gtl    ...                0         0           0   \n",
       "3    AllPub    Corner       Gtl    ...              272         0           0   \n",
       "4    AllPub       FR2       Gtl    ...                0         0           0   \n",
       "\n",
       "  PoolArea MiscVal  MoSold  YrSold  SaleType  SaleCondition SalePrice  \n",
       "0        0       0       2    2008        WD         Normal    12.248  \n",
       "1        0       0       5    2007        WD         Normal    12.109  \n",
       "2        0       0       9    2008        WD         Normal    12.317  \n",
       "3        0       0       2    2006        WD        Abnorml    11.849  \n",
       "4        0       0      12    2008        WD         Normal    12.429  \n",
       "\n",
       "[5 rows x 76 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#log transform skewed numeric features:\n",
    "numeric_feats = df.dtypes[df.dtypes != \"object\"].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.base.Index"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(numeric_feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',\n",
       "       'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',\n",
       "       'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF',\n",
       "       'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath',\n",
       "       'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces',\n",
       "       'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal',\n",
       "       'MoSold', 'YrSold', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(numeric_feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "skewed_feats = df[numeric_feats].apply(lambda x: skew(x.dropna())) #compute skewness\n",
    "skewed_feats = skewed_feats[skewed_feats > 0.75]\n",
    "skewed_feats = skewed_feats.index\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSSubClass', 'LotFrontage', 'LotArea', 'MasVnrArea', 'BsmtFinSF1',\n",
       "       'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF',\n",
       "       'LowQualFinSF', 'GrLivArea', 'BsmtHalfBath', 'KitchenAbvGr',\n",
       "       'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch',\n",
       "       'ScreenPorch', 'PoolArea', 'MiscVal'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skewed_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(skewed_feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[skewed_feats] = np.log1p(df[skewed_feats])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>...</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.111</td>\n",
       "      <td>RL</td>\n",
       "      <td>4.190</td>\n",
       "      <td>9.042</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>12.248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.045</td>\n",
       "      <td>RL</td>\n",
       "      <td>4.394</td>\n",
       "      <td>9.170</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>12.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.111</td>\n",
       "      <td>RL</td>\n",
       "      <td>4.234</td>\n",
       "      <td>9.328</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>12.317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.263</td>\n",
       "      <td>RL</td>\n",
       "      <td>4.111</td>\n",
       "      <td>9.164</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>5.609</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>11.849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.111</td>\n",
       "      <td>RL</td>\n",
       "      <td>4.443</td>\n",
       "      <td>9.565</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>12.429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea Street LotShape LandContour  \\\n",
       "0       4.111       RL        4.190    9.042   Pave      Reg         Lvl   \n",
       "1       3.045       RL        4.394    9.170   Pave      Reg         Lvl   \n",
       "2       4.111       RL        4.234    9.328   Pave      IR1         Lvl   \n",
       "3       4.263       RL        4.111    9.164   Pave      IR1         Lvl   \n",
       "4       4.111       RL        4.443    9.565   Pave      IR1         Lvl   \n",
       "\n",
       "  Utilities LotConfig LandSlope    ...    EnclosedPorch 3SsnPorch ScreenPorch  \\\n",
       "0    AllPub    Inside       Gtl    ...            0.000     0.000       0.000   \n",
       "1    AllPub       FR2       Gtl    ...            0.000     0.000       0.000   \n",
       "2    AllPub    Inside       Gtl    ...            0.000     0.000       0.000   \n",
       "3    AllPub    Corner       Gtl    ...            5.609     0.000       0.000   \n",
       "4    AllPub       FR2       Gtl    ...            0.000     0.000       0.000   \n",
       "\n",
       "  PoolArea MiscVal  MoSold  YrSold  SaleType  SaleCondition SalePrice  \n",
       "0    0.000   0.000       2    2008        WD         Normal    12.248  \n",
       "1    0.000   0.000       5    2007        WD         Normal    12.109  \n",
       "2    0.000   0.000       9    2008        WD         Normal    12.317  \n",
       "3    0.000   0.000       2    2006        WD        Abnorml    11.849  \n",
       "4    0.000   0.000      12    2008        WD         Normal    12.429  \n",
       "\n",
       "[5 rows x 76 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.111</td>\n",
       "      <td>4.190</td>\n",
       "      <td>9.042</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>5.283</td>\n",
       "      <td>6.561</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.045</td>\n",
       "      <td>4.394</td>\n",
       "      <td>9.170</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.887</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.111</td>\n",
       "      <td>4.234</td>\n",
       "      <td>9.328</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>5.094</td>\n",
       "      <td>6.188</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.263</td>\n",
       "      <td>4.111</td>\n",
       "      <td>9.164</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.380</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.111</td>\n",
       "      <td>4.443</td>\n",
       "      <td>9.565</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>5.861</td>\n",
       "      <td>6.486</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 276 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0       4.111        4.190    9.042            7            5       2003   \n",
       "1       3.045        4.394    9.170            6            8       1976   \n",
       "2       4.111        4.234    9.328            7            5       2001   \n",
       "3       4.263        4.111    9.164            7            5       1915   \n",
       "4       4.111        4.443    9.565            8            5       2000   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2          ...            \\\n",
       "0          2003       5.283       6.561       0.000          ...             \n",
       "1          1976       0.000       6.887       0.000          ...             \n",
       "2          2002       5.094       6.188       0.000          ...             \n",
       "3          1970       0.000       5.380       0.000          ...             \n",
       "4          2000       5.861       6.486       0.000          ...             \n",
       "\n",
       "   SaleType_ConLw  SaleType_New  SaleType_Oth  SaleType_WD  \\\n",
       "0               0             0             0            1   \n",
       "1               0             0             0            1   \n",
       "2               0             0             0            1   \n",
       "3               0             0             0            1   \n",
       "4               0             0             0            1   \n",
       "\n",
       "   SaleCondition_Abnorml  SaleCondition_AdjLand  SaleCondition_Alloca  \\\n",
       "0                      0                      0                     0   \n",
       "1                      0                      0                     0   \n",
       "2                      0                      0                     0   \n",
       "3                      1                      0                     0   \n",
       "4                      0                      0                     0   \n",
       "\n",
       "   SaleCondition_Family  SaleCondition_Normal  SaleCondition_Partial  \n",
       "0                     0                     1                      0  \n",
       "1                     0                     1                      0  \n",
       "2                     0                     1                      0  \n",
       "3                     0                     0                      0  \n",
       "4                     0                     1                      0  \n",
       "\n",
       "[5 rows x 276 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 276)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.fillna(df.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['SalePrice'], axis = 1)\n",
    "y = df['SalePrice']\n",
    "X_train, X_test, y_train, y_test  = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ElasticNet\n",
    "\n",
    "- **Ridge** and **Lasso** regression are regularized linear regression models.\n",
    "\n",
    "\n",
    "- **ElasticNet** is essentially a Lasso/Ridge hybrid, that entails the minimization of an objective function that includes both L1 (Lasso) and L2 (Ridge) norms.\n",
    "\n",
    "\n",
    "- **ElasticNet** is useful when there are multiple features which are correlated with one another.\n",
    "\n",
    "\n",
    "- The class **ElasticNetCV** can be used to set the parameters alpha (α) and l1_ratio (ρ) by cross-validation.\n",
    "\n",
    "\n",
    "- **ElasticNetCV:** ElasticNet model with best model selection by cross-validation.\n",
    "\n",
    "Let’s see what ElasticNetCV is going to select for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal alpha: 0.00013634\n",
      "Optimal l1_ratio: 0.700\n",
      "Number of iterations 84\n"
     ]
    }
   ],
   "source": [
    "cv_model = ElasticNetCV(l1_ratio=[.1, .5, .7, .9, .95, .99, 1], eps=1e-3, n_alphas=100, fit_intercept=True, \n",
    "                        normalize=True, precompute='auto', max_iter=2000, tol=0.0001, cv=6, \n",
    "                        copy_X=True, verbose=0, n_jobs=-1, positive=False, random_state=0)\n",
    "               \n",
    "cv_model.fit(X_train, y_train)\n",
    "print('Optimal alpha: %.8f'%cv_model.alpha_)\n",
    "print('Optimal l1_ratio: %.3f'%cv_model.l1_ratio_)\n",
    "print('Number of iterations %d'%cv_model.n_iter_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** 0< The optimal l1_ratio <1 , indicating the penalty is a combination of L1 and L2, that is, the combination of Lasso and Ridge."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train r2 score:  0.935231601879496\n",
      "Test r2 score:  0.8300355301028952\n",
      "Train RMSE: 0.0963\n",
      "Test RMSE: 0.1604\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = cv_model.predict(X_train)\n",
    "y_test_pred = cv_model.predict(X_test)\n",
    "print('Train r2 score: ', r2_score(y_train_pred, y_train))\n",
    "print('Test r2 score: ', r2_score(y_test, y_test_pred))\n",
    "\n",
    "train_mse = mean_squared_error(y_train_pred, y_train)\n",
    "test_mse = mean_squared_error(y_test_pred, y_test)\n",
    "train_rmse = np.sqrt(train_mse)\n",
    "test_rmse = np.sqrt(test_mse)\n",
    "print('Train RMSE: %.4f' % train_rmse)\n",
    "print('Test RMSE: %.4f' % test_rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The RMSE here is actually RMSLE ( Root Mean Squared Logarithmic Error). Because we have taken the log of the actual values. Here is a [nice write up explaining the differences between RMSE and RMSLE.](https://www.quora.com/What-is-the-difference-between-an-RMSE-and-RMSLE-logarithmic-error-and-does-a-high-RMSE-imply-low-RMSLE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113 features, reduction of 58.91%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f6cd3cd080>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = pd.Series(index = X_train.columns, data = np.abs(cv_model.coef_))\n",
    "\n",
    "n_selected_features = (feature_importance>0).sum()\n",
    "\n",
    "print('{0:d} features, reduction of {1:2.2f}%'.format(\n",
    "    n_selected_features,(1-n_selected_features/len(feature_importance))*100))\n",
    "\n",
    "feature_importance.sort_values().tail(30).plot(kind = 'bar', figsize = (12,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A reduction of 58.91% features looks productive. The top 4 most important features selected by ElasticNetCV are `Condition2_PosN`, `MSZoning_C(all)`, `Exterior1st_BrkComm` & `GrLivArea`. We are going to see how these features compare with those selected by Xgboost."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first XGBoost model, we start from default parameters.Also note that here I am using the scikit_learnAPI of XGBoost and not native PythonAPI of XGBoost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python365\\lib\\site-packages\\xgboost\\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train r2 score:  0.9546691336034475\n",
      "Test r2 score:  0.8856612816972917\n",
      "Train RMSE: 0.0820\n",
      "Test RMSE: 0.1316\n"
     ]
    }
   ],
   "source": [
    "xgb_model1 = XGBRegressor()\n",
    "xgb_model1.fit(X_train, y_train, verbose=False)\n",
    "y_train_pred1 = xgb_model1.predict(X_train)\n",
    "y_pred1 = xgb_model1.predict(X_test)\n",
    "\n",
    "print('Train r2 score: ', r2_score(y_train_pred1, y_train))\n",
    "print('Test r2 score: ', r2_score(y_test, y_pred1))\n",
    "train_mse1 = mean_squared_error(y_train_pred1, y_train)\n",
    "test_mse1 = mean_squared_error(y_pred1, y_test)\n",
    "train_rmse1 = np.sqrt(train_mse1)\n",
    "test_rmse1 = np.sqrt(test_mse1)\n",
    "print('Train RMSE: %.4f' % train_rmse1)\n",
    "print('Test RMSE: %.4f' % test_rmse1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is already way better an the model selected by **ElasticNetCV!**\n",
    "\n",
    "In the second XGBoost model, we gradually add a few parameters that suppose to improve model’s accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python365\\lib\\site-packages\\xgboost\\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train r2 score:  0.9678837547788781\n",
      "Test r2 score:  0.8931330796873692\n",
      "Train RMSE: 0.0699\n",
      "Test RMSE: 0.1272\n"
     ]
    }
   ],
   "source": [
    "xgb_model2 = XGBRegressor(n_estimators=1000)\n",
    "xgb_model2.fit(X_train, y_train, early_stopping_rounds=5, \n",
    "             eval_set=[(X_test, y_test)], verbose=False)\n",
    "y_train_pred2 = xgb_model2.predict(X_train)\n",
    "y_pred2 = xgb_model2.predict(X_test)\n",
    "\n",
    "print('Train r2 score: ', r2_score(y_train_pred2, y_train))\n",
    "print('Test r2 score: ', r2_score(y_test, y_pred2))\n",
    "train_mse2 = mean_squared_error(y_train_pred2, y_train)\n",
    "test_mse2 = mean_squared_error(y_pred2, y_test)\n",
    "train_rmse2 = np.sqrt(train_mse2)\n",
    "test_rmse2 = np.sqrt(test_mse2)\n",
    "print('Train RMSE: %.4f' % train_rmse2)\n",
    "print('Test RMSE: %.4f' % test_rmse2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There was again an improvement!\n",
    "\n",
    "The third Xgboost model, we add a learning rate, hopefully it will yield a more accurate model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python365\\lib\\site-packages\\xgboost\\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train r2 score:  0.9479569687487649\n",
      "Test r2 score:  0.8840040768045745\n",
      "Train RMSE: 0.0869\n",
      "Test RMSE: 0.1325\n"
     ]
    }
   ],
   "source": [
    "xgb_model3 = XGBRegressor(n_estimators=1000, learning_rate=0.05)\n",
    "xgb_model3.fit(X_train, y_train, early_stopping_rounds=5, \n",
    "             eval_set=[(X_test, y_test)], verbose=False)\n",
    "y_train_pred3 = xgb_model3.predict(X_train)\n",
    "y_pred3 = xgb_model3.predict(X_test)\n",
    "\n",
    "print('Train r2 score: ', r2_score(y_train_pred3, y_train))\n",
    "print('Test r2 score: ', r2_score(y_test, y_pred3))\n",
    "train_mse3 = mean_squared_error(y_train_pred3, y_train)\n",
    "test_mse3 = mean_squared_error(y_pred3, y_test)\n",
    "train_rmse3 = np.sqrt(train_mse3)\n",
    "test_rmse3 = np.sqrt(test_mse3)\n",
    "print('Train RMSE: %.4f' % train_rmse3)\n",
    "print('Test RMSE: %.4f' % test_rmse3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, there was no improvement. I concluded that xgb_model2 is the best model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('LotArea', 68),\n",
       "             ('GrLivArea', 64),\n",
       "             ('OverallQual', 56),\n",
       "             ('TotalBsmtSF', 45),\n",
       "             ('OverallCond', 45),\n",
       "             ('GarageYrBlt', 33),\n",
       "             ('BsmtUnfSF', 30),\n",
       "             ('LotFrontage', 29),\n",
       "             ('YearRemodAdd', 27),\n",
       "             ('BsmtFinSF1', 26),\n",
       "             ('YearBuilt', 24),\n",
       "             ('MSSubClass', 24),\n",
       "             ('GarageArea', 23),\n",
       "             ('MoSold', 20),\n",
       "             ('2ndFlrSF', 19),\n",
       "             ('1stFlrSF', 18),\n",
       "             ('OpenPorchSF', 17),\n",
       "             ('MSZoning_C (all)', 16),\n",
       "             ('EnclosedPorch', 15),\n",
       "             ('Functional_Typ', 14),\n",
       "             ('SaleCondition_Abnorml', 13),\n",
       "             ('Condition1_Norm', 12),\n",
       "             ('WoodDeckSF', 11),\n",
       "             ('Neighborhood_Crawfor', 11),\n",
       "             ('ScreenPorch', 11),\n",
       "             ('PoolArea', 11),\n",
       "             ('MasVnrArea', 10),\n",
       "             ('KitchenAbvGr', 8),\n",
       "             ('Condition1_Artery', 8),\n",
       "             ('Exterior1st_BrkFace', 8),\n",
       "             ('MSZoning_FV', 8),\n",
       "             ('CentralAir_N', 7),\n",
       "             ('BedroomAbvGr', 7),\n",
       "             ('Neighborhood_BrkSide', 7),\n",
       "             ('Fireplaces', 6),\n",
       "             ('BsmtFullBath', 6),\n",
       "             ('SaleType_New', 6),\n",
       "             ('YrSold', 6),\n",
       "             ('BsmtFinSF2', 6),\n",
       "             ('GarageCars', 5),\n",
       "             ('KitchenQual_Ex', 5),\n",
       "             ('BsmtExposure_Gd', 5),\n",
       "             ('LotShape_Reg', 5),\n",
       "             ('Neighborhood_Mitchel', 5),\n",
       "             ('BsmtQual_Gd', 5),\n",
       "             ('Neighborhood_ClearCr', 5),\n",
       "             ('Neighborhood_Edwards', 5),\n",
       "             ('BsmtFinType1_ALQ', 5),\n",
       "             ('BsmtFinType1_Rec', 5),\n",
       "             ('GarageType_Attchd', 4),\n",
       "             ('GarageFinish_Unf', 4),\n",
       "             ('MasVnrType_BrkFace', 4),\n",
       "             ('BsmtExposure_No', 4),\n",
       "             ('FullBath', 4),\n",
       "             ('BsmtHalfBath', 4),\n",
       "             ('Exterior1st_AsbShng', 4),\n",
       "             ('RoofStyle_Gable', 4),\n",
       "             ('LotConfig_CulDSac', 4),\n",
       "             ('Foundation_BrkTil', 4),\n",
       "             ('FireplaceQu_Po', 4),\n",
       "             ('Exterior1st_BrkComm', 3),\n",
       "             ('MSZoning_RL', 3),\n",
       "             ('BsmtFinType1_GLQ', 3),\n",
       "             ('GarageQual_TA', 3),\n",
       "             ('HalfBath', 3),\n",
       "             ('HeatingQC_Fa', 3),\n",
       "             ('Exterior2nd_Wd Shng', 3),\n",
       "             ('Condition1_PosN', 3),\n",
       "             ('RoofMatl_WdShngl', 3),\n",
       "             ('LotShape_IR1', 3),\n",
       "             ('MSZoning_RM', 2),\n",
       "             ('BsmtQual_Ex', 2),\n",
       "             ('GarageType_Detchd', 2),\n",
       "             ('Neighborhood_Sawyer', 2),\n",
       "             ('Condition1_PosA', 2),\n",
       "             ('Neighborhood_OldTown', 2),\n",
       "             ('Neighborhood_NAmes', 2),\n",
       "             ('Condition2_Norm', 2),\n",
       "             ('Exterior1st_VinylSd', 2),\n",
       "             ('MSZoning_RH', 2),\n",
       "             ('FireplaceQu_TA', 2),\n",
       "             ('MasVnrType_BrkCmn', 2),\n",
       "             ('Neighborhood_NWAmes', 2),\n",
       "             ('SaleType_COD', 2),\n",
       "             ('LandContour_Low', 2),\n",
       "             ('BsmtFinType1_LwQ', 2),\n",
       "             ('Exterior1st_HdBoard', 2),\n",
       "             ('Exterior1st_Wd Sdng', 2),\n",
       "             ('BsmtFinType1_BLQ', 2),\n",
       "             ('Exterior1st_MetalSd', 2),\n",
       "             ('BsmtQual_Fa', 2),\n",
       "             ('LotConfig_FR2', 2),\n",
       "             ('Condition1_RRAe', 2),\n",
       "             ('BsmtFinType2_BLQ', 2),\n",
       "             ('Functional_Maj2', 1),\n",
       "             ('Heating_Grav', 1),\n",
       "             ('GarageCond_TA', 1),\n",
       "             ('PavedDrive_P', 1),\n",
       "             ('PavedDrive_Y', 1),\n",
       "             ('Functional_Min1', 1),\n",
       "             ('MasVnrType_Stone', 1),\n",
       "             ('HeatingQC_TA', 1),\n",
       "             ('RoofStyle_Flat', 1),\n",
       "             ('SaleCondition_Family', 1),\n",
       "             ('ExterCond_Fa', 1),\n",
       "             ('GarageQual_Fa', 1),\n",
       "             ('GarageFinish_RFn', 1),\n",
       "             ('BsmtCond_TA', 1),\n",
       "             ('BldgType_Duplex', 1),\n",
       "             ('BsmtQual_TA', 1),\n",
       "             ('LotConfig_Inside', 1),\n",
       "             ('LowQualFinSF', 1),\n",
       "             ('SaleCondition_Normal', 1),\n",
       "             ('LandContour_HLS', 1),\n",
       "             ('GarageType_2Types', 1),\n",
       "             ('GarageFinish_Fin', 1),\n",
       "             ('BsmtCond_Fa', 1),\n",
       "             ('Exterior1st_CemntBd', 1)])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import OrderedDict\n",
    "OrderedDict(sorted(xgb_model2.get_booster().get_fscore().items(), key=lambda t: t[1], reverse=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The top 4 most important features selected by XGBoost are `LotArea`, `GrLivArea`, `OverallQual` & `TotalBsmtSF`.\n",
    "\n",
    "There is only one feature `GrLivArea` was selected by both ElasticNetCV and XGBoost."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly see XGBoost performs better than regularised linear regression in this case."
   ]
  }
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