House Price Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advance House Prices Regression\n",
    "#### Data Source: Kaggle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary:\n",
    "Suppose you want to buy a new house, what are the factors that you will consider. My top factors would be the Number of Rooms, Number of Bathrooms, Street Access, whether it has a Garage. But if the house has a each of these features, it would be quite expensive. \n",
    "\n",
    "So eventually the house you buy has to be a balance between the features you want, and the price you are willing to pay. The dataset we have explains the price of 1500 such house with all the features they have, so that you can analyze the features and their effect they have on the price. \n",
    "\n",
    "For a buyer you can estimate the price you should be paying for a property, and for a seller you can estimate a quoting price for a property.\n",
    "\n",
    "\n",
    "#### Objective:\n",
    "To predict house prices based on 79 explanatory variables. \n",
    "\n",
    "#### DataSet:\n",
    "The Ames Housing dataset was compiled by Dean De Cock for use in data science education. It's an incredible alternative for data scientists looking for a modernized and expanded version of the often cited Boston Housing dataset. \n",
    "\n",
    "#### Procedure:\n",
    "1. Data exploration for missing values, outliers\n",
    "2. A thorough data cleaning exercise based on business understanding\n",
    "3. Feature engineer to create intuitive new features\n",
    "4. One hot encoding to convert categorical features to encoded columns containing zeroes and one's\n",
    "5. Training test split\n",
    "6. Trying basic models- first pass\n",
    "7. 37 skewed numerical features to Box Cox transform, to improve accuracy\n",
    "8. Testing a custom stacked regression model, which aggregates the prediction from various models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Importing Important Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (12,8)\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Reading the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data has: 1460 rows and 80 columns\n",
      "Test data has: 1459 rows and 79 columns\n"
     ]
    }
   ],
   "source": [
    "# Reading test and train data\n",
    "df_train = pd.read_csv(\"train.csv\",header = 0, index_col= 0)\n",
    "df_test = pd.read_csv(\"test.csv\",header = 0, index_col= 0)\n",
    "\n",
    "print(\"Train data has:\",df_train.shape[0], \"rows and\",df_train.shape[1], \"columns\")\n",
    "print(\"Test data has:\",df_test.shape[0], \"rows and\",df_test.shape[1], \"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A compiled dataset for both X and Y features can help us create a function which can be cleaned\n",
    "df_train1 = df_train.drop(\"SalePrice\", axis = 1)\n",
    "df = df_train1.append(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 79)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Checking data frame summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt',\n",
       "       'YearRemodAdd', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF',\n",
       "       '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath',\n",
       "       'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr',\n",
       "       'TotRmsAbvGrd', 'Fireplaces', 'GarageCars', 'GarageArea', 'WoodDeckSF',\n",
       "       'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea',\n",
       "       'MiscVal', 'MoSold', 'YrSold', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All the integer datatypes\n",
    "df_train.select_dtypes(\"int64\").columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['LotFrontage', 'MasVnrArea', 'GarageYrBlt'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All the float datatypes\n",
    "df_train.select_dtypes(\"float64\").columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities',\n",
       "       'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2',\n",
       "       'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st',\n",
       "       'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation',\n",
       "       'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',\n",
       "       'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual',\n",
       "       'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature',\n",
       "       'SaleType', 'SaleCondition'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All the string datatypes\n",
    "df_train.select_dtypes(\"object\").columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</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>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1201.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1452.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>56.897260</td>\n",
       "      <td>70.049958</td>\n",
       "      <td>10516.828082</td>\n",
       "      <td>6.099315</td>\n",
       "      <td>5.575342</td>\n",
       "      <td>1971.267808</td>\n",
       "      <td>1984.865753</td>\n",
       "      <td>103.685262</td>\n",
       "      <td>443.639726</td>\n",
       "      <td>46.549315</td>\n",
       "      <td>...</td>\n",
       "      <td>94.244521</td>\n",
       "      <td>46.660274</td>\n",
       "      <td>21.954110</td>\n",
       "      <td>3.409589</td>\n",
       "      <td>15.060959</td>\n",
       "      <td>2.758904</td>\n",
       "      <td>43.489041</td>\n",
       "      <td>6.321918</td>\n",
       "      <td>2007.815753</td>\n",
       "      <td>180921.195890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>42.300571</td>\n",
       "      <td>24.284752</td>\n",
       "      <td>9981.264932</td>\n",
       "      <td>1.382997</td>\n",
       "      <td>1.112799</td>\n",
       "      <td>30.202904</td>\n",
       "      <td>20.645407</td>\n",
       "      <td>181.066207</td>\n",
       "      <td>456.098091</td>\n",
       "      <td>161.319273</td>\n",
       "      <td>...</td>\n",
       "      <td>125.338794</td>\n",
       "      <td>66.256028</td>\n",
       "      <td>61.119149</td>\n",
       "      <td>29.317331</td>\n",
       "      <td>55.757415</td>\n",
       "      <td>40.177307</td>\n",
       "      <td>496.123024</td>\n",
       "      <td>2.703626</td>\n",
       "      <td>1.328095</td>\n",
       "      <td>79442.502883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>34900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7553.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1954.000000</td>\n",
       "      <td>1967.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>129975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9478.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>383.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>163000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11601.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>712.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>214000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>215245.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5644.000000</td>\n",
       "      <td>1474.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>857.000000</td>\n",
       "      <td>547.000000</td>\n",
       "      <td>552.000000</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>15500.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        MSSubClass  LotFrontage        LotArea  OverallQual  OverallCond  \\\n",
       "count  1460.000000  1201.000000    1460.000000  1460.000000  1460.000000   \n",
       "mean     56.897260    70.049958   10516.828082     6.099315     5.575342   \n",
       "std      42.300571    24.284752    9981.264932     1.382997     1.112799   \n",
       "min      20.000000    21.000000    1300.000000     1.000000     1.000000   \n",
       "25%      20.000000    59.000000    7553.500000     5.000000     5.000000   \n",
       "50%      50.000000    69.000000    9478.500000     6.000000     5.000000   \n",
       "75%      70.000000    80.000000   11601.500000     7.000000     6.000000   \n",
       "max     190.000000   313.000000  215245.000000    10.000000     9.000000   \n",
       "\n",
       "         YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1   BsmtFinSF2  ...  \\\n",
       "count  1460.000000   1460.000000  1452.000000  1460.000000  1460.000000  ...   \n",
       "mean   1971.267808   1984.865753   103.685262   443.639726    46.549315  ...   \n",
       "std      30.202904     20.645407   181.066207   456.098091   161.319273  ...   \n",
       "min    1872.000000   1950.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%    1954.000000   1967.000000     0.000000     0.000000     0.000000  ...   \n",
       "50%    1973.000000   1994.000000     0.000000   383.500000     0.000000  ...   \n",
       "75%    2000.000000   2004.000000   166.000000   712.250000     0.000000  ...   \n",
       "max    2010.000000   2010.000000  1600.000000  5644.000000  1474.000000  ...   \n",
       "\n",
       "        WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  ScreenPorch  \\\n",
       "count  1460.000000  1460.000000    1460.000000  1460.000000  1460.000000   \n",
       "mean     94.244521    46.660274      21.954110     3.409589    15.060959   \n",
       "std     125.338794    66.256028      61.119149    29.317331    55.757415   \n",
       "min       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
       "50%       0.000000    25.000000       0.000000     0.000000     0.000000   \n",
       "75%     168.000000    68.000000       0.000000     0.000000     0.000000   \n",
       "max     857.000000   547.000000     552.000000   508.000000   480.000000   \n",
       "\n",
       "          PoolArea       MiscVal       MoSold       YrSold      SalePrice  \n",
       "count  1460.000000   1460.000000  1460.000000  1460.000000    1460.000000  \n",
       "mean      2.758904     43.489041     6.321918  2007.815753  180921.195890  \n",
       "std      40.177307    496.123024     2.703626     1.328095   79442.502883  \n",
       "min       0.000000      0.000000     1.000000  2006.000000   34900.000000  \n",
       "25%       0.000000      0.000000     5.000000  2007.000000  129975.000000  \n",
       "50%       0.000000      0.000000     6.000000  2008.000000  163000.000000  \n",
       "75%       0.000000      0.000000     8.000000  2009.000000  214000.000000  \n",
       "max     738.000000  15500.000000    12.000000  2010.000000  755000.000000  \n",
       "\n",
       "[8 rows x 37 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistical summary of all the columns with int/float values\n",
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x72 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num=df_train.select_dtypes(exclude='object')\n",
    "numcorr=num.corr()\n",
    "f,ax=plt.subplots(figsize=(17,1))\n",
    "sns.heatmap(numcorr.sort_values(by=['SalePrice'], ascending=False).head(1), cmap='Blues')\n",
    "plt.title(\" Numerical features correlation with the sale price\", weight='bold', fontsize=18)\n",
    "plt.xticks(weight='bold')\n",
    "plt.yticks(weight='bold', color='dodgerblue', rotation=0)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Missing value treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSZoning           4\n",
       "LotFrontage      486\n",
       "Alley           2721\n",
       "Utilities          2\n",
       "Exterior1st        1\n",
       "Exterior2nd        1\n",
       "MasVnrType        24\n",
       "MasVnrArea        23\n",
       "BsmtQual          81\n",
       "BsmtCond          82\n",
       "BsmtExposure      82\n",
       "BsmtFinType1      79\n",
       "BsmtFinSF1         1\n",
       "BsmtFinType2      80\n",
       "BsmtFinSF2         1\n",
       "BsmtUnfSF          1\n",
       "TotalBsmtSF        1\n",
       "Electrical         1\n",
       "BsmtFullBath       2\n",
       "BsmtHalfBath       2\n",
       "KitchenQual        1\n",
       "Functional         2\n",
       "FireplaceQu     1420\n",
       "GarageType       157\n",
       "GarageYrBlt      159\n",
       "GarageFinish     159\n",
       "GarageCars         1\n",
       "GarageArea         1\n",
       "GarageQual       159\n",
       "GarageCond       159\n",
       "PoolQC          2909\n",
       "Fence           2348\n",
       "MiscFeature     2814\n",
       "SaleType           1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking which columns have missing values\n",
    "df1 = df[df.columns[df.isnull().sum()>0]]\n",
    "len(df1.index)-df1.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a cleaining function to clean the various columns based on different logics\n",
    "def cleantable(df):\n",
    "    # Masonry vaneer is a type of front wall add, and NA means absense, so making type as None and area as 0\n",
    "    df['MasVnrType'].fillna(\"None\", inplace = True)\n",
    "    df['MasVnrArea'].fillna(0, inplace = True)\n",
    "    # Garage related Nas are due to absense of Garage so converting to None for text and 0 for numeric\n",
    "    df['GarageType'].fillna(\"None\", inplace = True)\n",
    "    df['GarageYrBlt'].fillna(0, inplace = True)\n",
    "    df['GarageFinish'].fillna(\"None\", inplace = True)\n",
    "    df['GarageQual'].fillna(\"None\", inplace = True)\n",
    "    df['GarageCond'].fillna(\"None\", inplace = True)\n",
    "    df['GarageArea'].fillna(0, inplace = True)\n",
    "    df['GarageCars'].fillna(0, inplace = True)\n",
    "    # Basement Variables\n",
    "    df['BsmtQual'].fillna(\"None\", inplace = True)\n",
    "    df['BsmtCond'].fillna(\"None\", inplace = True)\n",
    "    df['BsmtFinType1'].fillna(\"None\", inplace = True)\n",
    "    df['BsmtExposure'].fillna(\"No\", inplace = True)\n",
    "    df['BsmtFinType2'].fillna(\"Unf\", inplace = True)\n",
    "    df['BsmtFinSF1'].fillna(0, inplace = True)\n",
    "    df['BsmtFinSF2'].fillna(0, inplace = True)\n",
    "    df['BsmtHalfBath'].fillna(0, inplace = True)\n",
    "    df['BsmtFullBath'].fillna(0, inplace = True)\n",
    "    df['TotalBsmtSF'].fillna(0, inplace = True)\n",
    "    df['BsmtUnfSF'].fillna(0, inplace = True)\n",
    "    # Remaininig substituting with mode\n",
    "    df['Exterior1st'].fillna('VinylSd', inplace = True)\n",
    "    df['Exterior2nd'].fillna('VinylSd', inplace = True)\n",
    "    df['Utilities'].fillna('AllPub', inplace = True)\n",
    "    df['KitchenQual'].fillna('TA', inplace = True)\n",
    "    df['MSZoning'].fillna('RL', inplace = True)\n",
    "    df['PoolQC'].fillna(\"NoPool\", inplace = True)\n",
    "    df['MiscFeature'].fillna(\"None\", inplace = True)\n",
    "    df['Electrical'].fillna(\"SBrkr\", inplace = True)\n",
    "    df['Alley'].fillna(\"None\", inplace = True)\n",
    "    df['LotFrontage'].fillna(0, inplace = True)\n",
    "    df['FireplaceQu'].fillna(\"None\", inplace = True)\n",
    "    df['Functional'].fillna('Typ', inplace = True)\n",
    "    df['SaleType'].fillna('WD', inplace = True)\n",
    "    df['Fence'].fillna(\"None\", inplace = True)\n",
    "    return(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleaning train/test data based on function defined above\n",
    "df_train_cleaned = cleantable(df_train)\n",
    "df_test_cleaned = cleantable(df_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Feature Engineering\n",
    "Year Sold - Year built = Construction Age at time of selling\n",
    "\n",
    "Year Sold - Year remodel = Remodel age at the age of selling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We are adding the above mentioned two columns\n",
    "\n",
    "df_train_cleaned['HouseAge'] = df_train_cleaned['YrSold'] - df_train_cleaned['YearBuilt']\n",
    "df_train_cleaned['RemodelAge'] = np.maximum((df_train_cleaned['YrSold'] - df_train_cleaned['YearRemodAdd']),0)\n",
    "\n",
    "df_test_cleaned['HouseAge'] = df_test_cleaned['YrSold'] - df_test_cleaned['YearBuilt']\n",
    "df_test_cleaned['RemodelAge'] = np.maximum(df_test_cleaned['YrSold'] - df_test_cleaned['YearRemodAdd'],0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the MSSubClass column to string\n",
    "df_train_cleaned['MSSubClass']= df_train_cleaned['MSSubClass'].astype(str)\n",
    "df_test_cleaned['MSSubClass']= df_test_cleaned['MSSubClass'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data has: 1460 rows and 82 columns\n",
      "Test data has: 1459 rows and 81 columns\n"
     ]
    }
   ],
   "source": [
    "# Checking the shape of data again\n",
    "print(\"Train data has:\",df_train_cleaned.shape[0], \"rows and\",df_train_cleaned.shape[1], \"columns\")\n",
    "print(\"Test data has:\",df_test_cleaned.shape[0], \"rows and\",df_test_cleaned.shape[1], \"columns\")\n",
    "\n",
    "#df_train_cleaned.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.One hot encoding for the entire dataset's categorical columns using pd.get_dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Y Train\n",
    "y_train = df_train_cleaned.SalePrice\n",
    "\n",
    "y_train_final = y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_cleaned = df_train_cleaned.drop('SalePrice',axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_train_cleaned.append(df_test_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 81)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the one hot encoding function \n",
    "from sklearn.base import TransformerMixin\n",
    "class DataFrameEncoder(TransformerMixin):\n",
    "\n",
    "    def __init__(self):\n",
    "        \"\"\"Encode the data.\n",
    "\n",
    "        Columns of data type object are appended in the list. After \n",
    "        appending Each Column of type object are taken dummies and \n",
    "        successively removed and two Dataframes are concated again.\n",
    "\n",
    "        \"\"\"\n",
    "    def fit(self, X, y=None):\n",
    "        self.object_col = []\n",
    "        for col in X.columns:\n",
    "            if(X[col].dtype == np.dtype('O')):\n",
    "                self.object_col.append(col)\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        dummy_df = pd.get_dummies(X[self.object_col],drop_first=True)\n",
    "        X = X.drop(X[self.object_col],axis=1)\n",
    "        X = pd.concat([dummy_df,X],axis=1)\n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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_150</th>\n",
       "      <th>MSSubClass_160</th>\n",
       "      <th>MSSubClass_180</th>\n",
       "      <th>MSSubClass_190</th>\n",
       "      <th>MSSubClass_20</th>\n",
       "      <th>MSSubClass_30</th>\n",
       "      <th>MSSubClass_40</th>\n",
       "      <th>MSSubClass_45</th>\n",
       "      <th>MSSubClass_50</th>\n",
       "      <th>MSSubClass_60</th>\n",
       "      <th>...</th>\n",
       "      <th>OpenPorchSF</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>HouseAge</th>\n",
       "      <th>RemodelAge</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>61</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>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>42</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>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>35</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>91</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>84</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>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 273 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    MSSubClass_150  MSSubClass_160  MSSubClass_180  MSSubClass_190  \\\n",
       "Id                                                                   \n",
       "1                0               0               0               0   \n",
       "2                0               0               0               0   \n",
       "3                0               0               0               0   \n",
       "4                0               0               0               0   \n",
       "5                0               0               0               0   \n",
       "\n",
       "    MSSubClass_20  MSSubClass_30  MSSubClass_40  MSSubClass_45  MSSubClass_50  \\\n",
       "Id                                                                              \n",
       "1               0              0              0              0              0   \n",
       "2               1              0              0              0              0   \n",
       "3               0              0              0              0              0   \n",
       "4               0              0              0              0              0   \n",
       "5               0              0              0              0              0   \n",
       "\n",
       "    MSSubClass_60  ...  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  \\\n",
       "Id                 ...                                                       \n",
       "1               1  ...           61              0          0            0   \n",
       "2               0  ...            0              0          0            0   \n",
       "3               1  ...           42              0          0            0   \n",
       "4               0  ...           35            272          0            0   \n",
       "5               1  ...           84              0          0            0   \n",
       "\n",
       "    PoolArea  MiscVal  MoSold  YrSold  HouseAge  RemodelAge  \n",
       "Id                                                           \n",
       "1          0        0       2    2008         5           5  \n",
       "2          0        0       5    2007        31          31  \n",
       "3          0        0       9    2008         7           6  \n",
       "4          0        0       2    2006        91          36  \n",
       "5          0        0      12    2008         8           8  \n",
       "\n",
       "[5 rows x 273 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = DataFrameEncoder().fit_transform(df)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checkpoint 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data back into test and train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 273)\n",
      "(1459, 273)\n",
      "(1460,)\n"
     ]
    }
   ],
   "source": [
    "X_train_final = df2[0:1460] \n",
    "X_test = df2[1460:]\n",
    "print(X_train_final.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train_final.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Trying various models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train Test split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X_train_final, y_train_final,test_size  = 0.33, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model 1: Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13502705944023644"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating the first model: Random forest\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "forest = RandomForestRegressor(n_estimators = 100,\n",
    "                              criterion = 'mse',\n",
    "                              random_state = 1,\n",
    "                              n_jobs = -1)\n",
    "\n",
    "forest.fit(X_train,Y_train)\n",
    "y_train_pred = forest.predict(X_train)\n",
    "y_test_pred = forest.predict(X_test)\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "#mean_squared_error(Y_train, y_train_pred)\n",
    "\n",
    "#Checking the test metric for accuracy as defined by Kaggle\n",
    "import math\n",
    "math.sqrt(mean_squared_error(np.log(Y_test), np.log(y_test_pred)))\n",
    "#math.sqrt(mean_squared_error(Y_test, y_test_pred)) # for log y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model 2: Gradient Boosting from sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1270228133658703"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "gb = GradientBoostingRegressor(n_estimators = 430,\n",
    "                              max_depth =4 ,\n",
    "                              random_state = 1,\n",
    "                              learning_rate = 0.03)\n",
    "\n",
    "gb.fit(X_train,Y_train)\n",
    "y_train_pred = gb.predict(X_train)\n",
    "y_test_pred = gb.predict(X_test)\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "#mean_squared_error(Y_train, y_train_pred)\n",
    "\n",
    "#Checking the test metric for accuracy as defined by Kaggle\n",
    "import math\n",
    "math.sqrt(mean_squared_error(np.log(Y_test), np.log(y_test_pred)))\n",
    "\n",
    "#math.sqrt(mean_squared_error(Y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model 3 with XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11841177805148058"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_xgb = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468, \n",
    "                             learning_rate=0.02, max_depth=4, \n",
    "                             min_child_weight=1.7817, n_estimators=800,\n",
    "                             reg_alpha=0.4640, reg_lambda=0.8571,\n",
    "                             subsample=0.5213, silent=1,\n",
    "                             random_state =4, nthread = -1)\n",
    "\n",
    "#fit model\n",
    "model_xgb.fit(X_train,Y_train)\n",
    "y_train_pred = model_xgb.predict(X_train)\n",
    "y_test_pred = model_xgb.predict(X_test)\n",
    "\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "#mean_squared_error(Y_test, y_test_pred)\n",
    "\n",
    "#Checking the test metric for accuracy as defined by Kaggle\n",
    "import math\n",
    "math.sqrt(mean_squared_error(np.log(Y_test), np.log(y_test_pred)))\n",
    "\n",
    "#math.sqrt(mean_squared_error(Y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x5400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = model_xgb.get_booster().get_score(importance_type= 'gain')\n",
    "\n",
    "from xgboost import plot_importance\n",
    "\n",
    "plot_importance(model_xgb)\n",
    "\n",
    "plt.gcf().set_size_inches(25,75)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The model shows that the features which play an important role in deciding the price of a property\n",
    "\n",
    "The most important features are\n",
    "1. The above ground living area of the property\n",
    "2. The surface area of the basement\n",
    "3. Area of the entire lot\n",
    "4. Surface area of the first floor\n",
    "5. Basement area\n",
    "6. Garage area\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalizing the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt',\n",
       "       'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF',\n",
       "       'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea',\n",
       "       'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr',\n",
       "       'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt',\n",
       "       'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal',\n",
       "       'MoSold', 'YrSold', 'HouseAge', 'RemodelAge'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trying to improve the model\n",
    "numeric_feats = df.dtypes[df.dtypes != \"object\"].index\n",
    "numeric_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "from scipy.stats import norm, skew #for some statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Skew in numerical features: \n",
      "\n"
     ]
    },
    {
     "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>Skew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>MiscVal</td>\n",
       "      <td>21.947195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PoolArea</td>\n",
       "      <td>16.898328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LotArea</td>\n",
       "      <td>12.822431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LowQualFinSF</td>\n",
       "      <td>12.088761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3SsnPorch</td>\n",
       "      <td>11.376065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>KitchenAbvGr</td>\n",
       "      <td>4.302254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BsmtFinSF2</td>\n",
       "      <td>4.146143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>EnclosedPorch</td>\n",
       "      <td>4.003891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ScreenPorch</td>\n",
       "      <td>3.946694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BsmtHalfBath</td>\n",
       "      <td>3.931594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Skew\n",
       "MiscVal        21.947195\n",
       "PoolArea       16.898328\n",
       "LotArea        12.822431\n",
       "LowQualFinSF   12.088761\n",
       "3SsnPorch      11.376065\n",
       "KitchenAbvGr    4.302254\n",
       "BsmtFinSF2      4.146143\n",
       "EnclosedPorch   4.003891\n",
       "ScreenPorch     3.946694\n",
       "BsmtHalfBath    3.931594"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the skew of all numerical features\n",
    "skewed_feats = df2[numeric_feats].apply(lambda x: skew(x.dropna())).sort_values(ascending=False)\n",
    "print(\"\\nSkew in numerical features: \\n\")\n",
    "skewness = pd.DataFrame({'Skew' :skewed_feats})\n",
    "skewness.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 37 skewed numerical features to Box Cox transform\n"
     ]
    }
   ],
   "source": [
    "skewness = skewness[abs(skewness) > 0.75]\n",
    "print(\"There are {} skewed numerical features to Box Cox transform\".format(skewness.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.special import boxcox1p\n",
    "skewed_features = skewness.index\n",
    "lam = 0.15\n",
    "for feat in skewed_features:\n",
    "    df2[feat] += 1\n",
    "    df2[feat] = boxcox1p(df2[feat], lam)\n",
    "    \n",
    "    \n",
    "# Running from checkpoint 1 again\n",
    "\n",
    "## marginal improvement\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Normalizing the sale price variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train_final_log = np.log(y_train_final)\n",
    "\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "y_train_final_log.hist(bins = 20, figsize = (5,4))\n",
    "plt.show()\n",
    "\n",
    "# Going to checkpoint 1\n",
    "# No improvement "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacked regression technique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hola stacked regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Datasets \n",
    "X_train_final\n",
    "\n",
    "X_test_final\n",
    "\n",
    "y_train_final_log\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing test predictions just in case\n",
    "\n",
    "#y_test = pd.read_csv(\"full-score.csv\")\n",
    "#y_test.head()\n",
    "#X_train_final.shape\n",
    "\n",
    "X_train_final.to_csv(\"xtrain.csv\", header = True)\n",
    "X_test.to_csv(\"xtest1.csv\")\n",
    "y_train_final.to_csv(\"ytrain.csv\", header = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 273)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape\n",
    "# Basic aggregator function: Defining 6 basic models, whose predictions are evvaluated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC\n",
    "from sklearn.ensemble import RandomForestRegressor,  GradientBoostingRegressor\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
    "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Validation function\n",
    "n_folds = 5\n",
    "\n",
    "def rmsle_cv(model):\n",
    "    kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(X_train_final.values)\n",
    "    rmse= np.sqrt(-cross_val_score(model, X_train_final.values, y_train_final_log, scoring=\"neg_mean_squared_error\", cv = kf))\n",
    "    return(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso = make_pipeline(RobustScaler(), Lasso(alpha =0.0005, random_state=1))\n",
    "ENet = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3))\n",
    "KRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)\n",
    "GBoost = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,\n",
    "                                   max_depth=4, max_features='sqrt',\n",
    "                                   min_samples_leaf=15, min_samples_split=10, \n",
    "                                   loss='huber', random_state =5)\n",
    "model_xgb = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468, \n",
    "                             learning_rate=0.05, max_depth=3, \n",
    "                             min_child_weight=1.7817, n_estimators=2200,\n",
    "                             reg_alpha=0.4640, reg_lambda=0.8571,\n",
    "                             subsample=0.5213, silent=1,\n",
    "                             random_state =7, nthread = -1)\n",
    "model_lgb = lgb.LGBMRegressor(objective='regression',num_leaves=5,\n",
    "                              learning_rate=0.05, n_estimators=720,\n",
    "                              max_bin = 55, bagging_fraction = 0.8,\n",
    "                              bagging_freq = 5, feature_fraction = 0.2319,\n",
    "                              feature_fraction_seed=9, bagging_seed=9,\n",
    "                              min_data_in_leaf =6, min_sum_hessian_in_leaf = 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Lasso score: 0.1275 (0.0137)\n",
      "\n",
      "ElasticNet score: 0.1277 (0.0137)\n",
      "\n",
      "Kernel Ridge score: 0.1279 (0.0148)\n",
      "\n",
      "Gradient Boosting: 0.1242 (0.0136)\n",
      "\n",
      "Gradient Boosting: 0.1259 (0.0101)\n",
      "\n",
      "Gradient Boosting: 0.1243 (0.0091)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Base model scores\n",
    "score = rmsle_cv(lasso)\n",
    "print(\"\\nLasso score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\n",
    "\n",
    "score = rmsle_cv(ENet)\n",
    "print(\"ElasticNet score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\n",
    "\n",
    "score = rmsle_cv(KRR)\n",
    "print(\"Kernel Ridge score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\n",
    "\n",
    "score = rmsle_cv(GBoost)\n",
    "print(\"Gradient Boosting: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\n",
    "\n",
    "score = rmsle_cv(model_xgb)\n",
    "print(\"XG Boosting: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\n",
    "\n",
    "score = rmsle_cv(model_lgb)\n",
    "print(\"Light G Boosting M: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Averaging the models\n",
    "\n",
    "class AveragingModels(BaseEstimator, RegressorMixin, TransformerMixin):\n",
    "    def __init__(self, models):\n",
    "        self.models = models\n",
    "        \n",
    "    # we define clones of the original models to fit the data in\n",
    "    def fit(self, X, y):\n",
    "        self.models_ = [clone(x) for x in self.models]\n",
    "        \n",
    "        # Train cloned base models\n",
    "        for model in self.models_:\n",
    "            model.fit(X, y)\n",
    "\n",
    "        return self\n",
    "    \n",
    "    #Now we do the predictions for cloned models and average them\n",
    "    def predict(self, X):\n",
    "        predictions = np.column_stack([\n",
    "            model.predict(X) for model in self.models_\n",
    "        ])\n",
    "        return np.mean(predictions, axis=1)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Averaged base models score: 0.1200 (0.0113)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "averaged_models = AveragingModels(models = (ENet, GBoost, model_lgb, model_xgb))\n",
    "\n",
    "score = rmsle_cv(averaged_models)\n",
    "print(\" Averaged base models score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
   ]
  }
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