House Price Prediction .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6cd7d1bd",
   "metadata": {},
   "source": [
    "# House Price Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9a144737",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Library used for modelling and eda\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')\n",
    "from matplotlib import rcParams\n",
    "rcParams['figure.figsize'] = 20,12\n",
    "\n",
    "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import RobustScaler, StandardScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from scipy.stats import skew\n",
    "from sklearn.decomposition import PCA, KernelPCA\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV, KFold\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor\n",
    "from sklearn.svm import SVR, LinearSVR\n",
    "from sklearn.linear_model import ElasticNet, SGDRegressor, BayesianRidge\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "path = 'C:/Users/shubh/Desktop/Python And Finance/house-prices-advanced-regression-techniques' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "94a8ddb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(f\"{path}/train.csv\")\n",
    "test =  pd.read_csv(f\"{path}/test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50767bd9",
   "metadata": {},
   "source": [
    "## Exploratory Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d77872dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='YearBuilt', ylabel='SalePrice'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(train.YearBuilt, train.SalePrice)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7caaf763",
   "metadata": {},
   "source": [
    "We can notice that the houses built later on are more expensive. Might be useful when encoding time features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2b21e6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 800000.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=train.GrLivArea, y=train.SalePrice)\n",
    "plt.xlabel(\"GrLivArea\", fontsize=13)\n",
    "plt.ylabel(\"SalePrice\", fontsize=13)\n",
    "plt.ylim(0,800000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "373681c5",
   "metadata": {},
   "source": [
    "We can see there are outliers in our dataset. Lets omit these outliers from our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "685545d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(train[(train[\"GrLivArea\"]>4000)&(train[\"SalePrice\"]<300000)].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "deee86a1",
   "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>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>65.0</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>208500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</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>181500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</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>223500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</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>140000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</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>250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2912</th>\n",
       "      <td>2915</td>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1936</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>6</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2913</th>\n",
       "      <td>2916</td>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1894</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>4</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2914</th>\n",
       "      <td>2917</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20000</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>9</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>2918</td>\n",
       "      <td>85</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441</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>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>2919</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627</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>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2917 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0        1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1        2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2        3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3        4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4        5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "2912  2915         160       RM         21.0     1936   Pave   NaN      Reg   \n",
       "2913  2916         160       RM         21.0     1894   Pave   NaN      Reg   \n",
       "2914  2917          20       RL        160.0    20000   Pave   NaN      Reg   \n",
       "2915  2918          85       RL         62.0    10441   Pave   NaN      Reg   \n",
       "2916  2919          60       RL         74.0     9627   Pave   NaN      Reg   \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",
       "2912         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "2913         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "2914         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "2915         Lvl    AllPub  ...        0    NaN  MnPrv        Shed     700   \n",
       "2916         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "\n",
       "     MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0         2   2008        WD         Normal   208500.0  \n",
       "1         5   2007        WD         Normal   181500.0  \n",
       "2         9   2008        WD         Normal   223500.0  \n",
       "3         2   2006        WD        Abnorml   140000.0  \n",
       "4        12   2008        WD         Normal   250000.0  \n",
       "...     ...    ...       ...            ...        ...  \n",
       "2912      6   2006        WD         Normal        NaN  \n",
       "2913      4   2006        WD        Abnorml        NaN  \n",
       "2914      9   2006        WD        Abnorml        NaN  \n",
       "2915      7   2006        WD         Normal        NaN  \n",
       "2916     11   2006        WD         Normal        NaN  \n",
       "\n",
       "[2917 rows x 81 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full=pd.concat([train,test], ignore_index=True)\n",
    "full "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e9dd3eb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2917, 80)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full.drop(['Id'],axis=1, inplace=True)  #drop id from our dataset\n",
    "full.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd66b5ce",
   "metadata": {},
   "source": [
    "## Data Cleaning and Prepocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3068773f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PoolQC          2908\n",
       "MiscFeature     2812\n",
       "Alley           2719\n",
       "Fence           2346\n",
       "SalePrice       1459\n",
       "FireplaceQu     1420\n",
       "LotFrontage      486\n",
       "GarageCond       159\n",
       "GarageYrBlt      159\n",
       "GarageFinish     159\n",
       "GarageQual       159\n",
       "GarageType       157\n",
       "BsmtExposure      82\n",
       "BsmtCond          82\n",
       "BsmtQual          81\n",
       "BsmtFinType2      80\n",
       "BsmtFinType1      79\n",
       "MasVnrType        24\n",
       "MasVnrArea        23\n",
       "MSZoning           4\n",
       "Functional         2\n",
       "BsmtHalfBath       2\n",
       "BsmtFullBath       2\n",
       "Utilities          2\n",
       "KitchenQual        1\n",
       "TotalBsmtSF        1\n",
       "BsmtUnfSF          1\n",
       "GarageCars         1\n",
       "GarageArea         1\n",
       "BsmtFinSF2         1\n",
       "BsmtFinSF1         1\n",
       "Exterior2nd        1\n",
       "Exterior1st        1\n",
       "SaleType           1\n",
       "Electrical         1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa = full.isnull().sum()\n",
    "aa[aa>0].sort_values(ascending=False)   #sort missing columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb2c5ad7",
   "metadata": {},
   "source": [
    "Let's first imput the missing values of LotFrontage based on the median of LotArea and Neighborhood. Since LotArea is a continuous feature, We use qcut to divide it into 10 parts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b71da762",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">LotFrontage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Blmngtn</th>\n",
       "      <td>46.900000</td>\n",
       "      <td>43.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blueste</th>\n",
       "      <td>27.300000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BrDale</th>\n",
       "      <td>21.500000</td>\n",
       "      <td>21.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BrkSide</th>\n",
       "      <td>55.789474</td>\n",
       "      <td>51.0</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ClearCr</th>\n",
       "      <td>88.150000</td>\n",
       "      <td>80.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CollgCr</th>\n",
       "      <td>71.336364</td>\n",
       "      <td>70.0</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crawfor</th>\n",
       "      <td>69.951807</td>\n",
       "      <td>70.0</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edwards</th>\n",
       "      <td>65.153409</td>\n",
       "      <td>64.5</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gilbert</th>\n",
       "      <td>74.207207</td>\n",
       "      <td>64.0</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDOTRR</th>\n",
       "      <td>62.241379</td>\n",
       "      <td>60.0</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MeadowV</th>\n",
       "      <td>25.606061</td>\n",
       "      <td>21.0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mitchel</th>\n",
       "      <td>75.144444</td>\n",
       "      <td>74.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAmes</th>\n",
       "      <td>75.210667</td>\n",
       "      <td>73.0</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NPkVill</th>\n",
       "      <td>28.142857</td>\n",
       "      <td>24.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NWAmes</th>\n",
       "      <td>81.517647</td>\n",
       "      <td>80.0</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NoRidge</th>\n",
       "      <td>91.629630</td>\n",
       "      <td>89.0</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NridgHt</th>\n",
       "      <td>84.184049</td>\n",
       "      <td>92.0</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OldTown</th>\n",
       "      <td>61.777293</td>\n",
       "      <td>60.0</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SWISU</th>\n",
       "      <td>59.068182</td>\n",
       "      <td>60.0</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sawyer</th>\n",
       "      <td>74.551020</td>\n",
       "      <td>72.0</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SawyerW</th>\n",
       "      <td>70.669811</td>\n",
       "      <td>67.0</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Somerst</th>\n",
       "      <td>64.549383</td>\n",
       "      <td>72.5</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StoneBr</th>\n",
       "      <td>62.173913</td>\n",
       "      <td>60.0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timber</th>\n",
       "      <td>81.157895</td>\n",
       "      <td>82.0</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Veenker</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             LotFrontage             \n",
       "                    mean median count\n",
       "Neighborhood                         \n",
       "Blmngtn        46.900000   43.0    20\n",
       "Blueste        27.300000   24.0    10\n",
       "BrDale         21.500000   21.0    30\n",
       "BrkSide        55.789474   51.0    95\n",
       "ClearCr        88.150000   80.5    20\n",
       "CollgCr        71.336364   70.0   220\n",
       "Crawfor        69.951807   70.0    83\n",
       "Edwards        65.153409   64.5   176\n",
       "Gilbert        74.207207   64.0   111\n",
       "IDOTRR         62.241379   60.0    87\n",
       "MeadowV        25.606061   21.0    33\n",
       "Mitchel        75.144444   74.0    90\n",
       "NAmes          75.210667   73.0   375\n",
       "NPkVill        28.142857   24.0    21\n",
       "NWAmes         81.517647   80.0    85\n",
       "NoRidge        91.629630   89.0    54\n",
       "NridgHt        84.184049   92.0   163\n",
       "OldTown        61.777293   60.0   229\n",
       "SWISU          59.068182   60.0    44\n",
       "Sawyer         74.551020   72.0    98\n",
       "SawyerW        70.669811   67.0   106\n",
       "Somerst        64.549383   72.5   162\n",
       "StoneBr        62.173913   60.0    46\n",
       "Timber         81.157895   82.0    57\n",
       "Veenker        72.000000   80.0    16"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full.groupby(['Neighborhood'])[['LotFrontage']].agg(['mean','median','count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c78dfa01",
   "metadata": {},
   "outputs": [],
   "source": [
    "full[\"LotAreaCut\"] = pd.qcut(full.LotArea,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "38255da6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">LotFrontage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotAreaCut</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(1299.999, 4921.8]</th>\n",
       "      <td>35.741036</td>\n",
       "      <td>34.0</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(4921.8, 7007.2]</th>\n",
       "      <td>55.460674</td>\n",
       "      <td>52.0</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(7007.2, 7949.0]</th>\n",
       "      <td>62.959839</td>\n",
       "      <td>62.0</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(7949.0, 8740.4]</th>\n",
       "      <td>67.113725</td>\n",
       "      <td>65.0</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(8740.4, 9452.0]</th>\n",
       "      <td>69.959184</td>\n",
       "      <td>70.0</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(9452.0, 10148.8]</th>\n",
       "      <td>73.988235</td>\n",
       "      <td>75.0</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10148.8, 11000.0]</th>\n",
       "      <td>73.636364</td>\n",
       "      <td>75.0</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(11000.0, 12196.8]</th>\n",
       "      <td>83.371681</td>\n",
       "      <td>82.0</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(12196.8, 14285.8]</th>\n",
       "      <td>84.973684</td>\n",
       "      <td>85.0</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(14285.8, 215245.0]</th>\n",
       "      <td>92.846535</td>\n",
       "      <td>90.0</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    LotFrontage             \n",
       "                           mean median count\n",
       "LotAreaCut                                  \n",
       "(1299.999, 4921.8]    35.741036   34.0   251\n",
       "(4921.8, 7007.2]      55.460674   52.0   267\n",
       "(7007.2, 7949.0]      62.959839   62.0   249\n",
       "(7949.0, 8740.4]      67.113725   65.0   255\n",
       "(8740.4, 9452.0]      69.959184   70.0   245\n",
       "(9452.0, 10148.8]     73.988235   75.0   255\n",
       "(10148.8, 11000.0]    73.636364   75.0   253\n",
       "(11000.0, 12196.8]    83.371681   82.0   226\n",
       "(12196.8, 14285.8]    84.973684   85.0   228\n",
       "(14285.8, 215245.0]   92.846535   90.0   202"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full.groupby(['LotAreaCut'])[['LotFrontage']].agg(['mean','median','count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "241efe27",
   "metadata": {},
   "outputs": [],
   "source": [
    "full['LotFrontage']=full.groupby(['LotAreaCut','Neighborhood'])['LotFrontage'].transform(lambda x: x.fillna(x.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7317fc3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since some combinations of LotArea and Neighborhood are not available, so we just LotAreaCut alone.\n",
    "full['LotFrontage']=full.groupby(['LotAreaCut'])['LotFrontage'].transform(lambda x: x.fillna(x.median()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6192f08c",
   "metadata": {},
   "source": [
    "Then we filling in other missing values according to data_description."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "13865b16",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=[\"MasVnrArea\", \"BsmtUnfSF\", \"TotalBsmtSF\", \"GarageCars\", \"BsmtFinSF2\", \"BsmtFinSF1\", \"GarageArea\"]\n",
    "for col in cols:\n",
    "    full[col].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "435f85f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols1 = [\"PoolQC\" , \"MiscFeature\", \"Alley\", \"Fence\", \"FireplaceQu\", \"GarageQual\", \"GarageCond\", \"GarageFinish\", \"GarageYrBlt\", \"GarageType\", \"BsmtExposure\", \"BsmtCond\", \"BsmtQual\", \"BsmtFinType2\", \"BsmtFinType1\", \"MasVnrType\"]\n",
    "for col in cols1:\n",
    "    full[col].fillna(\"None\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c878625f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill in with mode\n",
    "cols2 = [\"MSZoning\", \"BsmtFullBath\", \"BsmtHalfBath\", \"Utilities\", \"Functional\", \"Electrical\", \"KitchenQual\", \"SaleType\",\"Exterior1st\", \"Exterior2nd\"]\n",
    "for col in cols2:\n",
    "    full[col].fillna(full[col].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c1ae6e22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalePrice    1459\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full.isnull().sum()[full.isnull().sum()>0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4494e72e",
   "metadata": {},
   "source": [
    "Now we only have missing value that needs to be predicted,"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e05f494",
   "metadata": {},
   "source": [
    "## Feature Engineering\n",
    "Convert some numerical features into categorical features. It's better to use LabelEncoder and get_dummies for these features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4c877ae0",
   "metadata": {},
   "outputs": [],
   "source": [
    "NumStr = [\"MSSubClass\",\"BsmtFullBath\",\"BsmtHalfBath\",\"HalfBath\",\"BedroomAbvGr\",\"KitchenAbvGr\",\"MoSold\",\"YrSold\",\"YearBuilt\",\"YearRemodAdd\",\"LowQualFinSF\",\"GarageYrBlt\"]\n",
    "for col in NumStr:\n",
    "    full[col]=full[col].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f7aa95dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">SalePrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>200779.080460</td>\n",
       "      <td>192000.0</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>138647.380952</td>\n",
       "      <td>146000.0</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>102300.000000</td>\n",
       "      <td>88500.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>129613.333333</td>\n",
       "      <td>128250.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>185224.811567</td>\n",
       "      <td>159250.0</td>\n",
       "      <td>536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>95829.724638</td>\n",
       "      <td>99900.0</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>156125.000000</td>\n",
       "      <td>142500.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>108591.666667</td>\n",
       "      <td>107500.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>143302.972222</td>\n",
       "      <td>132000.0</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>240403.542088</td>\n",
       "      <td>216000.0</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>166772.416667</td>\n",
       "      <td>156000.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>192437.500000</td>\n",
       "      <td>163500.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>169736.551724</td>\n",
       "      <td>166500.0</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>147810.000000</td>\n",
       "      <td>140750.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>133541.076923</td>\n",
       "      <td>135980.0</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                SalePrice                \n",
       "                     mean    median count\n",
       "MSSubClass                               \n",
       "120         200779.080460  192000.0    87\n",
       "150                   NaN       NaN     0\n",
       "160         138647.380952  146000.0    63\n",
       "180         102300.000000   88500.0    10\n",
       "190         129613.333333  128250.0    30\n",
       "20          185224.811567  159250.0   536\n",
       "30           95829.724638   99900.0    69\n",
       "40          156125.000000  142500.0     4\n",
       "45          108591.666667  107500.0    12\n",
       "50          143302.972222  132000.0   144\n",
       "60          240403.542088  216000.0   297\n",
       "70          166772.416667  156000.0    60\n",
       "75          192437.500000  163500.0    16\n",
       "80          169736.551724  166500.0    58\n",
       "85          147810.000000  140750.0    20\n",
       "90          133541.076923  135980.0    52"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full.groupby(['MSSubClass'])[['SalePrice']].agg(['mean','median','count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcccc9dc",
   "metadata": {},
   "source": [
    "So basically I'll do a mapping based on MS cloumns\n",
    "- '180' : 1\n",
    "- '30' : 2   '45' : 2\n",
    "- '190' : 3, '50' : 3, '90' : 3,\n",
    "- '85' : 4, '40' : 4, '160' : 4\n",
    "- '70' : 5, '20' : 5, '75' : 5, '80' : 5, '150' : 5\n",
    "- '120': 6, '60' : 6\n",
    "Below I also add a small \"o\" in front of the features so as to keep the original features to use get_dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "588725c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_values():\n",
    "    full[\"oMSSubClass\"] = full.MSSubClass.map({'180':1, \n",
    "                                        '30':2, '45':2, \n",
    "                                        '190':3, '50':3, '90':3, \n",
    "                                        '85':4, '40':4, '160':4, \n",
    "                                        '70':5, '20':5, '75':5, '80':5, '150':5,\n",
    "                                        '120': 6, '60':6})\n",
    "    \n",
    "    full[\"oMSZoning\"] = full.MSZoning.map({'C (all)':1, 'RH':2, 'RM':2, 'RL':3, 'FV':4})\n",
    "    \n",
    "    full[\"oNeighborhood\"] = full.Neighborhood.map({'MeadowV':1,\n",
    "                                               'IDOTRR':2, 'BrDale':2,\n",
    "                                               'OldTown':3, 'Edwards':3, 'BrkSide':3,\n",
    "                                               'Sawyer':4, 'Blueste':4, 'SWISU':4, 'NAmes':4,\n",
    "                                               'NPkVill':5, 'Mitchel':5,\n",
    "                                               'SawyerW':6, 'Gilbert':6, 'NWAmes':6,\n",
    "                                               'Blmngtn':7, 'CollgCr':7, 'ClearCr':7, 'Crawfor':7,\n",
    "                                               'Veenker':8, 'Somerst':8, 'Timber':8,\n",
    "                                               'StoneBr':9,\n",
    "                                               'NoRidge':10, 'NridgHt':10})\n",
    "    \n",
    "    full[\"oCondition1\"] = full.Condition1.map({'Artery':1,\n",
    "                                           'Feedr':2, 'RRAe':2,\n",
    "                                           'Norm':3, 'RRAn':3,\n",
    "                                           'PosN':4, 'RRNe':4,\n",
    "                                           'PosA':5 ,'RRNn':5})\n",
    "    \n",
    "    full[\"oBldgType\"] = full.BldgType.map({'2fmCon':1, 'Duplex':1, 'Twnhs':1, '1Fam':2, 'TwnhsE':2})\n",
    "    \n",
    "    full[\"oHouseStyle\"] = full.HouseStyle.map({'1.5Unf':1, \n",
    "                                           '1.5Fin':2, '2.5Unf':2, 'SFoyer':2, \n",
    "                                           '1Story':3, 'SLvl':3,\n",
    "                                           '2Story':4, '2.5Fin':4})\n",
    "    \n",
    "    full[\"oExterior1st\"] = full.Exterior1st.map({'BrkComm':1,\n",
    "                                             'AsphShn':2, 'CBlock':2, 'AsbShng':2,\n",
    "                                             'WdShing':3, 'Wd Sdng':3, 'MetalSd':3, 'Stucco':3, 'HdBoard':3,\n",
    "                                             'BrkFace':4, 'Plywood':4,\n",
    "                                             'VinylSd':5,\n",
    "                                             'CemntBd':6,\n",
    "                                             'Stone':7, 'ImStucc':7})\n",
    "    \n",
    "    full[\"oMasVnrType\"] = full.MasVnrType.map({'BrkCmn':1, 'None':1, 'BrkFace':2, 'Stone':3})\n",
    "    \n",
    "    full[\"oExterQual\"] = full.ExterQual.map({'Fa':1, 'TA':2, 'Gd':3, 'Ex':4})\n",
    "    \n",
    "    full[\"oFoundation\"] = full.Foundation.map({'Slab':1, \n",
    "                                           'BrkTil':2, 'CBlock':2, 'Stone':2,\n",
    "                                           'Wood':3, 'PConc':4})\n",
    "    \n",
    "    full[\"oBsmtQual\"] = full.BsmtQual.map({'Fa':2, 'None':1, 'TA':3, 'Gd':4, 'Ex':5})\n",
    "    \n",
    "    full[\"oBsmtExposure\"] = full.BsmtExposure.map({'None':1, 'No':2, 'Av':3, 'Mn':3, 'Gd':4})\n",
    "    \n",
    "    full[\"oHeating\"] = full.Heating.map({'Floor':1, 'Grav':1, 'Wall':2, 'OthW':3, 'GasW':4, 'GasA':5})\n",
    "    \n",
    "    full[\"oHeatingQC\"] = full.HeatingQC.map({'Po':1, 'Fa':2, 'TA':3, 'Gd':4, 'Ex':5})\n",
    "    \n",
    "    full[\"oKitchenQual\"] = full.KitchenQual.map({'Fa':1, 'TA':2, 'Gd':3, 'Ex':4})\n",
    "    \n",
    "    full[\"oFunctional\"] = full.Functional.map({'Maj2':1, 'Maj1':2, 'Min1':2, 'Min2':2, 'Mod':2, 'Sev':2, 'Typ':3})\n",
    "    \n",
    "    full[\"oFireplaceQu\"] = full.FireplaceQu.map({'None':1, 'Po':1, 'Fa':2, 'TA':3, 'Gd':4, 'Ex':5})\n",
    "    \n",
    "    full[\"oGarageType\"] = full.GarageType.map({'CarPort':1, 'None':1,\n",
    "                                           'Detchd':2,\n",
    "                                           '2Types':3, 'Basment':3,\n",
    "                                           'Attchd':4, 'BuiltIn':5})\n",
    "    \n",
    "    full[\"oGarageFinish\"] = full.GarageFinish.map({'None':1, 'Unf':2, 'RFn':3, 'Fin':4})\n",
    "    \n",
    "    full[\"oPavedDrive\"] = full.PavedDrive.map({'N':1, 'P':2, 'Y':3})\n",
    "    \n",
    "    full[\"oSaleType\"] = full.SaleType.map({'COD':1, 'ConLD':1, 'ConLI':1, 'ConLw':1, 'Oth':1, 'WD':1,\n",
    "                                       'CWD':2, 'Con':3, 'New':3})\n",
    "    \n",
    "    full[\"oSaleCondition\"] = full.SaleCondition.map({'AdjLand':1, 'Abnorml':2, 'Alloca':2, 'Family':2, 'Normal':3, 'Partial':4})            \n",
    "                \n",
    "                        \n",
    "                        \n",
    "    \n",
    "    return \"Done!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5ba09417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Done!'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "22c62acf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop two unwanted columns\n",
    "full.drop(\"LotAreaCut\",axis=1,inplace=True)\n",
    "full.drop(['SalePrice'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2ef7d47",
   "metadata": {},
   "source": [
    "### Pipeline\n",
    "Next we can build a pipeline. It's convenient to experiment different feature combinations once you've got a pipeline.\n",
    "Label Encoding three \"Year\" features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d19847aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "class labelenc(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def fit(self,X,y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self,X):\n",
    "        lab=LabelEncoder()\n",
    "        X[\"YearBuilt\"] = lab.fit_transform(X[\"YearBuilt\"])\n",
    "        X[\"YearRemodAdd\"] = lab.fit_transform(X[\"YearRemodAdd\"])\n",
    "        X[\"GarageYrBlt\"] = lab.fit_transform(X[\"GarageYrBlt\"])\n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b84e6be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Apply log1p to the skewed features, then get_dummies. \n",
    "\n",
    "class skew_dummies(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self,skew=0.5):\n",
    "        self.skew = skew\n",
    "    \n",
    "    def fit(self,X,y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self,X):\n",
    "        X_numeric=X.select_dtypes(exclude=[\"object\"])\n",
    "        skewness = X_numeric.apply(lambda x: skew(x))\n",
    "        skewness_features = skewness[abs(skewness) >= self.skew].index\n",
    "        X[skewness_features] = np.log1p(X[skewness_features])\n",
    "        X = pd.get_dummies(X)\n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c1fad0fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# build pipeline\n",
    "pipe = Pipeline([\n",
    "    ('labenc', labelenc()),\n",
    "    ('skew_dummies', skew_dummies(skew=1)),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "16200340",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the original data for later use\n",
    "full2 = full.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3bc11bc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pipe = pipe.fit_transform(full2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "28d551b4",
   "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>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>BsmtUnfSF</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>65.0</td>\n",
       "      <td>9.042040</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>110</td>\n",
       "      <td>53</td>\n",
       "      <td>5.283204</td>\n",
       "      <td>706.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <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>80.0</td>\n",
       "      <td>9.169623</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>83</td>\n",
       "      <td>26</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>978.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>284.0</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>68.0</td>\n",
       "      <td>9.328212</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>108</td>\n",
       "      <td>52</td>\n",
       "      <td>5.093750</td>\n",
       "      <td>486.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>434.0</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>60.0</td>\n",
       "      <td>9.164401</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>540.0</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>84.0</td>\n",
       "      <td>9.565284</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>107</td>\n",
       "      <td>50</td>\n",
       "      <td>5.860786</td>\n",
       "      <td>655.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>490.0</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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2912</th>\n",
       "      <td>21.0</td>\n",
       "      <td>7.568896</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>77</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>546.0</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>2913</th>\n",
       "      <td>21.0</td>\n",
       "      <td>7.546974</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>77</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>252.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>294.0</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>2914</th>\n",
       "      <td>160.0</td>\n",
       "      <td>9.903538</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>67</td>\n",
       "      <td>46</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>2915</th>\n",
       "      <td>62.0</td>\n",
       "      <td>9.253591</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>99</td>\n",
       "      <td>42</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>337.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>575.0</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>2916</th>\n",
       "      <td>74.0</td>\n",
       "      <td>9.172431</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>44</td>\n",
       "      <td>4.553877</td>\n",
       "      <td>758.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>238.0</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>2917 rows × 405 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      LotFrontage   LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0            65.0  9.042040            7            5        110   \n",
       "1            80.0  9.169623            6            8         83   \n",
       "2            68.0  9.328212            7            5        108   \n",
       "3            60.0  9.164401            7            5         25   \n",
       "4            84.0  9.565284            8            5        107   \n",
       "...           ...       ...          ...          ...        ...   \n",
       "2912         21.0  7.568896            4            7         77   \n",
       "2913         21.0  7.546974            4            5         77   \n",
       "2914        160.0  9.903538            5            7         67   \n",
       "2915         62.0  9.253591            5            5         99   \n",
       "2916         74.0  9.172431            7            5        100   \n",
       "\n",
       "      YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  BsmtUnfSF  ...  \\\n",
       "0               53    5.283204       706.0         0.0      150.0  ...   \n",
       "1               26    0.000000       978.0         0.0      284.0  ...   \n",
       "2               52    5.093750       486.0         0.0      434.0  ...   \n",
       "3               20    0.000000       216.0         0.0      540.0  ...   \n",
       "4               50    5.860786       655.0         0.0      490.0  ...   \n",
       "...            ...         ...         ...         ...        ...  ...   \n",
       "2912            20    0.000000         0.0         0.0      546.0  ...   \n",
       "2913            20    0.000000       252.0         0.0      294.0  ...   \n",
       "2914            46    0.000000      1224.0         0.0        0.0  ...   \n",
       "2915            42    0.000000       337.0         0.0      575.0  ...   \n",
       "2916            44    4.553877       758.0         0.0      238.0  ...   \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",
       "2912               0             0             0            1   \n",
       "2913               0             0             0            1   \n",
       "2914               0             0             0            1   \n",
       "2915               0             0             0            1   \n",
       "2916               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",
       "2912                      0                      0                     0   \n",
       "2913                      1                      0                     0   \n",
       "2914                      1                      0                     0   \n",
       "2915                      0                      0                     0   \n",
       "2916                      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",
       "2912                     0                     1                      0  \n",
       "2913                     0                     0                      0  \n",
       "2914                     0                     0                      0  \n",
       "2915                     0                     1                      0  \n",
       "2916                     0                     1                      0  \n",
       "\n",
       "[2917 rows x 405 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ed254a87",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = RobustScaler() # try to deal with  outliers using robust scalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "13caa9c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_train=train.shape[0]\n",
    "\n",
    "X = data_pipe[:n_train]\n",
    "test_X = data_pipe[n_train:]\n",
    "y= train.SalePrice\n",
    "\n",
    "X_scaled = scaler.fit(X).transform(X)\n",
    "y_log = np.log(train.SalePrice)\n",
    "test_X_scaled = scaler.transform(test_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3478ca8c",
   "metadata": {},
   "source": [
    "### Feature Selection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "60b15914",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso(alpha=0.001)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=0.001)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Lasso(alpha=0.001)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso=Lasso(alpha=0.001)\n",
    "lasso.fit(X_scaled,y_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "284730a7",
   "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>Feature Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GrLivArea</th>\n",
       "      <td>0.108860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>0.102520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <td>0.074243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>0.068834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1stFlrSF</th>\n",
       "      <td>0.059812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1_Unf</th>\n",
       "      <td>-0.016686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <td>-0.018823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenAbvGr_2</th>\n",
       "      <td>-0.027021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CentralAir_N</th>\n",
       "      <td>-0.034036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning_C (all)</th>\n",
       "      <td>-0.096852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>405 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Feature Importance\n",
       "GrLivArea                        0.108860\n",
       "OverallQual                      0.102520\n",
       "2ndFlrSF                         0.074243\n",
       "YearBuilt                        0.068834\n",
       "1stFlrSF                         0.059812\n",
       "...                                   ...\n",
       "BsmtFinType1_Unf                -0.016686\n",
       "SaleCondition_Abnorml           -0.018823\n",
       "KitchenAbvGr_2                  -0.027021\n",
       "CentralAir_N                    -0.034036\n",
       "MSZoning_C (all)                -0.096852\n",
       "\n",
       "[405 rows x 1 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FI_lasso = pd.DataFrame({\"Feature Importance\":lasso.coef_}, index=data_pipe.columns)\n",
    "FI_lasso.sort_values(\"Feature Importance\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "57a897d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FI_lasso[FI_lasso[\"Feature Importance\"]!=0].sort_values(\"Feature Importance\").plot(kind=\"barh\",figsize=(15,25))\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f42ea214",
   "metadata": {},
   "source": [
    "Based on the plot above we should try to merge features to create more informative features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3503861e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class add_feature(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self,additional=1):\n",
    "        self.additional = additional\n",
    "    \n",
    "    def fit(self,X,y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self,X):\n",
    "        if self.additional==1:\n",
    "            X[\"TotalHouse\"] = X[\"TotalBsmtSF\"] + X[\"1stFlrSF\"] + X[\"2ndFlrSF\"]   \n",
    "            X[\"TotalArea\"] = X[\"TotalBsmtSF\"] + X[\"1stFlrSF\"] + X[\"2ndFlrSF\"] + X[\"GarageArea\"]\n",
    "            \n",
    "        else:\n",
    "            X[\"TotalHouse\"] = X[\"TotalBsmtSF\"] + X[\"1stFlrSF\"] + X[\"2ndFlrSF\"]   \n",
    "            X[\"TotalArea\"] = X[\"TotalBsmtSF\"] + X[\"1stFlrSF\"] + X[\"2ndFlrSF\"] + X[\"GarageArea\"]\n",
    "            \n",
    "            X[\"+_TotalHouse_OverallQual\"] = X[\"TotalHouse\"] * X[\"OverallQual\"]\n",
    "            X[\"+_GrLivArea_OverallQual\"] = X[\"GrLivArea\"] * X[\"OverallQual\"]\n",
    "            X[\"+_oMSZoning_TotalHouse\"] = X[\"oMSZoning\"] * X[\"TotalHouse\"]\n",
    "            X[\"+_oMSZoning_OverallQual\"] = X[\"oMSZoning\"] + X[\"OverallQual\"]\n",
    "            X[\"+_oMSZoning_YearBuilt\"] = X[\"oMSZoning\"] + X[\"YearBuilt\"]\n",
    "            X[\"+_oNeighborhood_TotalHouse\"] = X[\"oNeighborhood\"] * X[\"TotalHouse\"]\n",
    "            X[\"+_oNeighborhood_OverallQual\"] = X[\"oNeighborhood\"] + X[\"OverallQual\"]\n",
    "            X[\"+_oNeighborhood_YearBuilt\"] = X[\"oNeighborhood\"] + X[\"YearBuilt\"]\n",
    "            X[\"+_BsmtFinSF1_OverallQual\"] = X[\"BsmtFinSF1\"] * X[\"OverallQual\"]\n",
    "            \n",
    "            X[\"-_oFunctional_TotalHouse\"] = X[\"oFunctional\"] * X[\"TotalHouse\"]\n",
    "            X[\"-_oFunctional_OverallQual\"] = X[\"oFunctional\"] + X[\"OverallQual\"]\n",
    "            X[\"-_LotArea_OverallQual\"] = X[\"LotArea\"] * X[\"OverallQual\"]\n",
    "            X[\"-_TotalHouse_LotArea\"] = X[\"TotalHouse\"] + X[\"LotArea\"]\n",
    "            X[\"-_oCondition1_TotalHouse\"] = X[\"oCondition1\"] * X[\"TotalHouse\"]\n",
    "            X[\"-_oCondition1_OverallQual\"] = X[\"oCondition1\"] + X[\"OverallQual\"]\n",
    "            \n",
    "           \n",
    "            X[\"Bsmt\"] = X[\"BsmtFinSF1\"] + X[\"BsmtFinSF2\"] + X[\"BsmtUnfSF\"]\n",
    "            X[\"Rooms\"] = X[\"FullBath\"]+X[\"TotRmsAbvGrd\"]\n",
    "            X[\"PorchArea\"] = X[\"OpenPorchSF\"]+X[\"EnclosedPorch\"]+X[\"3SsnPorch\"]+X[\"ScreenPorch\"]\n",
    "            X[\"TotalPlace\"] = X[\"TotalBsmtSF\"] + X[\"1stFlrSF\"] + X[\"2ndFlrSF\"] + X[\"GarageArea\"] + X[\"OpenPorchSF\"]+X[\"EnclosedPorch\"]+X[\"3SsnPorch\"]+X[\"ScreenPorch\"]\n",
    "\n",
    "    \n",
    "            return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "138a97a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline([\n",
    "    ('labenc', labelenc()),\n",
    "    ('add_feature', add_feature(additional=2)),\n",
    "    ('skew_dummies', skew_dummies(skew=1)),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "89521aaa",
   "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>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>BsmtUnfSF</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>65.0</td>\n",
       "      <td>9.042040</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>110</td>\n",
       "      <td>53</td>\n",
       "      <td>5.283204</td>\n",
       "      <td>706.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</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>80.0</td>\n",
       "      <td>9.169623</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>83</td>\n",
       "      <td>26</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>978.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>284.0</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>68.0</td>\n",
       "      <td>9.328212</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>108</td>\n",
       "      <td>52</td>\n",
       "      <td>5.093750</td>\n",
       "      <td>486.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>434.0</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>60.0</td>\n",
       "      <td>9.164401</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>540.0</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>84.0</td>\n",
       "      <td>9.565284</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>107</td>\n",
       "      <td>50</td>\n",
       "      <td>5.860786</td>\n",
       "      <td>655.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>490.0</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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2912</th>\n",
       "      <td>21.0</td>\n",
       "      <td>7.568896</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>77</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>546.0</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>2913</th>\n",
       "      <td>21.0</td>\n",
       "      <td>7.546974</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>77</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>252.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>294.0</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>2914</th>\n",
       "      <td>160.0</td>\n",
       "      <td>9.903538</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>67</td>\n",
       "      <td>46</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>2915</th>\n",
       "      <td>62.0</td>\n",
       "      <td>9.253591</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>99</td>\n",
       "      <td>42</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>337.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>575.0</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>2916</th>\n",
       "      <td>74.0</td>\n",
       "      <td>9.172431</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>44</td>\n",
       "      <td>4.553877</td>\n",
       "      <td>758.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>238.0</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>2917 rows × 426 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      LotFrontage   LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0            65.0  9.042040            7            5        110   \n",
       "1            80.0  9.169623            6            8         83   \n",
       "2            68.0  9.328212            7            5        108   \n",
       "3            60.0  9.164401            7            5         25   \n",
       "4            84.0  9.565284            8            5        107   \n",
       "...           ...       ...          ...          ...        ...   \n",
       "2912         21.0  7.568896            4            7         77   \n",
       "2913         21.0  7.546974            4            5         77   \n",
       "2914        160.0  9.903538            5            7         67   \n",
       "2915         62.0  9.253591            5            5         99   \n",
       "2916         74.0  9.172431            7            5        100   \n",
       "\n",
       "      YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  BsmtUnfSF  ...  \\\n",
       "0               53    5.283204       706.0         0.0      150.0  ...   \n",
       "1               26    0.000000       978.0         0.0      284.0  ...   \n",
       "2               52    5.093750       486.0         0.0      434.0  ...   \n",
       "3               20    0.000000       216.0         0.0      540.0  ...   \n",
       "4               50    5.860786       655.0         0.0      490.0  ...   \n",
       "...            ...         ...         ...         ...        ...  ...   \n",
       "2912            20    0.000000         0.0         0.0      546.0  ...   \n",
       "2913            20    0.000000       252.0         0.0      294.0  ...   \n",
       "2914            46    0.000000      1224.0         0.0        0.0  ...   \n",
       "2915            42    0.000000       337.0         0.0      575.0  ...   \n",
       "2916            44    4.553877       758.0         0.0      238.0  ...   \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",
       "2912               0             0             0            1   \n",
       "2913               0             0             0            1   \n",
       "2914               0             0             0            1   \n",
       "2915               0             0             0            1   \n",
       "2916               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",
       "2912                      0                      0                     0   \n",
       "2913                      1                      0                     0   \n",
       "2914                      1                      0                     0   \n",
       "2915                      0                      0                     0   \n",
       "2916                      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",
       "2912                     0                     1                      0  \n",
       "2913                     0                     0                      0  \n",
       "2914                     0                     0                      0  \n",
       "2915                     0                     1                      0  \n",
       "2916                     0                     1                      0  \n",
       "\n",
       "[2917 rows x 426 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_pipe = pipe.fit_transform(full)\n",
    "full_pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4113a8a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_train=train.shape[0]\n",
    "X = full_pipe[:n_train]\n",
    "test_X = full_pipe[n_train:]\n",
    "y= train.SalePrice\n",
    "\n",
    "X_scaled = scaler.fit(X).transform(X)\n",
    "y_log = np.log(train.SalePrice)\n",
    "test_X_scaled = scaler.transform(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ea8f069e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=410)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "704eb652",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1458, 410), (1459, 410))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled=pca.fit_transform(X_scaled)\n",
    "test_X_scaled = pca.transform(test_X_scaled)\n",
    "X_scaled.shape, test_X_scaled.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b82df5c",
   "metadata": {},
   "source": [
    "## Modeling & Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "72f5ecbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define cross validation strategy\n",
    "def rmse_cv(model,X,y):\n",
    "    rmse = np.sqrt(-cross_val_score(model, X, y, scoring=\"neg_mean_squared_error\", cv=5))\n",
    "    return rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1248fe24",
   "metadata": {},
   "source": [
    "Models include:\n",
    "\n",
    "- LinearRegression\n",
    "- Ridge\n",
    "- Lasso\n",
    "- Random Forrest\n",
    "- Gradient Boosting Tree\n",
    "- Support Vector Regression\n",
    "- Linear Support Vector Regression\n",
    "- ElasticNet\n",
    "- Stochastic Gradient Descent\n",
    "- BayesianRidge\n",
    "- KernelRidge\n",
    "- ExtraTreesRegressor\n",
    "- XgBoost\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "31c9b0e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [LinearRegression(),Ridge(),Lasso(alpha=0.01,max_iter=10000),RandomForestRegressor(),GradientBoostingRegressor(),SVR(),LinearSVR(),\n",
    "          ElasticNet(alpha=0.001,max_iter=10000),SGDRegressor(max_iter=1000,tol=1e-3),BayesianRidge(),KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5),\n",
    "          ExtraTreesRegressor(),XGBRegressor()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "03e9bc78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 581466512.715703, 163267785.0771\n",
      "Ridge: 0.117596, 0.0091\n",
      "Lasso: 0.121474, 0.0060\n",
      "RF: 0.134117, 0.0074\n",
      "GBR: 0.123835, 0.0070\n",
      "SVR: 0.136997, 0.0122\n",
      "LinSVR: 0.122622, 0.0091\n",
      "Ela: 0.111113, 0.0059\n",
      "SGD: 0.147340, 0.0153\n",
      "Bay: 0.110576, 0.0060\n",
      "Ker: 0.109276, 0.0055\n",
      "Extra: 0.129612, 0.0051\n",
      "Xgb: 0.138774, 0.0087\n"
     ]
    }
   ],
   "source": [
    "names = [\"LR\", \"Ridge\", \"Lasso\", \"RF\", \"GBR\", \"SVR\", \"LinSVR\", \"Ela\",\"SGD\",\"Bay\",\"Ker\",\"Extra\",\"Xgb\"]\n",
    "for name, model in zip(names, models):\n",
    "    score = rmse_cv(model, X_scaled, y_log)\n",
    "    print(\"{}: {:.6f}, {:.4f}\".format(name,score.mean(),score.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "794a71e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "class grid():\n",
    "    def __init__(self,model):\n",
    "        self.model = model\n",
    "    \n",
    "    def grid_get(self,X,y,param_grid):\n",
    "        grid_search = GridSearchCV(self.model,param_grid,cv=5, scoring=\"neg_mean_squared_error\")\n",
    "        grid_search.fit(X,y)\n",
    "        print(grid_search.best_params_, np.sqrt(-grid_search.best_score_))\n",
    "        grid_search.cv_results_['mean_test_score'] = np.sqrt(-grid_search.cv_results_['mean_test_score'])\n",
    "        print(pd.DataFrame(grid_search.cv_results_)[['params','mean_test_score','std_test_score']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ea47ba81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.0005, 'max_iter': 10000} 0.11129043546033596\n",
      "                                 params  mean_test_score  std_test_score\n",
      "0  {'alpha': 0.0004, 'max_iter': 10000}         0.111457        0.001392\n",
      "1  {'alpha': 0.0005, 'max_iter': 10000}         0.111290        0.001339\n",
      "2  {'alpha': 0.0007, 'max_iter': 10000}         0.111532        0.001284\n",
      "3  {'alpha': 0.0009, 'max_iter': 10000}         0.111910        0.001205\n"
     ]
    }
   ],
   "source": [
    "grid(Lasso()).grid_get(X_scaled,y_log,{'alpha': [0.0004,0.0005,0.0007,0.0009],'max_iter':[10000]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "36645b14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 60} 0.1101964539673491\n",
      "          params  mean_test_score  std_test_score\n",
      "0  {'alpha': 35}         0.110369        0.001268\n",
      "1  {'alpha': 40}         0.110300        0.001249\n",
      "2  {'alpha': 45}         0.110252        0.001234\n",
      "3  {'alpha': 50}         0.110221        0.001222\n",
      "4  {'alpha': 55}         0.110204        0.001213\n",
      "5  {'alpha': 60}         0.110196        0.001204\n",
      "6  {'alpha': 65}         0.110198        0.001198\n",
      "7  {'alpha': 70}         0.110207        0.001192\n",
      "8  {'alpha': 80}         0.110242        0.001184\n",
      "9  {'alpha': 90}         0.110295        0.001177\n"
     ]
    }
   ],
   "source": [
    "grid(Ridge()).grid_get(X_scaled,y_log,{'alpha':[35,40,45,50,55,60,65,70,80,90]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c1b1793a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 13, 'epsilon': 0.009, 'gamma': 0.0004, 'kernel': 'rbf'} 0.10822437796234355\n",
      "                                               params  mean_test_score  \\\n",
      "0   {'C': 11, 'epsilon': 0.008, 'gamma': 0.0003, '...         0.108653   \n",
      "1   {'C': 11, 'epsilon': 0.008, 'gamma': 0.0004, '...         0.108305   \n",
      "2   {'C': 11, 'epsilon': 0.009, 'gamma': 0.0003, '...         0.108642   \n",
      "3   {'C': 11, 'epsilon': 0.009, 'gamma': 0.0004, '...         0.108289   \n",
      "4   {'C': 13, 'epsilon': 0.008, 'gamma': 0.0003, '...         0.108554   \n",
      "5   {'C': 13, 'epsilon': 0.008, 'gamma': 0.0004, '...         0.108240   \n",
      "6   {'C': 13, 'epsilon': 0.009, 'gamma': 0.0003, '...         0.108548   \n",
      "7   {'C': 13, 'epsilon': 0.009, 'gamma': 0.0004, '...         0.108224   \n",
      "8   {'C': 15, 'epsilon': 0.008, 'gamma': 0.0003, '...         0.108467   \n",
      "9   {'C': 15, 'epsilon': 0.008, 'gamma': 0.0004, '...         0.108241   \n",
      "10  {'C': 15, 'epsilon': 0.009, 'gamma': 0.0003, '...         0.108436   \n",
      "11  {'C': 15, 'epsilon': 0.009, 'gamma': 0.0004, '...         0.108244   \n",
      "\n",
      "    std_test_score  \n",
      "0         0.001553  \n",
      "1         0.001608  \n",
      "2         0.001555  \n",
      "3         0.001607  \n",
      "4         0.001601  \n",
      "5         0.001615  \n",
      "6         0.001602  \n",
      "7         0.001621  \n",
      "8         0.001628  \n",
      "9         0.001608  \n",
      "10        0.001632  \n",
      "11        0.001617  \n"
     ]
    }
   ],
   "source": [
    "grid(SVR()).grid_get(X_scaled,y_log,{'C':[11,13,15],'kernel':[\"rbf\"],\"gamma\":[0.0003,0.0004],\"epsilon\":[0.008,0.009]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a2dc908a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.2, 'coef0': 0.8, 'degree': 3, 'kernel': 'polynomial'} 0.10826401034858316\n",
      "                                              params  mean_test_score  \\\n",
      "0  {'alpha': 0.2, 'coef0': 0.8, 'degree': 3, 'ker...         0.108264   \n",
      "1  {'alpha': 0.2, 'coef0': 1, 'degree': 3, 'kerne...         0.108503   \n",
      "2  {'alpha': 0.3, 'coef0': 0.8, 'degree': 3, 'ker...         0.108394   \n",
      "3  {'alpha': 0.3, 'coef0': 1, 'degree': 3, 'kerne...         0.108273   \n",
      "4  {'alpha': 0.4, 'coef0': 0.8, 'degree': 3, 'ker...         0.108756   \n",
      "5  {'alpha': 0.4, 'coef0': 1, 'degree': 3, 'kerne...         0.108294   \n",
      "\n",
      "   std_test_score  \n",
      "0        0.001209  \n",
      "1        0.001243  \n",
      "2        0.001188  \n",
      "3        0.001210  \n",
      "4        0.001181  \n",
      "5        0.001191  \n"
     ]
    }
   ],
   "source": [
    "param_grid={'alpha':[0.2,0.3,0.4], 'kernel':[\"polynomial\"], 'degree':[3],'coef0':[0.8,1]}\n",
    "grid(KernelRidge()).grid_get(X_scaled,y_log,param_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6fa9954e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.005, 'l1_ratio': 0.08, 'max_iter': 10000} 0.11116534330224477\n",
      "                                              params  mean_test_score  \\\n",
      "0  {'alpha': 0.0008, 'l1_ratio': 0.08, 'max_iter'...         0.114753   \n",
      "1  {'alpha': 0.0008, 'l1_ratio': 0.1, 'max_iter':...         0.114242   \n",
      "2  {'alpha': 0.0008, 'l1_ratio': 0.3, 'max_iter':...         0.112055   \n",
      "3  {'alpha': 0.004, 'l1_ratio': 0.08, 'max_iter':...         0.111272   \n",
      "4  {'alpha': 0.004, 'l1_ratio': 0.1, 'max_iter': ...         0.111203   \n",
      "5  {'alpha': 0.004, 'l1_ratio': 0.3, 'max_iter': ...         0.112478   \n",
      "6  {'alpha': 0.005, 'l1_ratio': 0.08, 'max_iter':...         0.111165   \n",
      "7  {'alpha': 0.005, 'l1_ratio': 0.1, 'max_iter': ...         0.111186   \n",
      "8  {'alpha': 0.005, 'l1_ratio': 0.3, 'max_iter': ...         0.112979   \n",
      "\n",
      "   std_test_score  \n",
      "0        0.001937  \n",
      "1        0.001904  \n",
      "2        0.001596  \n",
      "3        0.001382  \n",
      "4        0.001326  \n",
      "5        0.001159  \n",
      "6        0.001312  \n",
      "7        0.001277  \n",
      "8        0.001158  \n"
     ]
    }
   ],
   "source": [
    "grid(ElasticNet()).grid_get(X_scaled,y_log,{'alpha':[0.0008,0.004,0.005],'l1_ratio':[0.08,0.1,0.3],'max_iter':[10000]})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41b29f47",
   "metadata": {},
   "source": [
    "### Ensemble Methods\n",
    "Weight Average : Average base models according to their weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "358c8940",
   "metadata": {},
   "outputs": [],
   "source": [
    "class AverageWeight(BaseEstimator, RegressorMixin):\n",
    "    def __init__(self,mod,weight):\n",
    "        self.mod = mod\n",
    "        self.weight = weight\n",
    "        \n",
    "    def fit(self,X,y):\n",
    "        self.models_ = [clone(x) for x in self.mod]\n",
    "        for model in self.models_:\n",
    "            model.fit(X,y)\n",
    "        return self\n",
    "    \n",
    "    def predict(self,X):\n",
    "        w = list()\n",
    "        pred = np.array([model.predict(X) for model in self.models_])\n",
    "        # for every data point, single model prediction times weight, then add them together\n",
    "        for data in range(pred.shape[1]):\n",
    "            single = [pred[model,data]*weight for model,weight in zip(range(pred.shape[0]),self.weight)]\n",
    "            w.append(np.sum(single))\n",
    "        return w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b86f1dac",
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso = Lasso(alpha=0.0005,max_iter=10000)\n",
    "ridge = Ridge(alpha=60)\n",
    "svr = SVR(gamma= 0.0004,kernel='rbf',C=13,epsilon=0.009)\n",
    "ker = KernelRidge(alpha=0.2 ,kernel='polynomial',degree=3 , coef0=0.8)\n",
    "ela = ElasticNet(alpha=0.005,l1_ratio=0.08,max_iter=10000)\n",
    "bay = BayesianRidge()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "53600015",
   "metadata": {},
   "outputs": [],
   "source": [
    "# assign weights based on their gridsearch score\n",
    "w1 = 0.02\n",
    "w2 = 0.2\n",
    "w3 = 0.25\n",
    "w4 = 0.3\n",
    "w5 = 0.03\n",
    "w6 = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "67b2451e",
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_avg = AverageWeight(mod = [lasso,ridge,svr,ker,ela,bay],weight=[w1,w2,w3,w4,w5,w6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5e18a256",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1076845842299667\n"
     ]
    }
   ],
   "source": [
    "score = rmse_cv(weight_avg,X_scaled,y_log)\n",
    "print(score.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d06f44fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10668349587194799\n"
     ]
    }
   ],
   "source": [
    "# Averaging the two models \n",
    "weight_avg = AverageWeight(mod = [svr,ker],weight=[0.5,0.5])\n",
    "score = rmse_cv(weight_avg,X_scaled,y_log)\n",
    "print(score.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "528f3d26",
   "metadata": {},
   "source": [
    "### Stacking Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d5927f52",
   "metadata": {},
   "outputs": [],
   "source": [
    "class stacking(BaseEstimator, RegressorMixin, TransformerMixin):\n",
    "    def __init__(self,mod,meta_model):\n",
    "        self.mod = mod\n",
    "        self.meta_model = meta_model\n",
    "        self.kf = KFold(n_splits=5, random_state=42, shuffle=True)\n",
    "        \n",
    "    def fit(self,X,y):\n",
    "        self.saved_model = [list() for i in self.mod]\n",
    "        oof_train = np.zeros((X.shape[0], len(self.mod)))\n",
    "        \n",
    "        for i,model in enumerate(self.mod):\n",
    "            for train_index, val_index in self.kf.split(X,y):\n",
    "                renew_model = clone(model)\n",
    "                renew_model.fit(X[train_index], y[train_index])\n",
    "                self.saved_model[i].append(renew_model)\n",
    "                oof_train[val_index,i] = renew_model.predict(X[val_index])\n",
    "        \n",
    "        self.meta_model.fit(oof_train,y)\n",
    "        return self\n",
    "    \n",
    "    def predict(self,X):\n",
    "        whole_test = np.column_stack([np.column_stack(model.predict(X) for model in single_model).mean(axis=1) \n",
    "                                      for single_model in self.saved_model]) \n",
    "        return self.meta_model.predict(whole_test)\n",
    "    \n",
    "    def get_oof(self,X,y,test_X):\n",
    "        oof = np.zeros((X.shape[0],len(self.mod)))\n",
    "        test_single = np.zeros((test_X.shape[0],5))\n",
    "        test_mean = np.zeros((test_X.shape[0],len(self.mod)))\n",
    "        for i,model in enumerate(self.mod):\n",
    "            for j, (train_index,val_index) in enumerate(self.kf.split(X,y)):\n",
    "                clone_model = clone(model)\n",
    "                clone_model.fit(X[train_index],y[train_index])\n",
    "                oof[val_index,i] = clone_model.predict(X[val_index])\n",
    "                test_single[:,j] = clone_model.predict(test_X)\n",
    "            test_mean[:,i] = test_single.mean(axis=1)\n",
    "        return oof, test_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6cdb176a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# must do imputer first, otherwise stacking won't work, and i don't know why.\n",
    "a = SimpleImputer().fit_transform(X_scaled)\n",
    "b = SimpleImputer().fit_transform(y_log.values.reshape(-1,1)).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a2a7d719",
   "metadata": {},
   "outputs": [],
   "source": [
    "stack_model = stacking(mod=[lasso,ridge,svr,ker,ela,bay],meta_model=ker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8a02e54b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10657571808512126\n"
     ]
    }
   ],
   "source": [
    "score = rmse_cv(stack_model,a,b)\n",
    "print(score.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1a1ef7dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_stack, X_test_stack = stack_model.get_oof(a,b,test_X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "388eaa10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1458, 6), (1458, 410))"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_stack.shape, a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "654709f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_add = np.hstack((a,X_train_stack))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "1155fd51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1458, 416), (1459, 416))"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_add = np.hstack((test_X_scaled,X_test_stack))\n",
    "X_train_add.shape, X_test_add.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9b619a93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10182770654553966\n"
     ]
    }
   ],
   "source": [
    "score = rmse_cv(stack_model,X_train_add,b)\n",
    "print(score.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "bfd2b3ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "         mod=[Lasso(alpha=0.0005, max_iter=10000), Ridge(alpha=60),\n",
       "              SVR(C=13, epsilon=0.009, gamma=0.0004),\n",
       "              KernelRidge(alpha=0.2, coef0=0.8, kernel=&#x27;polynomial&#x27;),\n",
       "              ElasticNet(alpha=0.005, l1_ratio=0.08, max_iter=10000),\n",
       "              BayesianRidge()])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">stacking</label><div class=\"sk-toggleable__content\"><pre>stacking(meta_model=KernelRidge(alpha=0.2, coef0=0.8, kernel=&#x27;polynomial&#x27;),\n",
       "         mod=[Lasso(alpha=0.0005, max_iter=10000), Ridge(alpha=60),\n",
       "              SVR(C=13, epsilon=0.009, gamma=0.0004),\n",
       "              KernelRidge(alpha=0.2, coef0=0.8, kernel=&#x27;polynomial&#x27;),\n",
       "              ElasticNet(alpha=0.005, l1_ratio=0.08, max_iter=10000),\n",
       "              BayesianRidge()])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">meta_model: KernelRidge</label><div class=\"sk-toggleable__content\"><pre>KernelRidge(alpha=0.2, coef0=0.8, kernel=&#x27;polynomial&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KernelRidge</label><div class=\"sk-toggleable__content\"><pre>KernelRidge(alpha=0.2, coef0=0.8, kernel=&#x27;polynomial&#x27;)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "stacking(meta_model=KernelRidge(alpha=0.2, coef0=0.8, kernel='polynomial'),\n",
       "         mod=[Lasso(alpha=0.0005, max_iter=10000), Ridge(alpha=60),\n",
       "              SVR(C=13, epsilon=0.009, gamma=0.0004),\n",
       "              KernelRidge(alpha=0.2, coef0=0.8, kernel='polynomial'),\n",
       "              ElasticNet(alpha=0.005, l1_ratio=0.08, max_iter=10000),\n",
       "              BayesianRidge()])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is the final model I use\n",
    "stack_model = stacking(mod=[lasso,ridge,svr,ker,ela,bay],meta_model=ker)\n",
    "stack_model.fit(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "cd624c2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([120845.84286344, 168011.39206917, 192140.53204743, ...,\n",
       "       168800.97995805, 122776.12118464, 212982.63718136])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = np.exp(stack_model.predict(test_X_scaled))\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8bca5b99",
   "metadata": {},
   "outputs": [],
   "source": [
    "result=pd.DataFrame({'Id':test.Id, 'SalePrice':pred})\n",
    "result.to_csv(\"submission.csv\",index=False)"
   ]
  }
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