predict_house_prices.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "from scipy import stats\n",
    "from scipy.stats import norm,skew\n",
    "\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### import train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"./train.csv\")\n",
    "test  = pd.read_csv(\"./test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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</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</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</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</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</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     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",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal     208500  \n",
       "1   2007        WD         Normal     181500  \n",
       "2   2008        WD         Normal     223500  \n",
       "3   2006        WD        Abnorml     140000  \n",
       "4   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>ScreenPorch</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</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>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</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>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</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>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</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>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0  1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
       "1  1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
       "2  1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
       "3  1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
       "4  1465         120       RL         43.0     5005   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... ScreenPorch PoolArea PoolQC  Fence MiscFeature  \\\n",
       "0         Lvl    AllPub  ...         120        0    NaN  MnPrv         NaN   \n",
       "1         Lvl    AllPub  ...           0        0    NaN    NaN        Gar2   \n",
       "2         Lvl    AllPub  ...           0        0    NaN  MnPrv         NaN   \n",
       "3         Lvl    AllPub  ...           0        0    NaN    NaN         NaN   \n",
       "4         HLS    AllPub  ...         144        0    NaN    NaN         NaN   \n",
       "\n",
       "  MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0       0      6    2010        WD         Normal  \n",
       "1   12500      6    2010        WD         Normal  \n",
       "2       0      3    2010        WD         Normal  \n",
       "3       0      6    2010        WD         Normal  \n",
       "4       0      1    2010        WD         Normal  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Id feature can be dropped\n",
    "train.drop(\"Id\",axis=1,inplace=True)\n",
    "test.drop(\"Id\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#outlier detection???"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mu = 180921.20 sigma = 79415.29\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['SalePrice'],fit=norm)\n",
    "\n",
    "(mu,sigma) = norm.fit(train['SalePrice'])\n",
    "print(\"mu = {:.2f} sigma = {:.2f}\".format(mu,sigma))\n",
    "\n",
    "plt.legend(['normal distribution mu = {:.2f} and sigma = {:.2f}'.format(mu,sigma)],loc='best')\n",
    "plt.ylabel('Freq')\n",
    "plt.title('Sales Price dist')\n",
    "\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(train['SalePrice'],plot=plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#log transformation\n",
    "train['SalePrice'] =np.log1p(train['SalePrice'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mu = 12.02 sigma = 0.40\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['SalePrice'],fit=norm)\n",
    "\n",
    "(mu,sigma) = norm.fit(train['SalePrice'])\n",
    "print(\"mu = {:.2f} sigma = {:.2f}\".format(mu,sigma))\n",
    "\n",
    "plt.legend(['normal distribution mu = {:.2f} and sigma = {:.2f}'.format(mu,sigma)],loc='best')\n",
    "plt.ylabel('Freq')\n",
    "plt.title('Sales Price dist')\n",
    "\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(train['SalePrice'],plot=plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "ntrain = train.shape[0]\n",
    "ntest = test.shape[0]\n",
    "y_train = train.SalePrice.values\n",
    "all_data = pd.concat((train, test)).reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_data size is : (2919, 79)\n"
     ]
    }
   ],
   "source": [
    "all_data.drop(['SalePrice'], axis=1, inplace=True)\n",
    "print(\"all_data size is : {}\".format(all_data.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2919\n",
      "MSSubClass        0.000000\n",
      "MSZoning          0.137033\n",
      "LotFrontage      16.649538\n",
      "LotArea           0.000000\n",
      "Street            0.000000\n",
      "                   ...    \n",
      "MiscVal           0.000000\n",
      "MoSold            0.000000\n",
      "YrSold            0.000000\n",
      "SaleType          0.034258\n",
      "SaleCondition     0.000000\n",
      "Length: 79, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(len(all_data))\n",
    "all_data_na = (all_data.isnull().sum() / len(all_data)) *100\n",
    "print(all_data_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_na = all_data_na.drop(all_data_na[all_data_na==0].index).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PoolQC', 'MiscFeature', 'Alley', 'Fence', 'FireplaceQu', 'LotFrontage',\n",
       "       'GarageFinish', 'GarageYrBlt', 'GarageQual', 'GarageCond', 'GarageType',\n",
       "       'BsmtExposure', 'BsmtCond', 'BsmtQual', 'BsmtFinType2', 'BsmtFinType1',\n",
       "       'MasVnrType', 'MasVnrArea', 'MSZoning', 'BsmtFullBath', 'BsmtHalfBath',\n",
       "       'Utilities', 'Functional', 'Exterior2nd', 'Exterior1st', 'SaleType',\n",
       "       'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'Electrical', 'KitchenQual',\n",
       "       'GarageCars', 'GarageArea', 'TotalBsmtSF'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data_na.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Percent missing data by feature')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(15, 12))\n",
    "plt.xticks(rotation='90')\n",
    "sns.barplot(x=all_data_na.index, y=all_data_na)\n",
    "plt.xlabel('Features', fontsize=15)\n",
    "plt.ylabel('Percent of missing values', fontsize=15)\n",
    "plt.title('Percent missing data by feature', fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation map to see how features are correlated with SalePrice\n",
    "corrmat = train.corr()\n",
    "plt.subplots(figsize=(12,9))\n",
    "sns.heatmap(corrmat, vmax=0.9, square=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data description says NA means \"No  Pool\". \n",
    "#That make sense, given the huge ratio of missing value (+99%) and majority of houses have\n",
    "#no Pool at all in general. \n",
    "all_data[\"PoolQC\"] = all_data[\"PoolQC\"].fillna(\"None\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data description says NA means \"no misc feature\"\n",
    "all_data[\"MiscFeature\"] = all_data[\"MiscFeature\"].fillna(\"None\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data description says NA means \"no alley access\"\n",
    "all_data[\"Alley\"] = all_data[\"Alley\"].fillna(\"None\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data description says NA means \"no fence\"\n",
    "all_data[\"Fence\"] = all_data[\"Fence\"].fillna(\"None\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data description says NA means \"no fireplace\"\n",
    "all_data[\"FireplaceQu\"] = all_data[\"FireplaceQu\"].fillna(\"None\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Group by neighborhood and fill in missing value by the median LotFrontage of all the neighborhood\n",
    "all_data['LotFrontage'] = all_data.groupby(\"Neighborhood\")[\"LotFrontage\"].transform(lambda x:x.fillna(x.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in ('GarageType', 'GarageFinish', 'GarageQual', 'GarageCond'):\n",
    "    all_data[col] = all_data[col].fillna('None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'):\n",
    "    all_data[col] = all_data[col].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in ('BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF','TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath'):\n",
    "    all_data[col] = all_data[col].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in ('BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'):\n",
    "    all_data[col] = all_data[col].fillna('None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data[\"MasVnrType\"] = all_data[\"MasVnrType\"].fillna(\"None\")\n",
    "all_data[\"MasVnrArea\"] = all_data[\"MasVnrArea\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['MSZoning'] = all_data['MSZoning'].fillna(all_data['MSZoning'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Utilities\n",
       "AllPub    2916\n",
       "NoSeWa       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For this categorical feature all records are \"AllPub\", except for one \"NoSeWa\" and 2 NA . \n",
    "#Since the house with 'NoSewa' is in the training set, this feature won't help \n",
    "#in predictive modelling. We can then safely remove it.\n",
    "all_data.groupby(\"Utilities\").size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = all_data.drop(['Utilities'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data description says NA means typical\n",
    "all_data[\"Functional\"] = all_data[\"Functional\"].fillna(\"Typ\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# It has one NA value. Since this feature has mostly 'SBrkr', we can set that for the missing value.\n",
    "all_data['Electrical'] = all_data['Electrical'].fillna(all_data['Electrical'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only one NA value, and same as Electrical, we set 'TA' \n",
    "# (which is the most frequent) for the missing value in KitchenQual.\n",
    "all_data['KitchenQual'] = all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Both Exterior 1 & 2 have only one missing value. We will just substitute in the most common string\n",
    "all_data['Exterior1st'] = all_data['Exterior1st'].fillna(all_data['Exterior1st'].mode()[0])\n",
    "all_data['Exterior2nd'] = all_data['Exterior2nd'].fillna(all_data['Exterior2nd'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['SaleType'] = all_data['SaleType'].fillna(all_data['SaleType'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all_data['MSSubClass'] = all_data['MSSubClass'].fillna(\"None\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_na = (all_data.isnull().sum() / len(all_data)) * 100\n",
    "all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSZoning\n",
       "C (all)      25\n",
       "FV          139\n",
       "RH           26\n",
       "RL         2269\n",
       "RM          460\n",
       "dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.groupby('MSZoning').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['MSSubClass'] = all_data['MSSubClass'].apply(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Changing OverallCond into a categorical variable\n",
    "all_data['OverallCond'] = all_data['OverallCond'].astype(str)\n",
    "\n",
    "\n",
    "#Year and month sold are transformed into categorical features.\n",
    "all_data['YrSold'] = all_data['YrSold'].astype(str)\n",
    "all_data['MoSold'] = all_data['MoSold'].astype(str)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       None\n",
       "1       None\n",
       "2       None\n",
       "3       None\n",
       "4       None\n",
       "        ... \n",
       "2914    None\n",
       "2915    None\n",
       "2916    None\n",
       "2917    None\n",
       "2918    None\n",
       "Name: Alley, Length: 2919, dtype: object"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " all_data['Alley']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 78)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape all_data: (2919, 78)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "cols = ('FireplaceQu', 'BsmtQual', 'BsmtCond', 'GarageQual', 'GarageCond', \n",
    "        'ExterQual', 'ExterCond','HeatingQC', 'PoolQC', 'KitchenQual', 'BsmtFinType1', \n",
    "        'BsmtFinType2', 'Functional', 'Fence', 'BsmtExposure', 'GarageFinish', 'LandSlope',\n",
    "        'LotShape', 'PavedDrive', 'Street', 'Alley', 'CentralAir', 'MSSubClass', 'OverallCond', \n",
    "        'YrSold', 'MoSold')\n",
    "# process columns, apply LabelEncoder to categorical features\n",
    "for c in cols:\n",
    "    lbl = LabelEncoder() \n",
    "    lbl.fit(list(all_data[c].values)) \n",
    "    all_data[c] = lbl.transform(list(all_data[c].values))\n",
    "\n",
    "# shape        \n",
    "print('Shape all_data: {}'.format(all_data.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       1\n",
       "2       1\n",
       "3       1\n",
       "4       1\n",
       "       ..\n",
       "2914    1\n",
       "2915    1\n",
       "2916    1\n",
       "2917    1\n",
       "2918    1\n",
       "Name: Alley, Length: 2919, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " all_data['Alley']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding total sqfootage feature \n",
    "all_data['TotalSF'] = all_data['TotalBsmtSF'] + all_data['1stFlrSF'] + all_data['2ndFlrSF']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 79)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Skewed features??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get dummy categorical features\n",
    "all_data = pd.get_dummies(all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 221)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new train and test\n",
    "train = all_data[:ntrain]\n",
    "test = all_data[ntrain:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC\n",
    "from sklearn.ensemble import RandomForestRegressor,  GradientBoostingRegressor\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
    "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "# import xgboost as xgb\n",
    "# import lightgbm as lgb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "n_folds = 5\n",
    "\n",
    "def rmsle_cv(model):\n",
    "    kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(train.values)\n",
    "    rmse= np.sqrt(-cross_val_score(model, train.values, y_train, scoring=\"neg_mean_squared_error\", cv = kf))\n",
    "    return(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: [0.10753398 0.1495919  0.13173708 0.10802364 0.18880341]\n",
      "\n",
      "Lasso score: 0.1371 (0.0303)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lasso = make_pipeline(RobustScaler(), Lasso(alpha =0.0005, random_state=1))\n",
    "score = rmsle_cv(lasso)\n",
    "print(\"score:\",score)\n",
    "print(\"\\nLasso score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: [0.10761076 0.14901863 0.13221868 0.10822968 0.18851957]\n",
      "ElasticNet score: 0.1371 (0.0300)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ENet = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3))\n",
    "score = rmsle_cv(ENet)\n",
    "print(\"score:\",score)\n",
    "print(\"ElasticNet score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jaswant/.local/lib/python3.6/site-packages/sklearn/linear_model/ridge.py:187: LinAlgWarning: Ill-conditioned matrix (rcond=5.18846e-19): result may not be accurate.\n",
      "  overwrite_a=False)\n",
      "/home/jaswant/.local/lib/python3.6/site-packages/sklearn/linear_model/ridge.py:187: LinAlgWarning: Ill-conditioned matrix (rcond=1.75359e-18): result may not be accurate.\n",
      "  overwrite_a=False)\n",
      "/home/jaswant/.local/lib/python3.6/site-packages/sklearn/linear_model/ridge.py:187: LinAlgWarning: Ill-conditioned matrix (rcond=7.29845e-19): result may not be accurate.\n",
      "  overwrite_a=False)\n",
      "/home/jaswant/.local/lib/python3.6/site-packages/sklearn/linear_model/ridge.py:187: LinAlgWarning: Ill-conditioned matrix (rcond=4.68097e-19): result may not be accurate.\n",
      "  overwrite_a=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: [1.33265869 0.57290391 0.783153   0.41163165 1.00292907]\n",
      "Kernel Ridge score: 0.8207 (0.3242)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jaswant/.local/lib/python3.6/site-packages/sklearn/linear_model/ridge.py:187: LinAlgWarning: Ill-conditioned matrix (rcond=4.82552e-19): result may not be accurate.\n",
      "  overwrite_a=False)\n"
     ]
    }
   ],
   "source": [
    "KRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)\n",
    "score = rmsle_cv(KRR)\n",
    "print(\"score:\",score)\n",
    "print(\"Kernel Ridge score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: [0.11146805 0.14125238 0.12482757 0.10855726 0.13153389]\n",
      "Gradient Boosting score: 0.1235 (0.0122)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "GBoost = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,\n",
    "                                   max_depth=4, max_features='sqrt',\n",
    "                                   min_samples_leaf=15, min_samples_split=10, \n",
    "                                   loss='huber', random_state =5)\n",
    "score = rmsle_cv(GBoost)\n",
    "print(\"score:\",score)\n",
    "print(\"Gradient Boosting score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}