house prices prediction-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from scipy.stats import skew,norm\n",
    "from scipy.stats.stats import pearsonr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train.csv\")\n",
    "test = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#save the ID column\n",
    "train_ID = train['Id']\n",
    "test_ID = test['Id']\n",
    "\n",
    "#Drop the ID column since it is unnecessary for the prediction process\n",
    "train.drop(\"Id\",axis =1,inplace = True)\n",
    "test.drop(\"Id\",axis =1,inplace= True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data: \n",
      "\n",
      "Number of columns: 80\n",
      "number of rows: 1460\n",
      "\n",
      "Test data: \n",
      "\n",
      "number of columns:79\n",
      "Number of columns:1459\n"
     ]
    }
   ],
   "source": [
    "print (\"Train data: \\n\")\n",
    "print (\"Number of columns: \" + str (train.shape[1]))\n",
    "print (\"number of rows: \" + str (train.shape[0]))\n",
    "\n",
    "print('\\nTest data: \\n')\n",
    "print (\"number of columns:\" + str (test.shape[1]))\n",
    "print (\"Number of columns:\" +  str (test.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</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>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>Inside</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>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>FR2</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>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>Inside</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>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>Corner</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>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>FR2</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 × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities LotConfig    ...     PoolArea PoolQC Fence  \\\n",
       "0         Lvl    AllPub    Inside    ...            0    NaN   NaN   \n",
       "1         Lvl    AllPub       FR2    ...            0    NaN   NaN   \n",
       "2         Lvl    AllPub    Inside    ...            0    NaN   NaN   \n",
       "3         Lvl    AllPub    Corner    ...            0    NaN   NaN   \n",
       "4         Lvl    AllPub       FR2    ...            0    NaN   NaN   \n",
       "\n",
       "  MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0         NaN       0      2    2008        WD         Normal     208500  \n",
       "1         NaN       0      5    2007        WD         Normal     181500  \n",
       "2         NaN       0      9    2008        WD         Normal     223500  \n",
       "3         NaN       0      2    2006        WD        Abnorml     140000  \n",
       "4         NaN       0     12    2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1460.000000\n",
       "mean     180921.195890\n",
       "std       79442.502883\n",
       "min       34900.000000\n",
       "25%      129975.000000\n",
       "50%      163000.000000\n",
       "75%      214000.000000\n",
       "max      755000.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#descriptive statistics summary\n",
    "train['SalePrice'].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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S53xgga0rz9btsQ1rlzFmhP35eZPOgB/Zn1+KoWWnGgPU1FRTXVXFGX1jMPqgpFJ+x+lF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\npqSymuLyKorKqigur6K0spqqGvd/yAr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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11137f710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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y2G03+OEPw6hi4MB8VyciAhR+aMwB9jKzAYSwOB04M78lZdGGDeEZFdXV4dbj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\niU2hISIisSk0REQktv8PfPu3+VZ11JcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1108c2400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skewness: 1.882876\n",
      "kurtosis: 6.536282\n"
     ]
    }
   ],
   "source": [
    "# kernel density plot\n",
    "sns.distplot(train.SalePrice,fit=norm);\n",
    "plt.ylabel =('Frequency')\n",
    "plt.title = ('SalePrice Distribution');\n",
    "#Get the fitted parameters used by the function\n",
    "(mu, sigma) = norm.fit(train['SalePrice']);\n",
    "#QQ plot\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(train['SalePrice'], plot=plt)\n",
    "plt.show()\n",
    "print(\"skewness: %f\" % train['SalePrice'].skew())\n",
    "print(\"kurtosis: %f\" % train ['SalePrice'].kurt())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4fdLoWKKPkOIvekXbLp+80jqKqxqky+caJIT7MyDIh92nShmROg2T2cLhHRuN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\nSIq/EEJ4ISn+Qgjhhf4/SNLbnfcCzu8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111788390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0pB4/cA6T+SPvxAx99fGzi/bqEaniSu6t8zu+5UaG8Tf+xRp2oT+3cjeX8xs7Rb1WWydn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\nSQGzNvtL8HYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111788320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#log transform the target \n",
    "train[\"SalePrice\"] = np.log1p(train[\"SalePrice\"])\n",
    "\n",
    "#Kernel Density plot\n",
    "sns.distplot(train.SalePrice,fit=norm);\n",
    "plt.ylabel=('Frequency')\n",
    "plt.title=('SalePrice distribution');\n",
    "#Get the fitted parameters used by the function\n",
    "(mu,sigma)= norm.fit(train['SalePrice']);\n",
    "#QQ plot\n",
    "fig =plt.figure()\n",
    "res =stats. probplot(train['SalePrice'], plot=plt)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skewness: 0.121347\n",
      "kurtosis: 0.809519\n"
     ]
    }
   ],
   "source": [
    "print(\"skewness: %f\" % train['SalePrice'].skew())\n",
    "print(\"kurtosis: %f\" % train ['SalePrice'].kurt())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploring the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V+jtm9O0O4HBJg9IFYYemsqmpfKCk4cAHU/sjgHcAoyNiTESMAb5AkcA+Amwt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wAAAg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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111788278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correration matrix\n",
    "corrmat = train.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "sns.heatmap(corrmat,vmax=0.9, square=True)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a/VweupzYbnNx7/s29r4eSF0dkDopiO02lycHYqg2vZeZptReZuaNa9Dna1rL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1xDe57uucjNib8VWVeD5vVR09bk4fej0HG0ofy4vjwdNAMANX1dyo/9kUfdx5GXlIpJ3w6oly1D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\njoBCoVC6Of8Pp/sp0Zv2zfwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112443cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = corrmat.nlargest(10, 'SalePrice')['SalePrice'].index\n",
    "cm = np.corrcoef(train[cols].values.T)\n",
    "sns.set(font_scale=1.25)\n",
    "hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values,\n",
    "                 xticklabels=cols.values)\n",
    "plt.yticks(rotation=0)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GjNWva+drbufPHTYROYpbBULVsMvqRwjWfANvyh11zaGfr40J59BPInIKbhUI1e+MVsUe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\nEgOBiIgAAP8PFwyc31uNVWsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112412ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var ='TotalBsmtSF'\n",
    "data = pd.concat([train['SalePrice'],train[var]],axis=1)\n",
    "data.plot.scatter(x=var, y='SalePrice', ylim =0.800000);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Tp889cy39Q9R9+/ahuLgYX375pWls4MCBWLhwIQoKCkxjtbW10Ov10Gg0cHZ2xo4dO2Sr\nERGRvCwG0JYtWxrlIP/617/Mtv39/bFz5060bNkSSUlJSE1NRf/+/fHRRx/B19cXfn5+aNu2LUpK\nSmSrERGRvBzEh/xAp66uDj/++CPatGmDuro6uLi4PPRB/f398c033+CJJ57AuXPnkJiYCJ1Ohw4d\nOiApKQm+vr4AIHvNkoZe1triJbGtYG+ksTfS2BtpttgbS7fg6h1A1dXVWLVqFbZv347a2lrs27cP\nK1euRLNmzbB48WJ4eHg02oJtEQOo8bE30tgbaeyNNFvsjaUAqvf36qxevRonTpzA1q1b4erqCgAY\nP348Ll++jKVLl/72VRIRkV2pdwDt3bsXiYmJCAwMNI0FBARgyZIlOHTokFUWR0RETVe9A+iXX365\n7+PKjo6O/H0gIiJ6aPUOoPDwcLz//vtm/2jzxx9/xOLFi/HSSy9ZZXFERNR01TuAEhMTUVNTg27d\nukEQBAwdOhQRERFwd3fH/PnzrblGIiJqgur9g3SPPfYYPvjgAxQWFkKn06G2thYajYb/hoaIiBrE\nYgAVFRXdM+bs7Ax/f/975jz++OONvDQiImrKLAZQ7969zb77TRTFe74L7u6Ypa/iISIi+l8WA+ib\nb76Rax1ERGRnLAZQ27ZtH/gC1dXVuHDhQr3mEhER3VXvhxD+/e9/Y+HChcjLy0NdXZ1ZzdHREefO\nnWv0xRERUdNV78ewFy9ejLZt2+LDDz+ESqVCSkoK5s+fDy8vL6xYscKaayQioiao3ldAly5dwsqV\nK+Hn54dOnTrB2dkZr732Glq0aIGNGzeif//+1lwnERE1MfW+AlKpVHB0vPOT1FqtFhcuXABw5/vg\n8vPzrbM6IiJqsuodQGFhYVi9ejWKi4sRFBSEPXv2oLy8HIcOHcJjjz1mzTUSEVETVO8AmjdvHsrL\ny7F3714MGDAAnp6e6N69O5YtW4bJkydbc41ERNQE1eszoJs3b6Jly5amn+jOzc1Fr1690LdvX0RE\nRKB169ZWXSQRETU9Fq+Abt++jbi4OLz44osoKCgAAKSlpWHo0KHYtm0btm3bhtjYWFy7dq1eB9u3\nbx/69u2LoKAgDB06FDk5OQCA06dPIzo6GoGBgYiNjYVerzftI3eNiIjkYTGAUlJS8OOPP2Lbtm3Q\narUQBAHvvvsuOnfujP3792Pv3r3o2bMnVq1a9cADXb16FXPmzMHy5cuRk5OD2NhYJCQkoKqqCpMn\nT8b48eORlZWFHj16ICEhAQBkrxERkXwsBtD+/fsxb948BAcHw8HBAenp6TAYDBg5ciScnZ0BAEOG\nDEF6evoDD/TEE08gPT0dXbp0gdFoRHl5Oby8vJCRkQEvLy9ERUXBxcUF8fHxuHLlCvLy8mSvERGR\nfCx+BlRcXAxfX1/T9vHjx+Ho6IiePXuaxlq2bGn2I3WWqNVqXLhwAdHR0XB0dMSGDRtw6dIlaLVa\n0xxHR0e0a9cOOp0ORUVFstaefvrpep0HERH9dhYDqHXr1rh69Soef/xxiKKII0eOoEuXLvjd735n\nmpOTk4M2bdrU+4BarRZnzpzBzp07MWXKFIwbNw5ubm5mc1QqFQRBQEVFhaw1S7y93eHk5Fjv8/w1\nHx/PBu1nD9gbaeyNNPZG2qPUG4sBNGjQICxevBjTpk1DVlYWioqKMH36dFM9NzcXq1evxsCBA+t9\nwLu37oYOHYqPP/4YKpUKlZWVZnMEQYBarZa9ZklZWUW9z/HXfHw8UVx8q0H7NnXsjTT2Rhp7I80W\ne2MpEC1+BhQfH4/u3bvjL3/5C9LS0jB16lRERUUBAJYvX47o6Gg8++yziI+Pf+Aijh8/jjFjxpiN\nGY1GaLVa0xN2AFBbWwu9Xg+NRiN7jYiI5GMxgJycnDB37lxkZmYiIyMDkyZNMtWio6Px1Vdf4cMP\nP4Srq+sDD9S+fXucP38eX3/9NWpqarBt2zbU1tYiLCwMJSUlSE1NhdFoxPr16+Hr6ws/Pz+EhobK\nWiMiIvk4iKIoynWwEydOYPHixbh69So6duyId955B1qtFufOnUNiYiJ0Oh06dOiApKQk08MPctek\nNPSy1hYviW0FeyONvZHG3kizxd5YugUnawA9yhhAjY+9kcbeSGNvpNlibxr8GRAREZG1MICIiEgR\nDCAiIlIEA4iIiBTBACIiIkUwgIiISBEMICIiUgQDiIiIFMEAIiIiRTCAiIhIEQwgIiJSBAOIiIgU\nwQAiIiJFMICIiEgRDCAiIlIEA4iIiBTBACIiIkXIGkAHDx7EgAED0LVrVwwZMgQnT54EABw+fBiR\nkZEIDAxEXFwcSktLTfvIXSMiInnIFkBXrlzB7NmzkZiYiBMnTmD06NGYNGkSfvrpJ8ycOROLFi1C\nZmYmWrZsicTERABAcXGxrDUiIpKPbAH0008/YdiwYQgJCUGzZs0wcOBAAEBqaiqCg4MRGhoKV1dX\nzJgxA4cOHYLBYMCBAwdkrRERkXyc5DpQSEgIQkJCTNunTp2CIAjQ6/XQarWmcW9vb3h4eKCwsBA6\nnU7WWqdOnax1+kRE9D9kC6BfKywsxJQpUzBt2jTodDq4ubmZ1VUqFQRBgCAI8PT0lK1mibe3O5yc\nHB/2VAEAPj6eD55kp9gbaeyNNPZG2qPUG9kD6MyZM5g4cSJiY2Mxbtw4LF68GJWVlWZzBEGAWq2G\nSqWStWZJWVnFw54qgDtvhuLiWw3at6ljb6SxN9LYG2m22BtLgSjrU3DfffcdxowZg+nTp2PKlCkA\nAI1Gg4KCAtOc0tJSGAwG+Pr6yl4jIiL5yBZABQUFmDp1KpYuXYqYmBjTeEREBLKzs3Hs2DFUVVUh\nOTkZ4eHhUKvVsteIiEg+DqIoinIcaPny5di0aRNUKpXZ+MaNGyEIApYtW4Zr166hW7duSEpKQvPm\nzQHcuWqSsyaloZe1tnhJbCvYG2nsjTT2Rpot9sbSLTjZAuhRxwBqfOyNNPZGGnsjzRZ7YzOfARER\nEd3FACIiIkUwgIiISBEMICIiUgQDiIiIFMEAIiIiRTCAiIhIEQwgIiJSBAOIiIgUwQAiIiJFMICI\niEgRDCAiIlIEA4iIiBTBACIiIkUwgIiISBEMICIiUgQDiIiIFKFIAG3atAlz5swxbR8+fBiRkZEI\nDAxEXFwcSktLFasREZE8ZA2g2tpafPTRR1ixYoVprLi4GDNnzsSiRYuQmZmJli1bIjExUZEaERHJ\nx0nOg82ePRu3bt1CTEwMjEYjAODAgQMIDg5GaGgoAGDGjBno2bMnDAaD7DUPDw8520FEZNdkvQKa\nNWsWNmzYAB8fH9OYTqeDVqs1bXt7e8PDwwOFhYWy14iISD6yBlCrVq3uGRMEAW5ubmZjKpUKgiDI\nXiMiIvnIegvuflQqFSorK83GBEGAWq2WvWaJt7c7nJwcH/b0AAA+Pp4N2s8esDfS2Btp7I20R6k3\nigeQRqNBenq6abu0tBQGgwG+vr6y1ywpK6to0Pn5+HiiuPhWg/Zt6tgbaeyNNPZGmi32xlIgKv7v\ngCIiIpCdnY1jx46hqqoKycnJCA8Ph1qtlr1GRETyUfwKqHXr1khOTsaSJUtw7do1dOvWDUlJSYrU\niIhIPg6iKIpKL+JR0NDLWlu8JLYV7I009kYaeyPNFntj07fgiIjIPjGAiIhIEQwgIiJSBAOIiIgU\nwQAiIiJFMICIiEgRDCAiIlIEA4iIiBTBACIiIkUwgIiISBEMICIiUgQDiIiIFMEAIiIiRTCAiIhI\nEQwgIiJSBAOIiIgUwQAiIiJFMICIiEgRdhtAp0+fRnR0NAIDAxEbGwu9Xq/0koiI7IpdBlBVVRUm\nT56M8ePHIysrCz169EBCQoLSyyIisitOSi9ACRkZGfDy8kJUVBQAID4+Hps3b0ZeXh6efvrpRjuO\nocKIsTPSGu31GpOjA+Dk5AAHAG4uTnjMwwUtPFUQqoy4dPUX1AHwdHdGq9+54nKRASKAZs2ATk81\nx4iIZ7DjaD6uldyGobIGnu5OaO2txsjIZ+GhcgFw59y37r+I66W3cauiBh4qJ/y+xZ05EIGt+y+i\n/LYRXmoX9Hn+CaR8dRa3KqrRzMEBHZ/yxhuvdDTNKy4X4OOlMnv9u+4ex9Kch5nXFPHcpc/dnntT\nH9buj10GUH5+PrRarWnb0dER7dq1g06na9QA2rr/YqO9VmOrFYHaahEAUFldjfLb1dBfv20255fb\n1fjldrVpu64OOKsrxdVPT6HsVpVpvOxWlWnf+OjnANw59+zcG/+dY6jCleL/vv6va6cuFaO6Vvz/\ndYk4qyvF1n0XzeYVXLtl9vp3/fo4UnMeZl5TxHOXPnd77k19WLs/dhlAFRUVcHNzMxtTqVQQBEFy\nHx8fz4c+Tvlt40Pv8yioqKy+73j5baOpT1Lnfr/xmv8PnwfN+/XrS82735yHmWdLGmt9j+K5P0h9\n1/+gc7fn3tSHtfvjIIrivf/3N3GbNm1CTk4O1q5daxobMmQIJk2ahIiICAVXRkRkP+zyIQStVouC\nggLTdm1tLfR6PTQajXKLIiKyM3YZQKGhoSgpKUFqaiqMRiPWr18PX19f+Pn5Kb00IiK7YZe34ADg\n3LlzSExMhE6nQ4cOHZCUlARfX1+ll0VEZDfsNoCIiEhZdnkLjoiIlMcAshJ7+Kqf5cuXo3PnzggK\nCkJQUBBCQ0MBAIcPH0ZkZCQCAwMRFxeH0tJS0z7WqNmSTZs2Yc6cOaZtuXthy336395MnToVAQEB\npvfPkCFDTLWvvvoKvXr1QlBQEGbNmoXKykqr1pR08OBBDBgwAF27dsWQIUNw8uRJAHby3hGp0VVW\nVoovvPCCuGvXLrGqqkpMSUkRBw8erPSyGt3o0aPF/fv3m43duHFDDA4OFjMyMsTKykpx3rx54ptv\nvmm1mq2oqakRN2zYILZv316cPXu2KIry98JW+3S/3oiiKL788svi+fPn75n/ww8/iKGhoWJubq54\n69Yt8Y033hCXLVtmtZqS9Hq92LVrVzEzM1Osra0V09LSxJCQELGoqMgu3jsMICs4fPiwOGDAANN2\nTU2N2K1bN/HSpUsKrqrxhYWFiXq93mzsk08+ESdMmGDaLi0tFTt27CjeunXLKjVbMWPGDHHChAni\nggULTH9k5e6Frfbpfr0xGAxip06dxKqqqnvmr1ixQly4cKFp++zZs2JYWJjVakrKzMwUk5KSzMZC\nQkLEDz74wC7eO7wFZwWWvuqnqbhx4wbKy8uxdOlShIWFYdiwYTh16hR0Op3ZuXt7e8PDwwOFhYVW\nqdmKWbNmYcOGDfDx8TGNyd0LW+3T/Xpz8eJFuLu744033kBYWBhGjx6Ny5cvA7i3bxqNBqWlpSgv\nL7dKTUkhISGYPXu2afvUqVMQBAF6vd4u3jsMICtoyFf9PGrKysoQEhKCCRMm4OjRo4iJicHEiRNx\n+/ZtyXMXBKHRa7aiVatW94xZ43wfxT5J9aZLly5YsGABjhw5gq5du2LSpEmorq6+5zxUKhUAoLKy\n0io1W1FYWIgpU6Zg2rRpaNasmV28dxhAVqBSqe55YwuCALVardCKGp+/vz82b96MoKAguLi4ICYm\nBq1atUJWVpbkuVvqS0Nrtswa59tU+tSjRw9s3LgRTz/9NFxdXTFlyhTcvHkTly9fhpubG6qq/vtl\nt3f/CLq7u1ulZgvOnDmD4cOHY9iwYRg3bpzdvHcYQFZgD1/1c/LkSWzfvt1szGg0YtSoUWbnXlpa\nCoPBAF9fX2g0mkav2TJrnG9T6dO3336Lr7/+2rRdW1uL2tpauLq6QqvVIj8/31TLz89Hy5Yt8dhj\nj1mlprTvvvsOY8aMwfTp0zFlyhQA9vPeYQBZgT181Y+LiwtWrlyJEydOoKamBlu2bIHRaERkZCSy\ns7Nx7NgxVFVVITk5GeHh4VCr1YiIiGj0mi2zxvk2lT5VV1djyZIluHz5MoxGI9577z08++yz0Gg0\n6N+/P3bv3o3z58/DYDBg7dq1pt/uskZNSQUFBZg6dSqWLl2KmJgY07jdvHes+oiDHTt79qw4ZMgQ\nMTAwUBwxYoRYWFio9JIa3Y4dO8SIiAixS5cu4quvvirm5uaKoiiKR48eFfv16ycGBQWJb7zxhlhS\nUmLaxxqiiryEAAAE0ElEQVQ1W7J27VqzR43l7oUt9+l/e7Nx40bxD3/4gxgYGCiOGzdOLCoqMtV2\n7Ngh/vGPfxS7du0qzpw5UxQEwao1pSQlJYn+/v5iYGCg2X/Z2dl28d7hV/EQEZEieAuOiIgUwQAi\nIiJFMICIiEgRDCAiIlIEA4iIiBTBACIiIkUwgIhk4u/vj8zMzAbtW1JSgr17994z/umnn8Lf3x97\n9uz5rcsjkh0DiOgRsGrVKhw5cuSe8T179sDX1xdpaWkKrIrot2EAET0C7vfvxa9fv44TJ04gPj4e\n6enpKCkpUWBlRA3HACKyEQcPHsQrr7yCgIAADBo0CMePHwcApKSkYMeOHdixYwdGjhxpmr937160\naNECAwcOhFqtxq5du8xez9/fH6tXr0ZoaCimTp0KAMjMzMTgwYMREBCAgQMH4ttvvzXNNxgMmDt3\nLrp3747nnnsOffv2xcGDB2U4c7JXDCAiG3D8+HEkJCRg+PDhSEtLQ0REBOLi4lBUVISxY8eiX79+\n6NevH1JSUkz77NmzB71794aTkxNeeuml+96GS09Px2effYZp06bh+vXriI+PR0xMDHbv3o1Ro0Zh\n+vTp+OGHHwAAS5YsgV6vx6ZNm7B7925069YN8+bNg9FolK0PZF8YQEQ24JNPPkG/fv3w2muvQaPR\nYMqUKQgICMAnn3wCtVoNNzc3uLm5wcvLCwBw9epVnD59Gr179wYAvPzyy/jhhx9w6dIls9cdPnw4\nNBoN/Pz8sH37dvzhD39AbGwsfH19MXToULzyyivYunUrAOD555/HwoUL0b59ezz11FMYO3YsysvL\neWuPrMZJ6QUQ0Z2foR4xYoTZWEBAgNlv2Pzanj174O7uju7duwMAevbsCVdXV6SmpuLPf/6zaV6b\nNm3MjvHtt98iKCjINFZdXY2AgAAAQHR0NA4ePIjPPvsMOp0O58+fB3Dnt3qIrIEBRGQDXF1d7xkT\nRVHyj//XX3+NiooKszCpra3Frl27MGPGDDRrdufmhouLi1l94MCBmDhxotlr3Z0za9Ys5OTkYNCg\nQRgxYgR8fHzw6quv/uZzI5LCACKyAU899RTOnj1rNnbq1CnT1YmDg4PpSbjLly8jNzcXCxcuRNeu\nXc3mL1iwAN9//z1eeOGF+x7jzJkzePLJJ01jH3zwAdRqNYYOHYrdu3fj888/Nx3zfo99EzUmBhCR\njM6cOYOqqiqzsbCwMLz++ut4/fXXERQUhO7du2P37t04e/YsFi1aBABQqVTIy8vDzZs3sWfPHrRo\n0QJ/+tOf4OT03/+F/fz88Ne//hWpqan3DaARI0Zg69atSElJwcCBA5GdnY1169YhJSUFLi4uUKlU\n2L9/P5o3b478/HzTsfkQAlkLf5COSCb+/v73HU9PT4ePjw/S0tKwbt06/PTTT/D398fMmTMRFhYG\nAMjJycGkSZPQunVrVFVVoVevXpg9e/Y9r5WSkoJ//OMfSE9PR9euXbFlyxaEhoaaHWvVqlXIy8tD\nmzZtMGHCBNNPQR88eBDLly/HjRs38MQTT2DMmDFYs2YN5s2bh379+lmhI2TvGEBERKQIPoZNRESK\nYAAREZEiGEBERKQIBhARESmCAURERIpgABERkSIYQEREpAgGEBERKYIBREREivg/MuehNTLCdRYA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1122925c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatter plot LotArea/salePrice\n",
    "var = 'LotArea'\n",
    "data = pd.concat([train['SalePrice'], train[var]], axis=1)\n",
    "data.plot.scatter(x= var, y='SalePrice', ylim =(0,800000));\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hLFiwQJg2bdoj1aSi1+uFvn37CqmpqUJNTY2wa9cuwc/PTygoKJBNj4IgCPn5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1123f74a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatter plot GrLivArea/salePrice\n",
    "var ='GrLivArea'\n",
    "data =pd.concat([train['SalePrice'], train[var]], axis=1)\n",
    "data.plot.scatter(x=var, y='SalePrice',ylim=(0,800000));\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yZb3h+++/R/fu3c3WFDuugmRRX18vnnzySfHll1+KhoYGkZKSIkaOHKlaHr1e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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111ce27f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatter plot GarageArea/SalePrice\n",
    "var = 'GarageArea'\n",
    "data =pd.concat([train['SalePrice'], train[var]], axis=1)\n",
    "data.plot.scatter(x=var,y='SalePrice', ylim= (0,800000));\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Deleting Outliers of GrLivArea\n",
    "train = train.drop(train[(train['GrLivArea']>4000) & (train['SalePrice']<300000)].index)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relation exploration for categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SkhQQEKCJEyequLhY+fn5ptcAAEDjeAwPlZWVCgkJcS9ff/31CggI0IULFwxvKC8vT5s2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\neAAAAIb8P+Qa8eZUx9FJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1119197f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#box plot overallqual/salePrice\n",
    "var = 'OverallQual'\n",
    "data = pd.concat([train['SalePrice'], train[var]], axis=1)\n",
    "f, ax =plt.subplots(figsize=(8,6))\n",
    "fig = sns.boxplot(x=var, y='SalePrice', data=data)\n",
    "fig.axis(ymin=0, ymax=800000)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SJEnTMEmSePTRR2POnDmxcePGiIjo2rVrfPe7342LL744srOzm7SjzW3TpuLm7gIAAABN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\nhBBCCCFEBziBJoQQQgghhBBCdOD/XyP4P16BYlgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112b43ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#year built\n",
    "var  = 'YearBuilt'\n",
    "data= pd.concat([train['SalePrice'], train[var]], axis =1)\n",
    "f, ax = plt.subplots(figsize=(16, 8))\n",
    "fig = sns.boxplot(x=var, y='SalePrice', data=data)\n",
    "fig.axis(ymin=0, ymax=800000)\n",
    "plt.xticks(rotation=90);\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inputing Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</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>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>Inside</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>12.247699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>FR2</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>12.109016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>Inside</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>12.317171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>Corner</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>11.849405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>FR2</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>12.429220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities LotConfig    ...     PoolArea PoolQC Fence  \\\n",
       "0         Lvl    AllPub    Inside    ...            0    NaN   NaN   \n",
       "1         Lvl    AllPub       FR2    ...            0    NaN   NaN   \n",
       "2         Lvl    AllPub    Inside    ...            0    NaN   NaN   \n",
       "3         Lvl    AllPub    Corner    ...            0    NaN   NaN   \n",
       "4         Lvl    AllPub       FR2    ...            0    NaN   NaN   \n",
       "\n",
       "  MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0         NaN       0      2    2008        WD         Normal  12.247699  \n",
       "1         NaN       0      5    2007        WD         Normal  12.109016  \n",
       "2         NaN       0      9    2008        WD         Normal  12.317171  \n",
       "3         NaN       0      2    2006        WD        Abnorml  11.849405  \n",
       "4         NaN       0     12    2008        WD         Normal  12.429220  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_data size is: (2915, 79) \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Raio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PoolQC</th>\n",
       "      <td>99.725557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature</th>\n",
       "      <td>96.397942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>93.207547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>80.445969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <td>48.713551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>16.672384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish</th>\n",
       "      <td>5.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>5.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual</th>\n",
       "      <td>5.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond</th>\n",
       "      <td>5.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType</th>\n",
       "      <td>5.385935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure</th>\n",
       "      <td>2.813036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond</th>\n",
       "      <td>2.813036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>2.778731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <td>2.744425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <td>2.710120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>0.823328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>0.789022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning</th>\n",
       "      <td>0.137221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <td>0.068611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Missing Raio\n",
       "PoolQC           99.725557\n",
       "MiscFeature      96.397942\n",
       "Alley            93.207547\n",
       "Fence            80.445969\n",
       "FireplaceQu      48.713551\n",
       "LotFrontage      16.672384\n",
       "GarageFinish      5.454545\n",
       "GarageYrBlt       5.454545\n",
       "GarageQual        5.454545\n",
       "GarageCond        5.454545\n",
       "GarageType        5.385935\n",
       "BsmtExposure      2.813036\n",
       "BsmtCond          2.813036\n",
       "BsmtQual          2.778731\n",
       "BsmtFinType2      2.744425\n",
       "BsmtFinType1      2.710120\n",
       "MasVnrType        0.823328\n",
       "MasVnrArea        0.789022\n",
       "MSZoning          0.137221\n",
       "BsmtFullBath      0.068611"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data = pd.concat((train.loc[:, 'MSSubClass': 'SaleCondition'],\n",
    "                     test.loc[:,'MSSubClass':'SaleCondition']))\n",
    "print(\"all_data size is: {} \".format(all_data.shape))\n",
    "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)\n",
    "# [:30]\n",
    "missing_data =pd.DataFrame({'Missing Raio':all_data_na})\n",
    "missing_data.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### inputing missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Based on feature description provide, A feature that has NA means it is absent "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for col in ('PoolQC','MiscFeature','GarageType','Alley','Fence','FireplaceQu','GarageFinish',\n",
    "           'GarageQual','GarageCond','MasVnrType','MSSubClass'):\n",
    "    all_data[col] = all_data[col].fillna('None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Replacing missing value with 0(since no garage = no cars in such garage)\n",
    "for col in ('GarageYrBlt','GarageArea', 'GarageCars'):\n",
    "    all_data[col] = all_data[col].fillna(0)\n",
    "\n",
    "#missing values are likely zero for no basement \n",
    "for col in ('BsmtFinSF1','BsmtFinSF2', 'BsmtUnfSF','TotalBsmtSF','BsmtFullBath',\n",
    "            'BsmtHalfBath'):\n",
    "    all_data[col] = all_data[col].fillna(0)\n",
    "\n",
    "#\n",
    "all_data[\"MasVnrArea\"] = all_data[\"MasVnrArea\"].fillna(0)\n",
    "\n",
    "#for below categorical basement-related feature NaN means that there is no basement \n",
    "for col in ('BsmtQual', 'BsmtCond','BsmtExposure','BsmtFinType1','BsmtFinType2'):\n",
    "    all_data[col] = all_data[col].fillna('None')\n",
    "    \n",
    "#group by Neigborhood and fill missing value with median Lot frontage of all the neighboorhood\n",
    "all_data[\"LotFrontage\"] = all_data.groupby(\"Neighborhood\")[\"LotFrontage\"].transform(\n",
    "lambda x: x.fillna(x.median()))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Setting Mode values for missing entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#msZoning classification: 'RL' is common\n",
    "all_data ['MSZoning'] = all_data['MSZoning'].fillna(all_data['MSZoning'].mode()[0])\n",
    "\n",
    "#functional: NA is typical\n",
    "all_data[\"Functional\"] = all_data[\"Functional\"].fillna('Typ')\n",
    "\n",
    "#Electrical\n",
    "all_data['Electrical'] = all_data['Electrical'].fillna(all_data['Electrical'].mode()[0])\n",
    "\n",
    "#KitchenQual\n",
    "all_data['KitchenQual'] =all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode()[0])\n",
    "\n",
    "#Extrerior !st and Exterior 2nd\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])\n",
    "\n",
    "#sale type\n",
    "all_data['SaleType'] = all_data['SaleType'].fillna(all_data['SaleType'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#dropping as same value 'AllPub' for all records except 2NA and 1 'NoSeWa'\n",
    "all_data = all_data.drop(['Utilities'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Transforming required numerical features to categorical \n",
    "all_data['MSSubClass']= all_data['MSSubClass'].apply(str)\n",
    "all_data['OverallCond'] =all_data['OverallCond'].astype(str)\n",
    "all_data['YrSold'] = all_data['YrSold'].astype(str)\n",
    "all_data['MoSold'] = all_data['MoSold'].astype(str)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape all_data: (2915, 78)\n"
     ]
    }
   ],
   "source": [
    "#Label Encoding some categorical variables\n",
    "#for information in their ordering set\n",
    "\n",
    "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",
    "#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",
    "#shape\n",
    "print('Shape all_data: {}'.format(all_data.shape))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#add total surface area as TotalSf = basement + firstflr + secondflr\n",
    "all_data['TotalSF'] = all_data['TotalBsmtSF'] + all_data['1stFlrSF'] + all_data['2ndFlrSF']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\skew in numerical features: \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Skew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MiscVal</th>\n",
       "      <td>21.932147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolArea</th>\n",
       "      <td>18.701829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>13.123758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <td>12.080315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3SsnPorch</th>\n",
       "      <td>11.368094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandSlope</th>\n",
       "      <td>4.971350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <td>4.298845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Skew\n",
       "MiscVal       21.932147\n",
       "PoolArea      18.701829\n",
       "LotArea       13.123758\n",
       "LowQualFinSF  12.080315\n",
       "3SsnPorch     11.368094\n",
       "LandSlope      4.971350\n",
       "KitchenAbvGr   4.298845"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#log transform skewed numeric features \n",
    "numeric_features = all_data.dtypes[all_data.dtypes != \"object\"].index\n",
    "\n",
    "skewed_features = all_data[numeric_features].apply(lambda x : skew (x.dropna())).sort_values(ascending=False)\n",
    "#compute skewness\n",
    "print (\"\\skew in numerical features: \\n\")\n",
    "skewness = pd.DataFrame({'Skew' :skewed_features})   \n",
    "skewness.head(7)                                              \n",
    "\n",
    "                                 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Box cox transformation of highly skewed features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 59 skewed numerical features to box cox transform\n"
     ]
    }
   ],
   "source": [
    "skewness = skewness[abs(skewness) > 0.75]\n",
    "print (\"There are {} skewed numerical features to box cox transform\".format(skewness.shape[0]))\n",
    "\n",
    "from scipy.special import boxcox1p \n",
    "skewed_features = skewness.index\n",
    "lam = 0.15\n",
    "for feat in skewed_features:\n",
    "    all_data[feat] = boxcox1p(all_data[feat], lam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### adding dummy categorical features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2915, 220)\n"
     ]
    }
   ],
   "source": [
    "all_data = pd.get_dummies(all_data)\n",
    "print(all_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ntrain = train.shape[0]\n",
    "ntest = test.shape[0]\n",
    "y_train= train.SalePrice.values\n",
    "train = pd.DataFrame(all_data[:ntrain])\n",
    "test = pd.DataFrame(all_data[ntrain:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear regression Modeling\n",
    "I will be implementing\n",
    "1. Lasso Regression\n",
    "2. Gradient Boosting Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#validation function\n",
    "n_folds = 5\n",
    "\n",
    "def RMSLE_cv(model):\n",
    "    kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(train.values)\n",
    "    rmse= np.sqrt(-cross_val_score(model, train.values, y_train, scoring=\"neg_mean_squared_error\",\n",
    "cv = kf))\n",
    "    return(rmse)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#lasso\n",
    "lasso = make_pipeline(RobustScaler(), Lasso(alpha = 0.0005, random_state = 1))\n",
    "\n",
    "#Gradient Boosting Regression\n",
    "GBoost = GradientBoostingRegressor(loss='huber', learning_rate=0.05, n_estimators=3000,\n",
    "                                   min_samples_split=10, min_samples_leaf=15,max_depth=4,\n",
    "                                   random_state=5,max_features='sqrt')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### scores from the above models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Lasso score: 0.1112 (0.0073)\n",
      "\n",
      "\n",
      " GBoost score: 0.1152 (0.0074)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Lasso\n",
    "score = RMSLE_cv(lasso)\n",
    "print (\"\\n Lasso score: {:.4f} ({:.4f})\\n\".format(score.mean(),score.std()))\n",
    "\n",
    "#Gradient Boosting Regression\n",
    "score = RMSLE_cv(GBoost)\n",
    "print (\"\\n GBoost score: {:.4f} ({:.4f})\\n\".format(score.mean(),score.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### stacking the models "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### averaging base model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Averaged base models score: 0.1081 (0.0071)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class AveragingModels(BaseEstimator, RegressorMixin, TransformerMixin):\n",
    "    def __init__(self, models):\n",
    "        self.models = models\n",
    "        \n",
    "    # we define clones of the original models to fit the data in\n",
    "    def fit(self, X, y):\n",
    "        self.models_ = [clone(x) for x in self.models]\n",
    "        \n",
    "        # Train cloned base models\n",
    "        for model in self.models_:\n",
    "            model.fit(X, y)\n",
    "\n",
    "        return self\n",
    "    \n",
    "    #Now we do the predictions for cloned models and average them\n",
    "    def predict(self, X):\n",
    "        predictions = np.column_stack([\n",
    "            model.predict(X) for model in self.models_\n",
    "        ])\n",
    "        return np.mean(predictions, axis=1)   \n",
    "\n",
    "\n",
    "    \n",
    "# Averaged base models score\n",
    "\n",
    "averaged_models = AveragingModels(models = (GBoost, lasso))\n",
    "\n",
    "score = RMSLE_cv(averaged_models)\n",
    "print(\" Averaged base models score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#defining RMSLE evaluation function\n",
    "def RMSLE (y, y_pred):\n",
    "    return np.sqrt(mean_squared_error(y, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSLE score on the train data:\n",
      "0.069815526114\n",
      "Accuracy score:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9689084324467041"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#final training and prediction of the stacked regressor\n",
    "\n",
    "averaged_models.fit(train.values, y_train) \n",
    "stacked_train_pred = averaged_models.predict(train.values)\n",
    "stacked_pred = np.expm1(averaged_models.predict(test.values))\n",
    "print(\"RMSLE score on the train data:\") \n",
    "print(RMSLE(y_train,stacked_train_pred))\n",
    "print(\"Accuracy score:\") \n",
    "averaged_models.score(train.values, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>121425.132538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>155609.339337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>191357.011599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>196858.153749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>189899.163028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id      SalePrice\n",
       "0  1461  121425.132538\n",
       "1  1462  155609.339337\n",
       "2  1463  191357.011599\n",
       "3  1464  196858.153749\n",
       "4  1465  189899.163028"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensemble = stacked_pred *1\n",
    "submit = pd.DataFrame()\n",
    "submit['id'] = test_ID\n",
    "submit['SalePrice'] = ensemble\n",
    "submit.to_csv('submission.csv', index = False)\n",
    "submit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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