HousePricePrediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Name : Rohan Singh Rajput and Rohan Vardhan\n",
    "### House Price Prediction \n",
    "Description : This is a notebook for visualization of various features which the sales price of houses. Then data is taken from the \"Kaggle House Price Prediction\" challenge. \n",
    "             \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Load Data**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First lets import all the libraries that will be used to load train and test datasets and data manipulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "\n",
    "# Pandas \n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame \n",
    "\n",
    "# Numpy and Matplotlib\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "#sns.set_style('whitegrid')\n",
    "%matplotlib inline\n",
    "\n",
    "# Machine Learning \n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get Data in Dataframe \n",
    "train = pd.read_csv('./input/train.csv')\n",
    "test = pd.read_csv('./input/test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preview of train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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  \\\n",
       "0         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "1         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "2         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "3         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "4         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "\n",
       "  MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0      2   2008        WD         Normal     208500  \n",
       "1      5   2007        WD         Normal     181500  \n",
       "2      9   2008        WD         Normal     223500  \n",
       "3      2   2006        WD        Abnorml     140000  \n",
       "4     12   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# head() shows the first 5 rows of the data\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "<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  \\\n",
       "0         Lvl    AllPub      ...               120        0    NaN  MnPrv   \n",
       "1         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "2         Lvl    AllPub      ...                 0        0    NaN  MnPrv   \n",
       "3         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "4         HLS    AllPub      ...               144        0    NaN    NaN   \n",
       "\n",
       "  MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0         NaN       0      6    2010        WD         Normal  \n",
       "1        Gar2   12500      6    2010        WD         Normal  \n",
       "2         NaN       0      3    2010        WD         Normal  \n",
       "3         NaN       0      6    2010        WD         Normal  \n",
       "4         NaN       0      1    2010        WD         Normal  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 1460 entries in the train data set and 1459 entries in test data set. The data contains some NaN values too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      "Id               1460 non-null int64\n",
      "MSSubClass       1460 non-null int64\n",
      "MSZoning         1460 non-null object\n",
      "LotFrontage      1201 non-null float64\n",
      "LotArea          1460 non-null int64\n",
      "Street           1460 non-null object\n",
      "Alley            91 non-null object\n",
      "LotShape         1460 non-null object\n",
      "LandContour      1460 non-null object\n",
      "Utilities        1460 non-null object\n",
      "LotConfig        1460 non-null object\n",
      "LandSlope        1460 non-null object\n",
      "Neighborhood     1460 non-null object\n",
      "Condition1       1460 non-null object\n",
      "Condition2       1460 non-null object\n",
      "BldgType         1460 non-null object\n",
      "HouseStyle       1460 non-null object\n",
      "OverallQual      1460 non-null int64\n",
      "OverallCond      1460 non-null int64\n",
      "YearBuilt        1460 non-null int64\n",
      "YearRemodAdd     1460 non-null int64\n",
      "RoofStyle        1460 non-null object\n",
      "RoofMatl         1460 non-null object\n",
      "Exterior1st      1460 non-null object\n",
      "Exterior2nd      1460 non-null object\n",
      "MasVnrType       1452 non-null object\n",
      "MasVnrArea       1452 non-null float64\n",
      "ExterQual        1460 non-null object\n",
      "ExterCond        1460 non-null object\n",
      "Foundation       1460 non-null object\n",
      "BsmtQual         1423 non-null object\n",
      "BsmtCond         1423 non-null object\n",
      "BsmtExposure     1422 non-null object\n",
      "BsmtFinType1     1423 non-null object\n",
      "BsmtFinSF1       1460 non-null int64\n",
      "BsmtFinType2     1422 non-null object\n",
      "BsmtFinSF2       1460 non-null int64\n",
      "BsmtUnfSF        1460 non-null int64\n",
      "TotalBsmtSF      1460 non-null int64\n",
      "Heating          1460 non-null object\n",
      "HeatingQC        1460 non-null object\n",
      "CentralAir       1460 non-null object\n",
      "Electrical       1459 non-null object\n",
      "1stFlrSF         1460 non-null int64\n",
      "2ndFlrSF         1460 non-null int64\n",
      "LowQualFinSF     1460 non-null int64\n",
      "GrLivArea        1460 non-null int64\n",
      "BsmtFullBath     1460 non-null int64\n",
      "BsmtHalfBath     1460 non-null int64\n",
      "FullBath         1460 non-null int64\n",
      "HalfBath         1460 non-null int64\n",
      "BedroomAbvGr     1460 non-null int64\n",
      "KitchenAbvGr     1460 non-null int64\n",
      "KitchenQual      1460 non-null object\n",
      "TotRmsAbvGrd     1460 non-null int64\n",
      "Functional       1460 non-null object\n",
      "Fireplaces       1460 non-null int64\n",
      "FireplaceQu      770 non-null object\n",
      "GarageType       1379 non-null object\n",
      "GarageYrBlt      1379 non-null float64\n",
      "GarageFinish     1379 non-null object\n",
      "GarageCars       1460 non-null int64\n",
      "GarageArea       1460 non-null int64\n",
      "GarageQual       1379 non-null object\n",
      "GarageCond       1379 non-null object\n",
      "PavedDrive       1460 non-null object\n",
      "WoodDeckSF       1460 non-null int64\n",
      "OpenPorchSF      1460 non-null int64\n",
      "EnclosedPorch    1460 non-null int64\n",
      "3SsnPorch        1460 non-null int64\n",
      "ScreenPorch      1460 non-null int64\n",
      "PoolArea         1460 non-null int64\n",
      "PoolQC           7 non-null object\n",
      "Fence            281 non-null object\n",
      "MiscFeature      54 non-null object\n",
      "MiscVal          1460 non-null int64\n",
      "MoSold           1460 non-null int64\n",
      "YrSold           1460 non-null int64\n",
      "SaleType         1460 non-null object\n",
      "SaleCondition    1460 non-null object\n",
      "SalePrice        1460 non-null int64\n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1459 entries, 0 to 1458\n",
      "Data columns (total 80 columns):\n",
      "Id               1459 non-null int64\n",
      "MSSubClass       1459 non-null int64\n",
      "MSZoning         1455 non-null object\n",
      "LotFrontage      1232 non-null float64\n",
      "LotArea          1459 non-null int64\n",
      "Street           1459 non-null object\n",
      "Alley            107 non-null object\n",
      "LotShape         1459 non-null object\n",
      "LandContour      1459 non-null object\n",
      "Utilities        1457 non-null object\n",
      "LotConfig        1459 non-null object\n",
      "LandSlope        1459 non-null object\n",
      "Neighborhood     1459 non-null object\n",
      "Condition1       1459 non-null object\n",
      "Condition2       1459 non-null object\n",
      "BldgType         1459 non-null object\n",
      "HouseStyle       1459 non-null object\n",
      "OverallQual      1459 non-null int64\n",
      "OverallCond      1459 non-null int64\n",
      "YearBuilt        1459 non-null int64\n",
      "YearRemodAdd     1459 non-null int64\n",
      "RoofStyle        1459 non-null object\n",
      "RoofMatl         1459 non-null object\n",
      "Exterior1st      1458 non-null object\n",
      "Exterior2nd      1458 non-null object\n",
      "MasVnrType       1443 non-null object\n",
      "MasVnrArea       1444 non-null float64\n",
      "ExterQual        1459 non-null object\n",
      "ExterCond        1459 non-null object\n",
      "Foundation       1459 non-null object\n",
      "BsmtQual         1415 non-null object\n",
      "BsmtCond         1414 non-null object\n",
      "BsmtExposure     1415 non-null object\n",
      "BsmtFinType1     1417 non-null object\n",
      "BsmtFinSF1       1458 non-null float64\n",
      "BsmtFinType2     1417 non-null object\n",
      "BsmtFinSF2       1458 non-null float64\n",
      "BsmtUnfSF        1458 non-null float64\n",
      "TotalBsmtSF      1458 non-null float64\n",
      "Heating          1459 non-null object\n",
      "HeatingQC        1459 non-null object\n",
      "CentralAir       1459 non-null object\n",
      "Electrical       1459 non-null object\n",
      "1stFlrSF         1459 non-null int64\n",
      "2ndFlrSF         1459 non-null int64\n",
      "LowQualFinSF     1459 non-null int64\n",
      "GrLivArea        1459 non-null int64\n",
      "BsmtFullBath     1457 non-null float64\n",
      "BsmtHalfBath     1457 non-null float64\n",
      "FullBath         1459 non-null int64\n",
      "HalfBath         1459 non-null int64\n",
      "BedroomAbvGr     1459 non-null int64\n",
      "KitchenAbvGr     1459 non-null int64\n",
      "KitchenQual      1458 non-null object\n",
      "TotRmsAbvGrd     1459 non-null int64\n",
      "Functional       1457 non-null object\n",
      "Fireplaces       1459 non-null int64\n",
      "FireplaceQu      729 non-null object\n",
      "GarageType       1383 non-null object\n",
      "GarageYrBlt      1381 non-null float64\n",
      "GarageFinish     1381 non-null object\n",
      "GarageCars       1458 non-null float64\n",
      "GarageArea       1458 non-null float64\n",
      "GarageQual       1381 non-null object\n",
      "GarageCond       1381 non-null object\n",
      "PavedDrive       1459 non-null object\n",
      "WoodDeckSF       1459 non-null int64\n",
      "OpenPorchSF      1459 non-null int64\n",
      "EnclosedPorch    1459 non-null int64\n",
      "3SsnPorch        1459 non-null int64\n",
      "ScreenPorch      1459 non-null int64\n",
      "PoolArea         1459 non-null int64\n",
      "PoolQC           3 non-null object\n",
      "Fence            290 non-null object\n",
      "MiscFeature      51 non-null object\n",
      "MiscVal          1459 non-null int64\n",
      "MoSold           1459 non-null int64\n",
      "YrSold           1459 non-null int64\n",
      "SaleType         1458 non-null object\n",
      "SaleCondition    1459 non-null object\n",
      "dtypes: float64(11), int64(26), object(43)\n",
      "memory usage: 912.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Data Manipulation and Visualization**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets check for NaN (null) values in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                  0\n",
       "MSSubClass          0\n",
       "MSZoning            0\n",
       "LotFrontage       259\n",
       "LotArea             0\n",
       "Street              0\n",
       "Alley            1369\n",
       "LotShape            0\n",
       "LandContour         0\n",
       "Utilities           0\n",
       "LotConfig           0\n",
       "LandSlope           0\n",
       "Neighborhood        0\n",
       "Condition1          0\n",
       "Condition2          0\n",
       "BldgType            0\n",
       "HouseStyle          0\n",
       "OverallQual         0\n",
       "OverallCond         0\n",
       "YearBuilt           0\n",
       "YearRemodAdd        0\n",
       "RoofStyle           0\n",
       "RoofMatl            0\n",
       "Exterior1st         0\n",
       "Exterior2nd         0\n",
       "MasVnrType          8\n",
       "MasVnrArea          8\n",
       "ExterQual           0\n",
       "ExterCond           0\n",
       "Foundation          0\n",
       "                 ... \n",
       "BedroomAbvGr        0\n",
       "KitchenAbvGr        0\n",
       "KitchenQual         0\n",
       "TotRmsAbvGrd        0\n",
       "Functional          0\n",
       "Fireplaces          0\n",
       "FireplaceQu       690\n",
       "GarageType         81\n",
       "GarageYrBlt        81\n",
       "GarageFinish       81\n",
       "GarageCars          0\n",
       "GarageArea          0\n",
       "GarageQual         81\n",
       "GarageCond         81\n",
       "PavedDrive          0\n",
       "WoodDeckSF          0\n",
       "OpenPorchSF         0\n",
       "EnclosedPorch       0\n",
       "3SsnPorch           0\n",
       "ScreenPorch         0\n",
       "PoolArea            0\n",
       "PoolQC           1453\n",
       "Fence            1179\n",
       "MiscFeature      1406\n",
       "MiscVal             0\n",
       "MoSold              0\n",
       "YrSold              0\n",
       "SaleType            0\n",
       "SaleCondition       0\n",
       "SalePrice           0\n",
       "Length: 81, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                  0\n",
       "MSSubClass          0\n",
       "MSZoning            4\n",
       "LotFrontage       227\n",
       "LotArea             0\n",
       "Street              0\n",
       "Alley            1352\n",
       "LotShape            0\n",
       "LandContour         0\n",
       "Utilities           2\n",
       "LotConfig           0\n",
       "LandSlope           0\n",
       "Neighborhood        0\n",
       "Condition1          0\n",
       "Condition2          0\n",
       "BldgType            0\n",
       "HouseStyle          0\n",
       "OverallQual         0\n",
       "OverallCond         0\n",
       "YearBuilt           0\n",
       "YearRemodAdd        0\n",
       "RoofStyle           0\n",
       "RoofMatl            0\n",
       "Exterior1st         1\n",
       "Exterior2nd         1\n",
       "MasVnrType         16\n",
       "MasVnrArea         15\n",
       "ExterQual           0\n",
       "ExterCond           0\n",
       "Foundation          0\n",
       "                 ... \n",
       "HalfBath            0\n",
       "BedroomAbvGr        0\n",
       "KitchenAbvGr        0\n",
       "KitchenQual         1\n",
       "TotRmsAbvGrd        0\n",
       "Functional          2\n",
       "Fireplaces          0\n",
       "FireplaceQu       730\n",
       "GarageType         76\n",
       "GarageYrBlt        78\n",
       "GarageFinish       78\n",
       "GarageCars          1\n",
       "GarageArea          1\n",
       "GarageQual         78\n",
       "GarageCond         78\n",
       "PavedDrive          0\n",
       "WoodDeckSF          0\n",
       "OpenPorchSF         0\n",
       "EnclosedPorch       0\n",
       "3SsnPorch           0\n",
       "ScreenPorch         0\n",
       "PoolArea            0\n",
       "PoolQC           1456\n",
       "Fence            1169\n",
       "MiscFeature      1408\n",
       "MiscVal             0\n",
       "MoSold              0\n",
       "YrSold              0\n",
       "SaleType            1\n",
       "SaleCondition       0\n",
       "Length: 80, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets check for the mean, standard deviation for Sales price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['SalePrice'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sales price is right skewed. So, we perform log transformation so that the skewness is nearly zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skew is: 1.88287575977\n"
     ]
    },
    {
     "data": {
      "image/png": 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J9OpNT+Abn3RsPFKXpIJY6pJUkK5Pv0TER4FnA4eBt2Xm/m7vQ5I0ua4eqUfE84FnZOZz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\nWOqSVBBLXZIKYqlLUkH+Dwh6vdCoBfGTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7a9deb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skew is: 0.121346619897\n"
     ]
    },
    {
     "data": {
      "image/png": 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o3Q4437GxWLMGNmksOhHqkqTRdHn6RZK0gqEuSYUY6pJUiKEuSYUY6pJUiKEuSYUY6pJU\nyP8Dgru4HoRgipcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3eb0764f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Determining the Skewness of data \n",
    "print (\"Skew is:\", train.SalePrice.skew())\n",
    "\n",
    "plt.hist(train.SalePrice)\n",
    "plt.show()\n",
    "\n",
    "# After log transformation of the data it looks much more center aligned\n",
    "train['Skewed_SP'] = np.log(train['SalePrice']+1)\n",
    "print (\"Skew is:\", train['Skewed_SP'].skew())\n",
    "plt.hist(train['Skewed_SP'], color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20     536\n",
       "60     299\n",
       "50     144\n",
       "120     87\n",
       "30      69\n",
       "160     63\n",
       "70      60\n",
       "80      58\n",
       "90      52\n",
       "190     30\n",
       "85      20\n",
       "75      16\n",
       "45      12\n",
       "180     10\n",
       "40       4\n",
       "Name: MSSubClass, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JkrQVmhkC/hywG7AMuAU4NzPvaLK9pcClEfEhiiHg7YEHmtyGJEmStkIzs4C3pxjCfRh4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\nsL8mnroAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7a9de9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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B79fM1JFtIvDDJtNNPZipD9gZ+EXz9Gcy8+tdzNIHfA24r9l1L3A+vXGe5gGnAWuBM4Af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\n2C1Um7ln9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7a9de8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('MSSubClass', 'Skewed_SP', data=train,kind='bar',size=3,aspect=3)\n",
    "fig, (axis1) = plt.subplots(1,1,figsize=(10,3))\n",
    "sns.countplot('MSSubClass', data=train)\n",
    "train['MSSubClass'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MSSubClass = 60 has highest SalePrice while the sales of houses with MSSubClass = 20 is the highest. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RL         1151\n",
       "RM          218\n",
       "FV           65\n",
       "RH           16\n",
       "C (all)      10\n",
       "Name: MSZoning, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e78686588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NlzDjvWbRgU2XoIbZcyZJklQQw5kkSVJBDGeSJEkFMZxJkiQVpOgLAiLiA8CzgWHg+Mxc\n0XBJkiRJHVVsz1lEPBfYPTP3Bl4NfKjhkiRJkjqu5J6z/YEvAGTmLRExEBGPyszfN1yXpC2w4sQ3\nN13CjPfMc/xbVn/qluXnNF1CT/iLvU7c4mMU23MGzAWGWtaH6jZJkqQZq294eLjpGsYUEYuBL2bm\n5fX6DcAxmfmTZiuTJEnqnJJ7zlby0J6yXYA7G6pFkiSpK0oOZ18GDgGIiGcAKzNzbbMlSZIkdVax\npzUBIuIs4G+BTcAbMvPmhkuSJEnqqKLDmSRJUq8p+bSmJElSzzGcSZIkFaTkm9D2rIjYDfhf4Nt1\n0zb1+uuAnwF7ZOa6ZqrTeCZ4324FPpqZZ7XsfzZwaGbu1t1Ke1tE7A58EBgEZgHfAN6amRtG7bc9\nsBRYmJmrxzjOacBvgB8AbwTeA5ycmYd19AVoXGP8DALcA1yQmZ9r2e84qt+jb+puhRrPw/nci4gF\nwBsz85DuV9od9pyVKzNzQf21N7A1cHjTRWlC471vdwEvHdkpIvqAv2moxp4VEbOAzwHvy8xn8eB7\ncMoYu58GfGysYDaWzPwOcGdEzNgPjGmi9WdwAfBvwOjAfBjwya5Xpon4uVcznE0fy4Hdmy5CD9vI\n+7YB+E1EPLVunw/c0lhVvet5wI8z82sAmTkMvA14V+tOEfFIqlv5fKZePzEivhkRyyPi1M0c/zxg\nUUcq12RdBewdEbMBIuKxwBMy85vNlqU29OznnuFsGoiIrah6Xb7TdC1q3xjv26U8+FfgK4DPN1FX\nj3sK8L3Whsy8Z/QpTeBZwPcz8/6Wtn2AZwNHRcSjxjp4Zt4KPHEkCKh5mXkfcCVwUN20EPhscxWp\nHWP8/rwyIq6LiOuohiXMaIazckXLf8RfA9dm5hcarkkT29z7djnwsvrU2gLgukYq7G3DVOPMJrIL\ncEfL+nrga8C1wGOAHTfz2LtwHuAmPfAzWH+9g+oU5svr7YfiKc1Sbe735wtbTlXP+N5pLwgoV9b/\nCYmISwHnFJ0exn3fMvPuiLgNOAG4KTM3RkQzVfauH1MN3n9ARGwD7J6ZPxi173C9fVfgLcBfZ+a6\niBi9n8rywM/giHqM58ci4snADpn5w0Yq00T83KvZczY9nASc5amSaWes9+2zwNupBqWr+74C7BoR\nLwGIiEcA7+XBXpURK4HH18uPAVbVwewZwK5UA5XH8ziq3jMVoh5beDnwr8CnGy5H7enpzz3D2TSQ\nmbdRfZi/s266sqXL/tgGS9NmjPG+AXwB2Agsa6SoHpeZm4AXAMdGxLeAG4A1wOhB/v8DPL0+Bf09\nYF1E3EgV4j5KdQXgn4iIPwPuyMz1HXoJmrxPAn8P/FfThWhi4/z+7BlO3yRJY4iI9wPLM7PtnpaI\n+ADwzcz8TOcqkzTT2XMmSWM7laqHbaCdnSPir4DHG8wkbSl7ziRJkgpiz5kkSVJBDGeSJEkFMZxJ\nkiQVxJvQSprWImI34DbgdZn5kZb2fYDrgf2o7vB/JtUfpNsAvweOzcxfRsQ5wJ6thwQuy8zXT6KW\nDwJLMvPbk3w5kmQ4kzQj/BQ4GvhIS9vRQNbLnwAOy8zvAkTEm6hmajghM08ceUBEPJFqmqb3TKaI\nzJzx08pI6jzDmaSZYCXwyIh4Wmb+sL6r+L7ATfX2HYEHJivPzPNGH6CeLeBi4O2ZeUfd9mLgFKqe\nt/VUvW2/iohfAOcCLwSeBLw2M6+p5wQ8g+pGwydTzc/5NOA+4MDMXF/P9XgY1dyB3wN2ycwjp/Df\nQtI055gzSTPFEuCYenkh8CVgU72+CFgaETdGxBn1NEyjnQjcmZmfAqgD3gXAwszcD7iSKniNuCcz\nn1+3vXmM4+0N/HNm7g3cD7wgInYHXltvezHw7Em/WkkzluFM0kzxaeCwiOgHjgI+PrIhM5cA84Cz\ngTnAVRFx5sj2iPhL4DigdZzZnwO/HulFA64Dntmy/br6++1UPXOj3ZKZq0bt83RgRWauz8z7qOZ7\nlKSHMJxJmhEy8zfAd4BXAztn5rdGtkXE7Mxcl5lfyMzjgfnAG+pt21D1uh2bmXe3HHL0Hbr7RrVt\nHLVttI2j1vuofuduamm7f8IXJqnnGM4kzSRLqAbzt05u/RTgJxGxc0vbk4Fb6+UzgWWZ+dVRx/oJ\n8Nj6IgGAA3hwDNtk/RjYMyK2rnv4Dt7C40magbwgQNJMcgWwmOrqzBE/phpP9vmI2EDVc/VH4PCI\nmEs1Hm15PZh/xF2Z+YqIeDXw6fpx66h65SYtM78fEZcD3wJ+CdwMtDV3p6Te4dyaktQlLePhlmTm\nhoj4ENVFCGdu/pGSeomnNSWpSzJzI/BEqp66r9fL5zdblaTS2HMmSZJUEHvOJEmSCmI4kyRJKojh\nTJIkqSCGM0mSpIIYziRJkgpiOJMkSSrI/wOwsNGIW7K4XwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e78686b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('MSZoning', 'Skewed_SP', data=train,kind='bar',size=3,aspect=3)\n",
    "fig, (axis1) = plt.subplots(1,1,figsize=(10,3))\n",
    "sns.countplot(x='MSZoning', data=train, ax=axis1)\n",
    "train['MSZoning'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f3e7a9d1f28>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dXVOZMW9/AGbM24/OrqktjkiSJEn1nNoqqS3N2mc+s/aZ3+owJEmSNATPSEqS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EAAAT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\nWEhKkiRJkiqxkJQkSZIkVfL/ASvibgze1DpxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3eb0764da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='MSZoning', y='SalePrice', col='MSSubClass', data=train, kind='bar', col_wrap=4, aspect=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets analyze the numeric features using the numpy library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                 int64\n",
       "MSSubClass         int64\n",
       "LotFrontage      float64\n",
       "LotArea            int64\n",
       "OverallQual        int64\n",
       "OverallCond        int64\n",
       "YearBuilt          int64\n",
       "YearRemodAdd       int64\n",
       "MasVnrArea       float64\n",
       "BsmtFinSF1         int64\n",
       "BsmtFinSF2         int64\n",
       "BsmtUnfSF          int64\n",
       "TotalBsmtSF        int64\n",
       "1stFlrSF           int64\n",
       "2ndFlrSF           int64\n",
       "LowQualFinSF       int64\n",
       "GrLivArea          int64\n",
       "BsmtFullBath       int64\n",
       "BsmtHalfBath       int64\n",
       "FullBath           int64\n",
       "HalfBath           int64\n",
       "BedroomAbvGr       int64\n",
       "KitchenAbvGr       int64\n",
       "TotRmsAbvGrd       int64\n",
       "Fireplaces         int64\n",
       "GarageYrBlt      float64\n",
       "GarageCars         int64\n",
       "GarageArea         int64\n",
       "WoodDeckSF         int64\n",
       "OpenPorchSF        int64\n",
       "EnclosedPorch      int64\n",
       "3SsnPorch          int64\n",
       "ScreenPorch        int64\n",
       "PoolArea           int64\n",
       "MiscVal            int64\n",
       "MoSold             int64\n",
       "YrSold             int64\n",
       "SalePrice          int64\n",
       "Skewed_SP        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_features = train.select_dtypes(include=[np.number])\n",
    "numerical_features.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SalePrice        1.000000\n",
      "Skewed_SP        0.948374\n",
      "OverallQual      0.790982\n",
      "GrLivArea        0.708624\n",
      "GarageCars       0.640409\n",
      "GarageArea       0.623431\n",
      "TotalBsmtSF      0.613581\n",
      "1stFlrSF         0.605852\n",
      "FullBath         0.560664\n",
      "TotRmsAbvGrd     0.533723\n",
      "YearBuilt        0.522897\n",
      "YearRemodAdd     0.507101\n",
      "GarageYrBlt      0.486362\n",
      "MasVnrArea       0.477493\n",
      "Fireplaces       0.466929\n",
      "BsmtFinSF1       0.386420\n",
      "LotFrontage      0.351799\n",
      "WoodDeckSF       0.324413\n",
      "2ndFlrSF         0.319334\n",
      "OpenPorchSF      0.315856\n",
      "HalfBath         0.284108\n",
      "LotArea          0.263843\n",
      "BsmtFullBath     0.227122\n",
      "BsmtUnfSF        0.214479\n",
      "BedroomAbvGr     0.168213\n",
      "ScreenPorch      0.111447\n",
      "PoolArea         0.092404\n",
      "MoSold           0.046432\n",
      "3SsnPorch        0.044584\n",
      "BsmtFinSF2      -0.011378\n",
      "BsmtHalfBath    -0.016844\n",
      "MiscVal         -0.021190\n",
      "Id              -0.021917\n",
      "LowQualFinSF    -0.025606\n",
      "YrSold          -0.028923\n",
      "OverallCond     -0.077856\n",
      "MSSubClass      -0.084284\n",
      "EnclosedPorch   -0.128578\n",
      "KitchenAbvGr    -0.135907\n",
      "Name: SalePrice, dtype: float64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Then we will try to find the corretation between the feature and target\n",
    "corr = numerical_features.corr()\n",
    "#print (corr['SalePrice'].sort_values(ascending=False)[:5], '\\n')\n",
    "#print (corr['SalePrice'].sort_values(ascending=False)[-5:])\n",
    "print (corr['SalePrice'].sort_values(ascending=False)[:], '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will analyze the features in their descending of correlation with sales price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7,  6,  8,  5,  9,  4, 10,  3,  1,  2])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.OverallQual.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Creating a pivot table \n",
    "quality_pivot = train.pivot_table(index='OverallQual',values='SalePrice', aggfunc=np.median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>SalePrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>108000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>133000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>200141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>269750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>345000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>432390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             SalePrice\n",
       "OverallQual           \n",
       "1                50150\n",
       "2                60000\n",
       "3                86250\n",
       "4               108000\n",
       "5               133000\n",
       "6               160000\n",
       "7               200141\n",
       "8               269750\n",
       "9               345000\n",
       "10              432390"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quality_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4r/x9mfkSQETcCYwDtgMuKdedClwYEcOAMZl5X49tbA+sA1xb3g2yKyKeAjYBHu7j9UmS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\nkqTKLBNJUmWWiSSpsv8P/aMx4zU6WmsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e77d24710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quality_pivot.plot(kind='bar',color='blue')\n",
    "plt.xlabel('Overall Quality')\n",
    "plt.ylabel('Median')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SalePrice varies directly with the Overall quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3e777d0fd0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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20JMhk1YZ7o/3M4cn3MnZCorsYNluFpHtuJk+EhI78mkeefp0oKAa/uL7E9Bn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\niuLx/79ZVTnnUHZGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e778b3eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='GrLivArea',y='Skewed_SP',data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SalePrice increases as the GrLivArea increases.  We will also get rid of the outliers which severely affect the prediction of the survival rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3e77753630>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P85d+ajgQphvsTfPsy6OUqyYdCZU5047MvAL3fXSmNX7m2l389+fOIEkSaUUm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\noiiKx/8DqCdGW8+FtsQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7770e358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Removing outliers\n",
    "train = train[train['GrLivArea'] < 4000]\n",
    "sns.regplot(x='GrLivArea',y='Skewed_SP',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3e77681080>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a5NIm2YxBLm3w4Qdva+gX/YNLC/z5ty8F+/eqEdbNiMRQJNgRaCU5MTDQx7Hh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\naK0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e77695470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='GarageArea',y='Skewed_SP',data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GarageArea and SalePrice are directly proportional. \n",
    "\n",
    "We will again get rid of the outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3e77636cc0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vrwcnBpOe98tVA1fsSXsgE/eosLpX9UtrZcwuFvCz778BMUnAJVdvBGch6+b3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\nlnWdxZ4i7O6yjXpmTyaZT2YKbPa9sXDZHLhaMqKL+ckvyv/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e775f9128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Removing outliers\n",
    "train = train[train['GarageArea'] < 1200]\n",
    "sns.regplot(x='GarageArea',y='Skewed_SP',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Null Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PoolQC</th>\n",
       "      <td>1447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature</th>\n",
       "      <td>1399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>1361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>1172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <td>689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofStyle</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofMatl</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterQual</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Null Count\n",
       "Feature                 \n",
       "PoolQC              1447\n",
       "MiscFeature         1399\n",
       "Alley               1361\n",
       "Fence               1172\n",
       "FireplaceQu          689\n",
       "LotFrontage          258\n",
       "GarageQual            81\n",
       "GarageCond            81\n",
       "GarageFinish          81\n",
       "GarageType            81\n",
       "GarageYrBlt           81\n",
       "BsmtExposure          38\n",
       "BsmtFinType2          38\n",
       "BsmtQual              37\n",
       "BsmtCond              37\n",
       "BsmtFinType1          37\n",
       "MasVnrArea             8\n",
       "MasVnrType             8\n",
       "Electrical             1\n",
       "RoofStyle              0\n",
       "RoofMatl               0\n",
       "ExterQual              0\n",
       "Exterior1st            0\n",
       "Exterior2nd            0\n",
       "YearBuilt              0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removing the null values\n",
    "nulls = pd.DataFrame(train.isnull().sum().sort_values(ascending=False)[:25])\n",
    "nulls.columns = ['Null Count']\n",
    "nulls.index.name = 'Feature'\n",
    "nulls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values are: [nan 'Shed' 'Gar2' 'Othr' 'TenC']\n"
     ]
    }
   ],
   "source": [
    "# Pool null value refers to no pool area\n",
    "print (\"Unique values are:\", train.MiscFeature.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>MSZoning</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>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>...</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1452</td>\n",
       "      <td>1452</td>\n",
       "      <td>91</td>\n",
       "      <td>1452</td>\n",
       "      <td>1452</td>\n",
       "      <td>1452</td>\n",
       "      <td>1452</td>\n",
       "      <td>1452</td>\n",
       "      <td>1452</td>\n",
       "      <td>1452</td>\n",
       "      <td>...</td>\n",
       "      <td>1371</td>\n",
       "      <td>1371</td>\n",
       "      <td>1371</td>\n",
       "      <td>1371</td>\n",
       "      <td>1452</td>\n",
       "      <td>5</td>\n",
       "      <td>280</td>\n",
       "      <td>53</td>\n",
       "      <td>1452</td>\n",
       "      <td>1452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>Unf</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>Fa</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1144</td>\n",
       "      <td>1447</td>\n",
       "      <td>50</td>\n",
       "      <td>921</td>\n",
       "      <td>1307</td>\n",
       "      <td>1451</td>\n",
       "      <td>1047</td>\n",
       "      <td>1375</td>\n",
       "      <td>225</td>\n",
       "      <td>1255</td>\n",
       "      <td>...</td>\n",
       "      <td>865</td>\n",
       "      <td>605</td>\n",
       "      <td>1303</td>\n",
       "      <td>1318</td>\n",
       "      <td>1332</td>\n",
       "      <td>2</td>\n",
       "      <td>156</td>\n",
       "      <td>48</td>\n",
       "      <td>1264</td>\n",
       "      <td>1195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MSZoning Street Alley LotShape LandContour Utilities LotConfig  \\\n",
       "count      1452   1452    91     1452        1452      1452      1452   \n",
       "unique        5      2     2        4           4         2         5   \n",
       "top          RL   Pave  Grvl      Reg         Lvl    AllPub    Inside   \n",
       "freq       1144   1447    50      921        1307      1451      1047   \n",
       "\n",
       "       LandSlope Neighborhood Condition1      ...      GarageType  \\\n",
       "count       1452         1452       1452      ...            1371   \n",
       "unique         3           25          9      ...               6   \n",
       "top          Gtl        NAmes       Norm      ...          Attchd   \n",
       "freq        1375          225       1255      ...             865   \n",
       "\n",
       "       GarageFinish GarageQual GarageCond PavedDrive PoolQC  Fence  \\\n",
       "count          1371       1371       1371       1452      5    280   \n",
       "unique            3          5          5          3      3      4   \n",
       "top             Unf         TA         TA          Y     Fa  MnPrv   \n",
       "freq            605       1303       1318       1332      2    156   \n",
       "\n",
       "       MiscFeature SaleType SaleCondition  \n",
       "count           53     1452          1452  \n",
       "unique           4        9             6  \n",
       "top           Shed       WD        Normal  \n",
       "freq            48     1264          1195  \n",
       "\n",
       "[4 rows x 43 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Analysing the non numeric data \n",
    "categoricals = train.select_dtypes(exclude=[np.number])\n",
    "categoricals.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3e775f9320>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U9+/8nHDqrkvF7F93v9nC6bkCQ5N84e4oreI/BWxgEXJ6I3C0Z5RZ8Ix6NlVx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\nQrSI/w9L0DB2civ6LgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e775ed4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Neighborhood'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f3e778de9e8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EvCczP39XgkXE2sBngXWBhcBbMvNbd+V7SZIkSdKwmUmJ4N7ALpn5L8DOwMF3I94LgMzM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\naUwssCRJkiRpTP4BnLGhwYTlx84AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7771d048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='Neighborhood', y='Skewed_SP', data=train, kind='bar', aspect=3)\n",
    "g.set_xticklabels(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Norm      1255\n",
       "Feedr       80\n",
       "Artery      48\n",
       "RRAn        26\n",
       "PosN        17\n",
       "RRAe        11\n",
       "PosA         8\n",
       "RRNn         5\n",
       "RRNe         2\n",
       "Name: Condition1, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Condition1'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Norm      1439\n",
       "Feedr        6\n",
       "Artery       2\n",
       "RRNn         2\n",
       "RRAe         1\n",
       "RRAn         1\n",
       "PosA         1\n",
       "Name: Condition2, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Condition2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f3e7770b6d8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pMg0MDvHQUpoDA+X29OKCOZLUhZFp4oDTxCVJWoMNrr0OG21XXBVgo213Z3DtdSY5o96b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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7738cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='Condition1', y='Skewed_SP', col='Condition2', data=train, kind='bar', col_wrap=4, aspect=0.8)\n",
    "g.set_xticklabels(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Normal     1195\n",
       "Partial     121\n",
       "Abnorml     100\n",
       "Family       20\n",
       "Alloca       12\n",
       "AdjLand       4\n",
       "Name: SaleCondition, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['SaleCondition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WD       1264\n",
       "New       118\n",
       "COD        43\n",
       "ConLD       8\n",
       "ConLw       5\n",
       "ConLI       5\n",
       "CWD         4\n",
       "Oth         3\n",
       "Con         2\n",
       "Name: SaleType, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['SaleType'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f3e76d97828>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LAUESAADGbvnXpl1++WULrojDnWskWZhNR+z9XWh7LwMAHLqGfif51buuuuZr03J1nv/e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\nJElSJxaSkiRJkqRO/n+ol9q8JfM5BQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e76d037f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='SaleCondition', y='Skewed_SP', col='SaleType', data=train, kind='bar', col_wrap=4, aspect=0.8)\n",
    "g.set_xticklabels(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original: \n",
      "\n",
      "Pave    1447\n",
      "Grvl       5\n",
      "Name: Street, dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Data Trasformation \n",
    "print (\"Original: \\n\") \n",
    "print (train.Street.value_counts(), \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Turn into one hot encoding \n",
    "train['enc_street'] = pd.get_dummies(train.Street, drop_first=True)\n",
    "test['enc_street'] = pd.get_dummies(train.Street, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoded: \n",
      "\n",
      "1    1447\n",
      "0       5\n",
      "Name: enc_street, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Encoded \n",
    "print ('Encoded: \\n') \n",
    "print (train.enc_street.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XgZL+If/s7YjYL7/eEvgS6bN9CvCV/PpgSWsCr+XfxWjgjFpUpBK522sDYAjp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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdacf573a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feature Engineering\n",
    "condition_pivot = train.pivot_table(index='SaleCondition',\n",
    "                                    values='SalePrice', aggfunc=np.median)\n",
    "condition_pivot.plot(kind='bar', color='blue')\n",
    "plt.xlabel('Sale Condition')\n",
    "plt.ylabel('Median Sale Price')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def encode(x): return 1 if x == 'Partial' else 0\n",
    "train['enc_condition'] = train.SaleCondition.apply(encode)\n",
    "test['enc_condition'] = test.SaleCondition.apply(encode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NErgUPW/83LZKH38CuDh93OzYTgnDKf3Z/gH4S0TUldiXdQEOE8uT1sZM1hQ9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdace3c3ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "condition_pivot = train.pivot_table(index='enc_condition', values='SalePrice', aggfunc=np.median)\n",
    "condition_pivot.plot(kind='bar', color='blue')\n",
    "plt.xlabel('Encoded Sale Condition')\n",
    "plt.ylabel('Median Sale Price')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Interpolation of data \n",
    "data = train.select_dtypes(include=[np.number]).interpolate().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(data.isnull().sum() != 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Linear Model for the  train and test\n",
    "y = np.log(train.SalePrice)\n",
    "X = data.drop(['SalePrice', 'Id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "                                    X, y, random_state=42, test_size=.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn import ensemble\n",
    "\n",
    "#lr =  ensemble.RandomForestRegressor(n_estimators = 100, oob_score = True, n_jobs = -1,random_state =50,max_features = \"sqrt\", min_samples_leaf = 50)\n",
    "#lr = linear_model.LinearRegression()\n",
    "lr = ensemble.GradientBoostingRegressor()\n",
    "#lr = linear_model.TheilSenRegressor()\n",
    "#lr = linear_model.RANSACRegressor(random_state=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 is: \n",
      " 0.999768628635\n"
     ]
    }
   ],
   "source": [
    "print (\"R^2 is: \\n\", model.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE is: \n",
      " 3.57545004789e-05\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "print ('RMSE is: \\n', mean_squared_error(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fdacaf8e7b8>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yyzXMdCQpKYjIqEmtaHrVVY288UZkYAmKfMycmeCXv+wpXnA1SklBREoutdfBTTfV8/vf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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdacb20c748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "actual_values = y_test\n",
    "plt.scatter(predictions, actual_values, alpha=.75,\n",
    "            color='b') #alpha helps to show overlapping data\n",
    "plt.xlabel('Predicted Price')\n",
    "plt.ylabel('Actual Price')\n",
    "plt.title('Linear Regression Model')\n",
    "#pltrandom_state=None.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gibQqVnTw00+y9JcQJYUvnxRtPB6HAc2ALUjgFMXA7t3QuXPB+zJ//NHKNddc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\nu3nuuSe5775efPPNWl58cSL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PHj3p39+YqvjAAz0zV3Zxs9vtdOnSjaeeep7U1FTefvtN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\nGGNMGixwGmOMMWmwwGmMMcak4f8Mn7CZPqqJhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdace5a1e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PW7Xphg3J1Kp10cMSQhQCQX8raK2TgWtCEIsQIed0wtixNmrVislTUoyKcrF3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdace51a390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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D0KCBj+rVYfx4F3FxVnOt1jhVpDR4KqXKvcDYzYULnaSmhu4H6fNBVpaVjzY52ae9Z9UJ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\nov8HXPa8FGVD4UkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdace419208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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w0ENeSkos/P7EBElnOaysLJPUXAjRNmKlsDsKOFhr/Tfgd8C57VMkIbouvx/y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\n0hhjjInBAqUxxhgTgwVKY4wxJob/Bwlw2YQxMdHDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdace3b98d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Rj8MXvhDjmWdsXn7ZlxKyaNTKMBMgNU0bi5m1TG+tsRjhhJ7er7kRZ22ty+7d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\n5P8Bu5Dp1KqGo/sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdacae9ff28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range (-2, 3):\n",
    "    alpha = 10**i\n",
    "    rm = linear_model.Ridge(alpha=alpha)\n",
    "    ridge_model = rm.fit(X_train, y_train)\n",
    "    preds_ridge = ridge_model.predict(X_test)\n",
    "    \n",
    "    plt.scatter(preds_ridge, actual_values, alpha=.75, color='b')\n",
    "    plt.xlabel('Predicted Price')\n",
    "    plt.ylabel('Actual Price')\n",
    "    plt.title('Ridge Regularization with alpha = {}'.format(alpha))\n",
    "    overlay = 'R^2 is: {}\\nRMSE is: {}'.format(\n",
    "                    ridge_model.score(X_test, y_test),\n",
    "                    mean_squared_error(y_test, preds_ridge))\n",
    "    plt.annotate(s=overlay,xy=(12.1,10.6),size='x-large')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame()\n",
    "submission['Id'] = test.Id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feats = test.select_dtypes(\n",
    "        include=[np.number]).drop(['Id'], axis=1).interpolate()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Number of features of the model must match the input. Model n_features is 39 and input n_features is 38 ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-73-61a78ccc3b44>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeats\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/sklearn/ensemble/gradient_boosting.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m   1850\u001b[0m         \"\"\"\n\u001b[1;32m   1851\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"C\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1852\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1853\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1854\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstaged_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/sklearn/ensemble/gradient_boosting.py\u001b[0m in \u001b[0;36m_decision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m   1122\u001b[0m         \u001b[0;31m# for use in inner loop, not raveling the output in single-class case,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m         \u001b[0;31m# not doing input validation.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1124\u001b[0;31m         \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_decision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1125\u001b[0m         \u001b[0mpredict_stages\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1126\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/sklearn/ensemble/gradient_boosting.py\u001b[0m in \u001b[0;36m_init_decision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m   1112\u001b[0m         \u001b[0;34m\"\"\"Check input and compute prediction of ``init``. \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1113\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1114\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1115\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_features\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1116\u001b[0m             raise ValueError(\"X.shape[1] should be {0:d}, not {1:d}.\".format(\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X, check_input)\u001b[0m\n\u001b[1;32m    374\u001b[0m                              \u001b[0;34m\"match the input. Model n_features is %s and \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m                              \u001b[0;34m\"input n_features is %s \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 376\u001b[0;31m                              % (self.n_features_, n_features))\n\u001b[0m\u001b[1;32m    377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Number of features of the model must match the input. Model n_features is 39 and input n_features is 38 "
     ]
    }
   ],
   "source": [
    "predictions = model.predict(feats)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_predictions = np.exp(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print (\"Original predictions are: \\n\", predictions[:5], \"\\n\")\n",
    "print (\"Final predictions are: \\n\", final_predictions[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission['SalePrice'] = final_predictions\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission.to_csv('submission1.csv', index=False)"
   ]
  }
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