Housing Price Prediction Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from scipy.stats import norm\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train= pd.read_csv(os.getcwd()+'/data/trainproject.csv')\n",
    "test=pd.read_csv(os.getcwd()+'/data/testproject.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>...</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1201.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1452.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>56.897260</td>\n",
       "      <td>70.049958</td>\n",
       "      <td>10516.828082</td>\n",
       "      <td>6.099315</td>\n",
       "      <td>5.575342</td>\n",
       "      <td>1971.267808</td>\n",
       "      <td>1984.865753</td>\n",
       "      <td>103.685262</td>\n",
       "      <td>443.639726</td>\n",
       "      <td>...</td>\n",
       "      <td>94.244521</td>\n",
       "      <td>46.660274</td>\n",
       "      <td>21.954110</td>\n",
       "      <td>3.409589</td>\n",
       "      <td>15.060959</td>\n",
       "      <td>2.758904</td>\n",
       "      <td>43.489041</td>\n",
       "      <td>6.321918</td>\n",
       "      <td>2007.815753</td>\n",
       "      <td>180921.195890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>421.610009</td>\n",
       "      <td>42.300571</td>\n",
       "      <td>24.284752</td>\n",
       "      <td>9981.264932</td>\n",
       "      <td>1.382997</td>\n",
       "      <td>1.112799</td>\n",
       "      <td>30.202904</td>\n",
       "      <td>20.645407</td>\n",
       "      <td>181.066207</td>\n",
       "      <td>456.098091</td>\n",
       "      <td>...</td>\n",
       "      <td>125.338794</td>\n",
       "      <td>66.256028</td>\n",
       "      <td>61.119149</td>\n",
       "      <td>29.317331</td>\n",
       "      <td>55.757415</td>\n",
       "      <td>40.177307</td>\n",
       "      <td>496.123024</td>\n",
       "      <td>2.703626</td>\n",
       "      <td>1.328095</td>\n",
       "      <td>79442.502883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>34900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>365.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7553.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1954.000000</td>\n",
       "      <td>1967.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>129975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9478.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>383.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>163000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1095.250000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11601.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>712.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>214000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>215245.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5644.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>857.000000</td>\n",
       "      <td>547.000000</td>\n",
       "      <td>552.000000</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>15500.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id   MSSubClass  LotFrontage        LotArea  OverallQual  \\\n",
       "count  1460.000000  1460.000000  1201.000000    1460.000000  1460.000000   \n",
       "mean    730.500000    56.897260    70.049958   10516.828082     6.099315   \n",
       "std     421.610009    42.300571    24.284752    9981.264932     1.382997   \n",
       "min       1.000000    20.000000    21.000000    1300.000000     1.000000   \n",
       "25%     365.750000    20.000000    59.000000    7553.500000     5.000000   \n",
       "50%     730.500000    50.000000    69.000000    9478.500000     6.000000   \n",
       "75%    1095.250000    70.000000    80.000000   11601.500000     7.000000   \n",
       "max    1460.000000   190.000000   313.000000  215245.000000    10.000000   \n",
       "\n",
       "       OverallCond    YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1  \\\n",
       "count  1460.000000  1460.000000   1460.000000  1452.000000  1460.000000   \n",
       "mean      5.575342  1971.267808   1984.865753   103.685262   443.639726   \n",
       "std       1.112799    30.202904     20.645407   181.066207   456.098091   \n",
       "min       1.000000  1872.000000   1950.000000     0.000000     0.000000   \n",
       "25%       5.000000  1954.000000   1967.000000     0.000000     0.000000   \n",
       "50%       5.000000  1973.000000   1994.000000     0.000000   383.500000   \n",
       "75%       6.000000  2000.000000   2004.000000   166.000000   712.250000   \n",
       "max       9.000000  2010.000000   2010.000000  1600.000000  5644.000000   \n",
       "\n",
       "           ...         WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  \\\n",
       "count      ...        1460.000000  1460.000000    1460.000000  1460.000000   \n",
       "mean       ...          94.244521    46.660274      21.954110     3.409589   \n",
       "std        ...         125.338794    66.256028      61.119149    29.317331   \n",
       "min        ...           0.000000     0.000000       0.000000     0.000000   \n",
       "25%        ...           0.000000     0.000000       0.000000     0.000000   \n",
       "50%        ...           0.000000    25.000000       0.000000     0.000000   \n",
       "75%        ...         168.000000    68.000000       0.000000     0.000000   \n",
       "max        ...         857.000000   547.000000     552.000000   508.000000   \n",
       "\n",
       "       ScreenPorch     PoolArea       MiscVal       MoSold       YrSold  \\\n",
       "count  1460.000000  1460.000000   1460.000000  1460.000000  1460.000000   \n",
       "mean     15.060959     2.758904     43.489041     6.321918  2007.815753   \n",
       "std      55.757415    40.177307    496.123024     2.703626     1.328095   \n",
       "min       0.000000     0.000000      0.000000     1.000000  2006.000000   \n",
       "25%       0.000000     0.000000      0.000000     5.000000  2007.000000   \n",
       "50%       0.000000     0.000000      0.000000     6.000000  2008.000000   \n",
       "75%       0.000000     0.000000      0.000000     8.000000  2009.000000   \n",
       "max     480.000000   738.000000  15500.000000    12.000000  2010.000000   \n",
       "\n",
       "           SalePrice  \n",
       "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",
       "\n",
       "[8 rows x 38 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'LotFrontage', u'Alley', u'MasVnrType', u'MasVnrArea', u'BsmtQual',\n",
       "       u'BsmtCond', u'BsmtExposure', u'BsmtFinType1', u'BsmtFinType2',\n",
       "       u'Electrical', u'FireplaceQu', u'GarageType', u'GarageYrBlt',\n",
       "       u'GarageFinish', u'GarageQual', u'GarageCond', u'PoolQC', u'Fence',\n",
       "       u'MiscFeature'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding which column has missing values\n",
    "train.columns[train.isnull().any()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding how many missing values are their in these columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Electrical      0.000685\n",
       "MasVnrType      0.005479\n",
       "MasVnrArea      0.005479\n",
       "BsmtQual        0.025342\n",
       "BsmtCond        0.025342\n",
       "BsmtFinType1    0.025342\n",
       "BsmtExposure    0.026027\n",
       "BsmtFinType2    0.026027\n",
       "GarageCond      0.055479\n",
       "GarageQual      0.055479\n",
       "GarageFinish    0.055479\n",
       "GarageType      0.055479\n",
       "GarageYrBlt     0.055479\n",
       "LotFrontage     0.177397\n",
       "FireplaceQu     0.472603\n",
       "Fence           0.807534\n",
       "Alley           0.937671\n",
       "MiscFeature     0.963014\n",
       "PoolQC          0.995205\n",
       "dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miss=train.isnull().sum()\n",
    "miss=miss[miss>0]\n",
    "#feature scaling\n",
    "miss=miss/len(train)\n",
    "miss=miss.sort_values()\n",
    "miss\n",
    "\n",
    "#index           Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting missing value array miss to dataframe for visualization purposes\n",
    "miss = miss.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#giving column a name \"count\" and making a column \"Name\" with values of index\n",
    "miss.columns = ['count']\n",
    "miss['Name'] = miss.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>0.000685</td>\n",
       "      <td>Electrical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>0.005479</td>\n",
       "      <td>MasVnrType</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>0.005479</td>\n",
       "      <td>MasVnrArea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>0.025342</td>\n",
       "      <td>BsmtQual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond</th>\n",
       "      <td>0.025342</td>\n",
       "      <td>BsmtCond</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <td>0.025342</td>\n",
       "      <td>BsmtFinType1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure</th>\n",
       "      <td>0.026027</td>\n",
       "      <td>BsmtExposure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <td>0.026027</td>\n",
       "      <td>BsmtFinType2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond</th>\n",
       "      <td>0.055479</td>\n",
       "      <td>GarageCond</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual</th>\n",
       "      <td>0.055479</td>\n",
       "      <td>GarageQual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish</th>\n",
       "      <td>0.055479</td>\n",
       "      <td>GarageFinish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType</th>\n",
       "      <td>0.055479</td>\n",
       "      <td>GarageType</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>0.055479</td>\n",
       "      <td>GarageYrBlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>0.177397</td>\n",
       "      <td>LotFrontage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <td>0.472603</td>\n",
       "      <td>FireplaceQu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>0.807534</td>\n",
       "      <td>Fence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>0.937671</td>\n",
       "      <td>Alley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature</th>\n",
       "      <td>0.963014</td>\n",
       "      <td>MiscFeature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolQC</th>\n",
       "      <td>0.995205</td>\n",
       "      <td>PoolQC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count          Name\n",
       "Electrical    0.000685    Electrical\n",
       "MasVnrType    0.005479    MasVnrType\n",
       "MasVnrArea    0.005479    MasVnrArea\n",
       "BsmtQual      0.025342      BsmtQual\n",
       "BsmtCond      0.025342      BsmtCond\n",
       "BsmtFinType1  0.025342  BsmtFinType1\n",
       "BsmtExposure  0.026027  BsmtExposure\n",
       "BsmtFinType2  0.026027  BsmtFinType2\n",
       "GarageCond    0.055479    GarageCond\n",
       "GarageQual    0.055479    GarageQual\n",
       "GarageFinish  0.055479  GarageFinish\n",
       "GarageType    0.055479    GarageType\n",
       "GarageYrBlt   0.055479   GarageYrBlt\n",
       "LotFrontage   0.177397   LotFrontage\n",
       "FireplaceQu   0.472603   FireplaceQu\n",
       "Fence         0.807534         Fence\n",
       "Alley         0.937671         Alley\n",
       "MiscFeature   0.963014   MiscFeature\n",
       "PoolQC        0.995205        PoolQC"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18],\n",
       " <a list of 19 Text xticklabel objects>)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YYDCOiG0i4oaIuDEiDh3n56+KiOsi4vsRcWlErDX4qkqSJEndmWcwjoiFgZOBbYH1gD0j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\nBQUFBQUFBQUFAPwf7TM6U+72BzoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff4532cedd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(12,8))\n",
    "ax.bar(miss['Name'],miss['count'])\n",
    "plt.xticks(rotation = 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now as you can see PoolQC have the highest number of missing values and MiscFeature have the second highest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff44a811d50>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W8vt9HbzR2p3scIwxUWTHN1KGOtI9yLyy7BitjPvEJYspyQvw7Wdt1GJMqrLE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\nIffLC3PO+p8hVWVgZJSegSB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff44a7dd950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now lets check how target value is distributed\n",
    "sns.distplot(train['SalePrice'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target value is right skewed but for better modelling between target variable and independent variable we need normally distributed data  , for that we take log of the target variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff44abe6290>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qKc7jX+67jLse2cKnvr+Vj62rJzdr4uUm7t3QMMPpZCrqucukth/toWdojA+u\nrNXCYLPY6vpSHr53HXta+3ns9WZt7pEmVNxlQoGxEJv3ddJQUcDy2mKv44jHrr+ghr/86Gr2dwzy\nxNZjOoM1Dai4y4Qee72Z/kCQG9Rrl6h71jfwkdVz2d3az4+2q8CnOo25yxmGRoM8vPkQi6oKWVJd\n6HUcSSFXLa0iFHb8cnc7PjPuuLReB9pTlIq7nOEHW45yYnBEm3DIhK5ZXk3YOZ7e04HfjI+uq1OB\nT0Eq7vIePadGeXjzQa5bUc3CKvXaZWLXraghGHY8v68Tnw9uv2SqhWLFCyru8h7ffO4Ap0aCfOHm\nlWw/2uN1HJlhZ7OD0wcuqCHsHC80dRF2cO/6Bnw+9eBThQ6oyruOnDjFv796lLsum8+KOZohI1Mz\nMz64spb3X1DD9qM9fO7Hb+kgawpRz13e9VdP7iU3y8cffXC511EkTZhZdEYV/OSNFkLhMH9z58Va\ngygFqLgLAM/v6+Cp3R38yQeXU1Oc53UcSTMfuKCWdQ3lfP2pJoJhxzfuukQF3mMq7kLf0Bhf+Oku\nVtQW88C1i72OI2nqs9cvJctnfPXJfYTCjr+/Zy3ZKvCe0TsvfOUXezgxOMrf3HnxpOuGiMTjM9cu\n4c8/spIn327ns//xBqNBLVXgFRX3We6Xb7fzkzda+P1rl7BaS/pKAnz6fYv58q2reHpPB7//79sJ\njGk3Jy9oWGYWe/NYL3/0xE7W1JfyBx9Y6nUcSXOx0yhzsvzcfsk8Nu5s5aZvvMQnLl/I/e9b5GG6\n2Uc991mquXuI+7+/lariHP7lvss0HCMJt2FRJR+/bD7HTg7zv391mPY+rQc/k1TcZ6HdrX3c+51X\nCYYd3/vd9VQX53odSTLUxfVl3HflQk4OjXLbP/6aHc06MW6mqLjPMj99o4Xf/KdXCIYcP/jUepZU\nF3kdSTLc0poifu/aJeRm+7jr26/yw63HcE4nOyWbivss8erhbu555FX++IdvsrahjF/84dWsqS/z\nOpbMEnNK8tj42atpXFjOn/7kLR58bAd9Q2Nex8poOqCaoYZGg+xtG+DF/V08s6eDvW39VBfn8qVb\nV/GJyxfoBBOZceWFOfzb/Rv41ouH+Ltn9vPG0R6+dOuF3Hih9gxIBvPqz6PGxka3bds2T147kwRD\nYf7x+YO09wfo6A/Q3j9CR3+AnlOjOMBncOmCcm5ZM4+7LptPXvZ/HTg9m0WiRBKppWeIn7zRQkf/\nCEuqC/nm3Wu5qE5TceNhZtudc43TtlNxTy9tfcO80NTF9qM97GvvZ3/H4LsnivgMKotyqS3JY05J\n5PvCykIKc/UHmqSeUNjx+pEtSKZiAAALFUlEQVRunt3byfBYiKuWVvLpqxdz9bIqndk6BRX3DDEW\nCvO1X+5jf/sA+zsGae+PTCcrzM1iXmkec0ryqI1+ry7O1YdC0s7waIjRUJh/ffkInQMjlBVkc+Oq\nOVy1rIrGBeXMK8v3OmJKUXFPYx39AV5o6uSFpi5+feAEAyNBfAYLKwtZMaeY5bXF1BTnapxSMsa9\nGxoYCYZ4samLTbvaeHZvJ4MjQQAqC3NoqCxgQUUBDZWFNFQUML88n7ryfOaU5M2640fxFve4/l43\ns5uAbwJ+4DvOub8ad38u8APgUqAbuMs5987Zhk4FsePQo8EwI8EQwZAj7Bw+n3FnYz3lBTkJ7SGP\nhcLsaO7lhaZONjd1sbetH4C5pXnccvFcfGYsqS56z3i5SKbJzfLzoQvn8KEL5xAMhdnbNsC2oyfZ\ntKuN7lOjvLC/i76hVmK7owaU5GdTVpBNeUEOZfnZlBXkUF4Yuf6ZaxfP2hP0pi3uZuYHHgY+CLQA\nW81so3NuT0yz+4Ee59xSM7sb+BpwVzICJ4pzjt6hMZpPDnH05BDHTg5xtPsUW9/poXdolMGRIGOh\nM/+q+fpTTQAU5Pgpys2iOC+L4rxsSvOz+eCqWurKIj2KmuJcivOy8cfsTOOco294jPb+AAc6Btnf\nMcD2oz3saO5leCyEz2BBZSE3XTiH5bXF1Jaody6zU5bfx+r6UlbXl76nOAfDYXpPjdEzNErv0Bi9\nw5HvPUNjHO0+xVvDY8TuF/J3z+6ntjiP+vJ85lcUMKc0L/KZzc3irZY+8rL95GT5yPIZ2f7I9yy/\nj2y/cff6BvKj96ejaYdlzOwK4MvOuRuj178A4Jz7akybp6JttphZFtAOVLspnjwRwzLOOUJhRyj6\nPRh2BEOOUyNB+gNjDASCDASCkYLaN0xrX4C23mHa+gIc7x1mIBB8z/NVF+eSn+2nvCCb4rxsCnP8\n5Gb7yfYbPrN3X+PUSJDBkchzD0Zfq3/cD1XkfYHCnCx8Bo7I2GIwppHP4II5JaxfVMFoMMzSGvXO\nRc5HKOwYCESKfc+pUeor8mnpGebYySFaeobp6A+85zMYj7xsHyV52ZTkZ1Ocl/Xu5ZJox64kP+uM\n+0ujtxXnZZOX7UtoJy2RwzJ1wLGY6y3AhsnaOOeCZtYHVAIn4osbvyd3tfE/ntgZKepn+Z9UUZjD\n3NI86ssLWL+ogoaKAhacHsOryKcgJ+ucpweGneOGlbUc7x2mtXeYzoER+obHGAiMcfpXXEGOn8qi\nXKqLc1laXcTi6sJ3i7mmJYqcP7/PKCvIoawgh0VVhdy7oeE99zvnGAmGGQgEeez1ZgJjkYO5wZAj\nGAozFu0gjoXCrKkvZXg0xMBIkP7hsXc7jL1DozSfHGIgMEbf8NiEf+GPZwY+M3wW2b3qgfct5n/e\nuCJZbwMQX3Gf6FfO+H9NPG0wsweAB6JXR8zs7TheP2GOAjvO/mFVJOGXVBIoZ2IpZ2J5kvO3zv4h\nM5Lzc9Gvc7QgnkbxFPcWYH7M9XqgdZI2LdFhmVLg5Pgncs49AjwCYGbb4vnTwmvKmVjKmVjKmVjp\nkjMe8Rwp2AosM7NFZpYD3A1sHNdmI3Bf9PIdwPNTjbeLiEhyTdtzj46hPwg8RWQq5Hedc7vN7CvA\nNufcRuBfgH8zs4NEeux3JzO0iIhMLa557s65TcCmcbc9FHM5ANx5lq/9yFm294pyJpZyJpZyJla6\n5JyWZ2eoiohI8qTn7HwREZlSwou7mX3XzDpjpzmaWYWZPWNmB6Lfyyd57H3RNgfM7L6J2qRIzpCZ\n7Yx+jT+4PBM57zSz3WYWNrNJj+yb2U1m1mRmB83s8ymc8x0z2xV9P5O64NAkOb9uZvvM7C0z+5mZ\nTbiLSQq8n/Hm9Pr9/Itoxp1m9rSZzZvksV5/3uPNOWOf94RyziX0C7gGWAe8HXPbXwOfj17+PPC1\nCR5XARyOfi+PXi5PdL7zzRm9bzBZueLMuRJYAbwANE7yOD9wCFgM5ABvAqtSLWe03TtAlYfv54eA\nrOjlr03y85kK7+e0OVPk/SyJufyHwLcmeFwqfN6nzRm9b8Y+74n8SnjP3Tn3EmfOcb8d+H708veB\n35jgoTcCzzjnTjrneoBngJsSnS8BOWfURDmdc3udc03TPHQ9cNA5d9g5Nwo8TuTflxTnkXNGTZLz\naefc6bUoXiVyLsd4qfB+xpNzRk2Ssz/maiETnNBICnze48yZtmZqzL3WOdcGEP1eM0GbiZY5qJuB\nbLHiyQmQZ2bbzOxVM/P8F8AkUuH9jJcDnjaz7dGzmL30KeDJCW5PtfdzspyQAu+nmf2lmR0jcpLo\nQxM0SYn3M46ckB6f9zOk0gHVuJYwSBENLnIW273AN8xsideBJpBO7+dVzrl1wM3AZ83sGi9CmNkX\ngSDwHxPdPcFtnryf0+SEFHg/nXNfdM7NJ5LxwQmapMT7GUdOSI/P+xlmqrh3mNlcgOj3zgnaxLPM\nQbLFkxPnXGv0+2Ei48lrZyrgWUiF9zMuMe9nJ/AzIkMgMyp6QO8W4LdcdKB1nJR4P+PImRLvZ4xH\ngY9NcHtKvJ8xJsuZLp/3M8xUcY9dnuA+4OcTtHkK+JCZlUdnqXwoettMmjZnNF9u9HIVcBWwZ3y7\nFBDPshGeM7NCMys+fZnI//uMLihnkc1o/hdwm3NuaJJmnr+f8eRMkfdzWczV24B9EzTz/PMeT840\n+ryfKQlHpR8D2oAxIr+d7yey/O9zwIHo94po20YiOzudfuyngIPRr99N5pHkc80JXAnsIjJbYhdw\nvwc5Pxq9PAJ0AE9F284DNsU89sPAfiKzPL6YijmJzD55M/q126OcB4mM/+6Mfn0rRd/PaXOmyPv5\nEyK/UN4C/hOoG/85il73+vM+bc6Z/rwn8ktnqIqIZKBUOqAqIiIJouIuIpKBVNxFRDKQiruISAZS\ncRcRyUAq7pLWzOyL0ZUnT6/ut2GKtt8zszumeb7vmdmR6HO9YWZXTNLu98zsd843v0iyxLUTk0gq\nihbeW4B1zrmR6EkmOQl46s85535sZh8Cvg2sGfe6Wc65byXgdUSSRsVd0tlc4IRzbgTAOXcCwMwe\nAm4F8oFXgM+4cSd0mNmlwN8CRcAJ4JMuumhcjJeApdH2L0Sf6ypgY/Qs0EHn3N+Y2VLgW0A1EALu\ndM4dMrPPAR8HcoGfOee+lOB/v8ikNCwj6expYL6Z7TezfzKza6O3/6Nz7jLn3EVECvwtsQ8ys2zg\nH4A7nHOXAt8F/nKC57+VyFmJp5U55651zv1/49r9B/Cwc+5iImc0tkV7/cuIrOtyCXCpVwuiyeyk\nnrukLefcYLQH/j7geuCJ6A5JA2b2p0ABkc0gdhM5vfy0FcBFwDNmBpGNOGJ77V83sz8Huoicpn7a\nE+MzRHvwdc65n0UzBaK3f4jIeik7ok2LiBT7l87n3ywSLxV3SWvOuRCRlfpeMLNdwGeIjJE3OueO\nmdmXgbxxDzNgt3NuwoOlRMfcJ7j91AS3TbR07enbv+qc+/Y0/wSRpNCwjKQtM1sxbmW/S4DTOz+d\nMLMiYKLZMU1A9emZMGaWbWYXnksGF9nNp+X0Jg5mlmtmBURWOPxUNANmVmdmk23+IpJw6rlLOisC\n/sEiG0UHiawu+ADQS2Ss/B0iS/W+h3NuNDol8u/NrJTI5+AbRIZvzsUngG+b2VeIrDp4p3PuaTNb\nCWyJDv0MAr/NJHsEiCSaVoUUEclAGpYREclAKu4iIhlIxV1EJAOpuIuIZCAVdxGRDKTiLiKSgVTc\nRUQykIq7iEgG+v8BStBNdqo3fM8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff44ace5e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target=np.log(train['SalePrice'])\n",
    "sns.distplot(target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now target variable is normally distributed therefore we will find correlation of variables with target variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it is little bit difficult to visualize all 80 variables at once therefore we will divide numerical variables and categorical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_values=train.select_dtypes(include=[np.number])\n",
    "cate_values=train.select_dtypes(exclude=[np.number])\n",
    "del numerical_values['Id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#find correlations\n",
    "corr=numerical_values.corr()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff449fdaa10>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SRCyQdA0wPyKWSNoP+GgaDEBfVmwcP1bSF1PbGwBbs2L2f02jpn+ezhYcyoqJ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\nJpPJZDKZzBD4XztGbE7AeurfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff448337950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig.ax=plt.subplots(figsize=(12,8))\n",
    "sns.heatmap(corr,cmap=\"RdYlBu_r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the last row in the graph which shows how these variables are related to target variable(SalesPrice) , saleprice is fully correlated with itself which is obvious , OverallQual(over all quality) is highly correlated with saleprice "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalePrice        1.000000\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",
       "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"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets check out correlation of salesprice with each column\n",
    "corr['SalePrice'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OverallQual is 79% correlated with the target variable and GrLivArea(Living area above the ground) is 70% correlated with target variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Lets find out the median sales price with respect to overallqual , median because there are outliers in data and media is robust to outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#we can easy make these kind of aggregate tables using pandas\n",
    "train['OverallQual'].unique()\n",
    "pivot = train.pivot_table(index='OverallQual', values='SalePrice', aggfunc=np.median)\n",
    "pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff44363dad0>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JqmZoSJKqGRqSpGqGhiSpmqEhSapmaEiSqhkakqRqhoYkqZqhIUmqZmhIkqoZGpKkat57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\naEiSqv1/U3LiRwlb2+IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff4436ee090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivot.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the graph, as the quality increases the price also increases which is obvious at it works that way in real life as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'SalePrice')"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6pKSCpD/zfknSZMg5AAyQ8dEiwRwA0LOs2jAFY2Njbnp6utuXAQAAgD5mZodq2pZHSnsy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\nR4Eb/fxru6e8GrDYkKTA7WGtFFsZiW5m4ST/z0H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Mor9GeeUrl6/e1KERAQCAsmFFvQMa\nBXmtrMa2e8W5ndebqtdouuSVr5xMvXYAAIBaBOodMBVBXrtTL9p9vaIHwnnlKydbrx0AAKCK1JcO\nyAvmWgny2p160e7rTcVrNJ2uO+sE9VV6x7RRrx0AAEwlAvUOmIogr90rzu2+XtED4aH5A/raZ07S\nQH+frOQEVspUAgCAqUTqSwdMxeFA7U69aPf1OnWAUivaWQEHAADMPATqHTLZIC/r5NDpXHFu9/Uk\nAmEAADCzEah3qXavOHfDCjcAAECZOCI6PYZCGBwcjOHh4U4PAwAAACVme11EDDbTl82kAAAAQAER\nqAMAAAAFRKAOAAAAFBCBOgAAAFBABOoAAABAARGoAwAAAAVEoA4AAAAUEIE6AAAAUEAE6gAAAEAB\nEagDAAAABUSgDgAAABRQaQN122fb3mR7s+2lnR4PAAAA0IpSBuq2eyV9U9I5kk6UdJntEzs7KgAA\nAKB5pQzUJZ0maXNE/DIiPpC0QtKnOzwmAAAAoGllDdQHJL1Wc3tL2jaG7atsD9se3r59e9sGBwAA\nAExkv04PYJo4oy3GNUTcIekOSbK93fYr0z0wTInDJP2604PApDGP5cFclgdzWQ7MY7Ed22zHsgbq\nWyQdXXP7KEnbGv1BRBw+rSPClLE9HBGDnR4HJod5LA/msjyYy3JgHsujrKkvayUdb/s42/tLulTS\nAx0eEwAAANC0Uq6oR8Qu238habWkXknfiogXOjwsAAAAoGmlDNQlKSIelvRwp8eBaXFHpweAKcE8\nlgdzWR7MZTkwjyXhiHF7LAEAAAB0WFlz1AEAAICuRqAOAAAAFBCBOjrO9rdsv2H7+Zq2D9t+1PbP\n05+HpO22/Q+2N9t+zvYpNX+zJO3/c9tLOvFcZjrbR9t+3PaLtl+w/cW0nfnsIrZn2X7a9rPpPN6c\nth9n+6l0TlamVbVk+4D09ub0/jk1j3V92r7J9lmdeUaw3Wt7ve0H09vMZRey/SvbG21vsD2ctvH+\nWmIE6iiCb0s6u65tqaSfRMTxkn6S3pakcyQdn/67StI/S8kblaQbJX1S0mmSbqy+WaGtdkn6UkR8\nQtICSVfbPlHMZ7d5X9KiiDhZ0jxJZ9teIOnvJN2WzuNbkq5M+18p6a2I+Jik29J+Suf+Uklzlfx/\n/HbbvW19Jqj6oqQXa24zl93rjIiYV1MnnffXEiNQR8dFxE8lvVnX/GlJd6a/3ylpqKb9O5FYI6nf\n9kclnSXp0Yh4MyLekvSoxgf/mGYR8XpEPJP+/o6SwGBAzGdXSefj/9KblfRfSFok6ftpe/08Vuf3\n+5L+yLbT9hUR8X5EvCxps5LAAG1k+yhJ50r6t/S2xVyWCe+vJUagjqI6IiJel5LgT9JH0vYBSa/V\n9NuStuW1o0PSr8znS3pKzGfXSVMlNkh6Q8l/yH8haUdE7Eq71M7JnvlK739b0qFiHoviG5L+StLu\n9PahYi67VUh6xPY621elbby/llhp66ijtJzRFg3a0QG2D5J0r6RrIuI3yYJcdteMNuazACJiVNI8\n2/2S7pP0iaxu6U/msaBsf0rSGxGxzvbp1eaMrsxld1gYEdtsf0TSo7ZfatCXuSwBVtRRVP+bfkWn\n9OcbafsWSUfX9DtK0rYG7Wgz2xUlQfpdEfGDtJn57FIRsUPSE0r2HPTbri7w1M7JnvlK7z9YSTob\n89h5CyWdb/tXklYoSXn5hpjLrhQR29Kfbyj5AH2aeH8tNQJ1FNUDkqo70ZdIur+m/Yp0N/sCSW+n\nX/WtlrTY9iHpppjFaRvaKM1l/XdJL0bE39fcxXx2EduHpyvpst0n6Uwl+w0el3Rh2q1+Hqvze6Gk\nxyI5Te8BSZemlUSOU7Kp7en2PAtIUkRcHxFHRcQcJZtBH4uIy8Vcdh3bB9r+UPV3Je+Lz4v311Ij\n9QUdZ/tuSadLOsz2FiW70ZdJusf2lZJelXRR2v1hSX+sZCPTe5I+L0kR8abtr0pam/a7JSLqN6hi\n+i2U9GeSNqb5zZL012I+u81HJd2ZVvXokXRPRDxo+2eSVti+VdJ6JR/KlP78D9ublay+XipJEfGC\n7Xsk/UxJRaCr05QadN6XxVx2myMk3ZemEu4n6XsR8WPba8X7a2k5+aAMAAAAoEhIfQEAAAAKiEAd\nAAAAKCACdQAAAKCACNQBAACAAiJQBwAAAAqIQB0ASsz2Eba/Z/uX6bHj/2P7TzL6zbH9fEb7LbbP\nbOI6822H7bOmauwAMNMRqANASaUHUK2S9NOI+J2IOFVJXeyj6vrlnqkREV+JiP9q4nKXSfrv9Gfm\nWGzz3xwAaAFvmgBQXoskfRAR/1JtiIhXIuIfbX/O9n/a/qGkR/IewPa3bV9o+5z0wJtq++np31Y/\nEFwo6XNKTjyclbbPsf2i7dslPSPpaNuL01X9Z9LrH5T2/Yrttbaft31H+pgAMKMRqANAec1VEiDn\n+QNJSyJiUROP9aikBenR5ZJ0iaSV6e8LJb0cEb+Q9ISS0xCrTpD0nYiYL+ldSTdIOjMiTpE0LOkv\n037/FBG/HxG/J6lP0qeaGBMAlBqBOgDMELa/afvZ9MhxSXq02aPDI2KXpB9LOi9NlTlX0v3p3ZdJ\nWpH+vkJj019eiYg16e8LJJ0o6UnbGyQtkXRset8Ztp+yvVHJNwFzW3+GAFAuuXmJAICu94KkC6o3\nIuJq24cpWcmWkhXuVqyUdLWkNyWtjYh3bPem1zjf9t9IsqRDbX8o4xpW8uFgTB57mipzu6TBiHjN\n9k2SZrU4NgAoHVbUAaC8HpM0y/YXatpmT+LxnpB0iqQ/1960lzMlPRsRR0fEnIg4VtK9koYy/n6N\npIW2PyZJtmfb/l3tDcp/neasXziJMQJAaRCoA0BJRUQoCZj/0PbLtp+WdKekL+f8yQm2t9T8u6ju\n8UYlPSjpnPSnlKS53Ff3OPdK+tOM8WxXsuH0btvPKQncPx4ROyT9q6SNSqrUrK3/WwCYiZy8jwMA\nAAAoElbUAQAAgAIiUAcAAAAKiEAdAAAAKCACdQAAAKCACNQBAACAAiJQBwAAAAqIQB0AAAAooP8H\n93ALy+gJ40YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff442de7190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now we will see GrLivArea\n",
    "fig,ax=plt.subplots(figsize=(12,8))\n",
    "ax.scatter(x=train['GrLivArea'], y=train['SalePrice'])\n",
    "\n",
    "ax.set_xlabel('GrLivArea')\n",
    "ax.set_ylabel('SalePrice')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can clearly see the correation between living area and sale price , but there is an outlier value GrLivArea > 5000, these outliers can degrade your model's performance hence we will get rid of it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}