{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## House Prices: Advanced Regression Techniques \n",
    "In this competitions we have 79 explanatory variables describing aspects of residential homes in Ames, Iowa. The goal is to predict the prices of these houses.\n",
    "\n",
    "The metric to calculate the accuracy of predictions is Root Mean Squared Logarithmic Error (it penalizes an under-predicted estimate greater than an over-predicted estimate).\n",
    "\n",
    "The RMSLE is calculated as\n",
    "\n",
    "$$\n",
    "\\epsilon = \\sqrt{\\frac{1}{n} \\sum_{i=1}^n (\\log(p_i + 1) - \\log(a_i+1))^2 }\n",
    "$$\n",
    "\n",
    "Where:\n",
    "\n",
    "\\\\(\\epsilon\\\\) is the RMSLE value (score);\n",
    "\\\\(n\\\\) is the number of observations;\n",
    "\\\\(p_i\\\\) is prediction;\n",
    "\\\\(a_i\\\\) is the actual response for \\\\(i\\\\);\n",
    "\\\\(\\log(x)\\\\) is the natural logarithm of \\\\(x\\\\)\n",
    "\n",
    "At first I explore the data, fill missing values and visualize some features, then I try several models for prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. [Data exploration](#datex)\n",
    "\n",
    "2. [Dealing with missing data](#datmis)\n",
    "    \n",
    "3. [Data visualization](#datvis)\n",
    "    \n",
    "4. [Data preparation](#datprep)\n",
    "\n",
    "5. [Model](#model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import rcParams\n",
    "import xgboost as xgb\n",
    "%matplotlib inline \n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "import scipy.stats as stats\n",
    "from scipy import stats\n",
    "from scipy.stats import pointbiserialr, spearmanr, skew, pearsonr\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV\n",
    "from sklearn.linear_model import Ridge, RidgeCV, LassoCV\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"datex\">Data exploration</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('../input/train.csv')\n",
    "test = pd.read_csv('../input/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    <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",
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       "      <th>SaleCondition</th>\n",
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       "  </thead>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gd</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1151</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1454</td>\n",
       "      <td>50</td>\n",
       "      <td>925</td>\n",
       "      <td>1311</td>\n",
       "      <td>1459</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>157</td>\n",
       "      <td>49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1267</td>\n",
       "      <td>1198</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>mean</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>56.897260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.049958</td>\n",
       "      <td>10516.828082</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.758904</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.489041</td>\n",
       "      <td>6.321918</td>\n",
       "      <td>2007.815753</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180921.195890</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>421.610009</td>\n",
       "      <td>42.300571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.284752</td>\n",
       "      <td>9981.264932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>40.177307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>496.123024</td>\n",
       "      <td>2.703626</td>\n",
       "      <td>1.328095</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79442.502883</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
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       "      <td>20.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34900.000000</td>\n",
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       "      <td>59.000000</td>\n",
       "      <td>7553.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129975.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9478.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163000.000000</td>\n",
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       "    <tr>\n",
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       "      <td>70.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11601.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>215245.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15500.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>755000.000000</td>\n",
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       "<p>11 rows × 81 columns</p>\n",
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      ],
      "text/plain": [
       "                 Id   MSSubClass MSZoning  LotFrontage        LotArea Street  \\\n",
       "count   1460.000000  1460.000000     1460  1201.000000    1460.000000   1460   \n",
       "unique          NaN          NaN        5          NaN            NaN      2   \n",
       "top             NaN          NaN       RL          NaN            NaN   Pave   \n",
       "freq            NaN          NaN     1151          NaN            NaN   1454   \n",
       "mean     730.500000    56.897260      NaN    70.049958   10516.828082    NaN   \n",
       "std      421.610009    42.300571      NaN    24.284752    9981.264932    NaN   \n",
       "min        1.000000    20.000000      NaN    21.000000    1300.000000    NaN   \n",
       "25%      365.750000    20.000000      NaN    59.000000    7553.500000    NaN   \n",
       "50%      730.500000    50.000000      NaN    69.000000    9478.500000    NaN   \n",
       "75%     1095.250000    70.000000      NaN    80.000000   11601.500000    NaN   \n",
       "max     1460.000000   190.000000      NaN   313.000000  215245.000000    NaN   \n",
       "\n",
       "       Alley LotShape LandContour Utilities      ...           PoolArea  \\\n",
       "count     91     1460        1460      1460      ...        1460.000000   \n",
       "unique     2        4           4         2      ...                NaN   \n",
       "top     Grvl      Reg         Lvl    AllPub      ...                NaN   \n",
       "freq      50      925        1311      1459      ...                NaN   \n",
       "mean     NaN      NaN         NaN       NaN      ...           2.758904   \n",
       "std      NaN      NaN         NaN       NaN      ...          40.177307   \n",
       "min      NaN      NaN         NaN       NaN      ...           0.000000   \n",
       "25%      NaN      NaN         NaN       NaN      ...           0.000000   \n",
       "50%      NaN      NaN         NaN       NaN      ...           0.000000   \n",
       "75%      NaN      NaN         NaN       NaN      ...           0.000000   \n",
       "max      NaN      NaN         NaN       NaN      ...         738.000000   \n",
       "\n",
       "       PoolQC  Fence MiscFeature       MiscVal       MoSold       YrSold  \\\n",
       "count       7    281          54   1460.000000  1460.000000  1460.000000   \n",
       "unique      3      4           4           NaN          NaN          NaN   \n",
       "top        Gd  MnPrv        Shed           NaN          NaN          NaN   \n",
       "freq        3    157          49           NaN          NaN          NaN   \n",
       "mean      NaN    NaN         NaN     43.489041     6.321918  2007.815753   \n",
       "std       NaN    NaN         NaN    496.123024     2.703626     1.328095   \n",
       "min       NaN    NaN         NaN      0.000000     1.000000  2006.000000   \n",
       "25%       NaN    NaN         NaN      0.000000     5.000000  2007.000000   \n",
       "50%       NaN    NaN         NaN      0.000000     6.000000  2008.000000   \n",
       "75%       NaN    NaN         NaN      0.000000     8.000000  2009.000000   \n",
       "max       NaN    NaN         NaN  15500.000000    12.000000  2010.000000   \n",
       "\n",
       "        SaleType  SaleCondition      SalePrice  \n",
       "count       1460           1460    1460.000000  \n",
       "unique         9              6            NaN  \n",
       "top           WD         Normal            NaN  \n",
       "freq        1267           1198            NaN  \n",
       "mean         NaN            NaN  180921.195890  \n",
       "std          NaN            NaN   79442.502883  \n",
       "min          NaN            NaN   34900.000000  \n",
       "25%          NaN            NaN  129975.000000  \n",
       "50%          NaN            NaN  163000.000000  \n",
       "75%          NaN            NaN  214000.000000  \n",
       "max          NaN            NaN  755000.000000  \n",
       "\n",
       "[11 rows x 81 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quite a lot of variables. Many categorical variables, which makes analysis more complex. And a lot of missing values. Or are they merely missing values? There are many features for which NaN value simply means an absense of the feature (for example, no Garage)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"datmis\">Dealing with missing data</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At first, lets see which columns have missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 19 columns):\n",
      "LotFrontage     1201 non-null float64\n",
      "Alley           91 non-null object\n",
      "MasVnrType      1452 non-null object\n",
      "MasVnrArea      1452 non-null float64\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",
      "BsmtFinType2    1422 non-null object\n",
      "Electrical      1459 non-null object\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",
      "GarageQual      1379 non-null object\n",
      "GarageCond      1379 non-null object\n",
      "PoolQC          7 non-null object\n",
      "Fence           281 non-null object\n",
      "MiscFeature     54 non-null object\n",
      "dtypes: float64(3), object(16)\n",
      "memory usage: 216.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data[data.columns[data.isnull().sum() > 0].tolist()].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I create lists of columns with missing values in train and test. Then I use list comprehension to get a list with columns which are present in test but not in train. Then I find the columns which are present in train but not in test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1459 entries, 0 to 1458\n",
      "Data columns (total 15 columns):\n",
      "MSZoning        1455 non-null object\n",
      "Utilities       1457 non-null object\n",
      "Exterior1st     1458 non-null object\n",
      "Exterior2nd     1458 non-null object\n",
      "BsmtFinSF1      1458 non-null float64\n",
      "BsmtFinSF2      1458 non-null float64\n",
      "BsmtUnfSF       1458 non-null float64\n",
      "TotalBsmtSF     1458 non-null float64\n",
      "BsmtFullBath    1457 non-null float64\n",
      "BsmtHalfBath    1457 non-null float64\n",
      "KitchenQual     1458 non-null object\n",
      "Functional      1457 non-null object\n",
      "GarageCars      1458 non-null float64\n",
      "GarageArea      1458 non-null float64\n",
      "SaleType        1458 non-null object\n",
      "dtypes: float64(8), object(7)\n",
      "memory usage: 171.1+ KB\n"
     ]
    }
   ],
   "source": [
    "list_data = data.columns[data.isnull().sum() > 0].tolist()\n",
    "list_test = test.columns[test.isnull().sum() > 0].tolist()\n",
    "test[list(i for i in list_test if i not in list_data)].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 1 columns):\n",
      "Electrical    1459 non-null object\n",
      "dtypes: object(1)\n",
      "memory usage: 11.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data[list(i for i in list_data if i not in list_test)].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No value in the following columns probably means absense of it:\n",
    "\n",
    "- Alley;\n",
    "- MasVnrType (Masonry veneer type) and MasVnrArea (its area);\n",
    "- BsmtQual and BsmtCond (two parameters of basement);\n",
    "- BsmtExposure (Walkout or garden level basement walls);\n",
    "- BsmtFinType1 and BsmtFinType2 (Quality of basement finished area) and BsmtFinSF1, BsmtFinSF2 (their area);\n",
    "- BsmtUnfSF (Unfinished square feet of basement area);\n",
    "- FireplaceQu (Fireplace quality);\n",
    "- GarageType, GarageFinish, GarageQual, GarageCond (garage parameters) and GarageYrBlt;\n",
    "- KitchenQual;\n",
    "- PoolQC;\n",
    "- Fence;\n",
    "- MiscFeature (Miscellaneous feature not covered in other categories);\n",
    "\n",
    "\n",
    "For other variables missing values could be replaced with most common parameters:\n",
    "\n",
    "- MSZoning (The general zoning classification);\n",
    "- Utilities;\n",
    "- Exterior1st and Exterior2nd (Exterior covering on house);\n",
    "- KitchenQual;\n",
    "- Functional (Home functionality rating);\n",
    "- SaleType;\n",
    "- Electrical;\n",
    "- LotFrontage;\n",
    "- GarageCars and GarageArea;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create a list of column to fill NA with \"None\" or 0.\n",
    "to_null = ['Alley', 'MasVnrType', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'FireplaceQu',\n",
    "           'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'GarageYrBlt', 'BsmtFullBath', 'BsmtHalfBath',\n",
    "           'PoolQC', 'Fence', 'MiscFeature']\n",
    "for col in to_null:\n",
    "    if data[col].dtype == 'object':\n",
    "\n",
    "        data[col].fillna('None',inplace=True)\n",
    "        test[col].fillna('None',inplace=True)\n",
    "    else:\n",
    "\n",
    "        data[col].fillna(0,inplace=True)\n",
    "        test[col].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Fill NA with common values.\n",
    "test.loc[test.KitchenQual.isnull(), 'KitchenQual'] = 'TA'\n",
    "test.loc[test.MSZoning.isnull(), 'MSZoning'] = 'RL'\n",
    "test.loc[test.Utilities.isnull(), 'Utilities'] = 'AllPub'\n",
    "test.loc[test.Exterior1st.isnull(), 'Exterior1st'] = 'VinylSd'\n",
    "test.loc[test.Exterior2nd.isnull(), 'Exterior2nd'] = 'VinylSd'\n",
    "test.loc[test.Functional.isnull(), 'Functional'] = 'Typ'\n",
    "test.loc[test.SaleType.isnull(), 'SaleType'] = 'WD'\n",
    "data.loc[data['Electrical'].isnull(), 'Electrical'] = 'SBrkr'\n",
    "data.loc[data['LotFrontage'].isnull(), 'LotFrontage'] = data['LotFrontage'].mean()\n",
    "test.loc[test['LotFrontage'].isnull(), 'LotFrontage'] = test['LotFrontage'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several additional cases: when a categorical variable is None, relevant numerical variable should be 0. For example if there is no veneer (MasVnrType is None), MasVnrArea should be 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.loc[data.MasVnrType == 'None', 'MasVnrArea'] = 0\n",
    "test.loc[test.MasVnrType == 'None', 'MasVnrArea'] = 0\n",
    "test.loc[test.BsmtFinType1=='None', 'BsmtFinSF1'] = 0\n",
    "test.loc[test.BsmtFinType2=='None', 'BsmtFinSF2'] = 0\n",
    "test.loc[test.BsmtQual=='None', 'BsmtUnfSF'] = 0\n",
    "test.loc[test.BsmtQual=='None', 'TotalBsmtSF'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>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>1116</th>\n",
       "      <td>2577</td>\n",
       "      <td>70</td>\n",
       "      <td>RM</td>\n",
       "      <td>50.0</td>\n",
       "      <td>9060</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Alloca</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "1116  2577          70       RM         50.0     9060   Pave  None      Reg   \n",
       "\n",
       "     LandContour Utilities      ...       ScreenPorch PoolArea PoolQC  Fence  \\\n",
       "1116         Lvl    AllPub      ...                 0        0   None  MnPrv   \n",
       "\n",
       "     MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "1116        None       0      3    2007        WD         Alloca  \n",
       "\n",
       "[1 rows x 80 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#And there is only one line where GarageCars and GarageArea is null, but it seems that there is no garage.\n",
    "test.loc[test.GarageCars.isnull() == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test.loc[test.GarageCars.isnull(), 'GarageCars'] = 0\n",
    "test.loc[test.GarageArea.isnull(), 'GarageArea'] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"datvis\">Data visualization</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At first I'll look into data correlation, then I'll visualize some data in order to see the impact of certain features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x168ccc99eb8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uOBatoZ/LKPdrHHea9RF4Gd7HkLxDRrld1DHK1Qs0e+wDbGaP/XHDOdXehkth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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x168cccab518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = data.corr()\n",
    "plt.figure(figsize=(12, 12))\n",
    "sns.heatmap(corr, vmax=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that only several pairs of variables have high correlation. But this chart shows data only for pairs of numerical values. I'll calculate correlation for all variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "threshold = 0.8 # Threshold value.\n",
    "def correlation():\n",
    "    for i in data.columns:\n",
    "        for j in data.columns[list(data.columns).index(i) + 1:]: #Ugly, but works. This way there won't be repetitions.\n",
    "            if data[i].dtype != 'object' and data[j].dtype != 'object':\n",
    "                #pearson is used by default for numerical.\n",
    "                if abs(pearsonr(data[i], data[j])[0]) >= threshold:\n",
    "                    yield (pearsonr(data[i], data[j])[0], i, j)\n",
    "            else:\n",
    "                #spearman works for categorical.\n",
    "                if abs(spearmanr(data[i], data[j])[0]) >= threshold:\n",
    "                    yield (spearmanr(data[i], data[j])[0], i, j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:253: RuntimeWarning: The input array could not be properly checked for nan values. nan values will be ignored.\n",
      "  \"values. nan values will be ignored.\", RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(0.85848725676346904, 'Exterior1st', 'Exterior2nd'),\n",
       " (-0.89606878858916439, 'BsmtFinType2', 'BsmtFinSF2'),\n",
       " (0.81952997500503311, 'TotalBsmtSF', '1stFlrSF'),\n",
       " (0.82548937430884295, 'GrLivArea', 'TotRmsAbvGrd'),\n",
       " (0.88247541428146214, 'GarageCars', 'GarageArea'),\n",
       " (-0.99999111097112325, 'PoolArea', 'PoolQC'),\n",
       " (0.9028952966055307, 'MiscFeature', 'MiscVal')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_list = list(correlation())\n",
    "corr_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a list of highly correlated features. They aren't surprising and none of them should be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x168cd121ac8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x168cd121550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#It seems that SalePrice is skewered, so it needs to be transformed.\n",
    "sns.distplot(data['SalePrice'], kde=False, color='c', hist_kws={'alpha': 0.9})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x168cd65dc18>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t27nmmmtYs2YNGzdupLKykqysLG688UbWrVtH7969mT17Nlu2bGHVqlU8+uij\nLFq0iOeff57u3btz//33c/DgQTRN48svv2TDhg0159Nv3LgxHC9LCN+qzjWuPZppbnWM7fFNag+I\nr2Wtxnjo9FvoMRG6RGEJExERYQmT0aNHM2qUGhvVNA2TyURubi5DhgwBYNiwYXz22WcYjUYGDRpE\nXFwccXFx9OjRg8OHD5OTk8N9991X89hVq1Zht9upqqqiR48eAGRmZrJ7927i4uLIzMzEYDDQpUsX\nXC4XhYWFpKamXrKfOTk54Xj5uoqF1xBKkXg/BmtO31/QnHyjY38GX/z0tX7fYD80J0llX5Ni/5SU\nkk+Jc52t9xCX0UaxLZPipBFcsN2A25gARUDRr0DdoosX96O2ZvF+tADh+P8kLGFis6mxYrvdzoMP\nPsicOXNYunRpzc55m81GSUkJdrudpKSkOt9nt9vrtNd+bGJiYp3HHj9+HKvVSkpKSp32kpKSgMIk\nYrulQyQnkju+o1DE3o/v/X9J1/7U6sfF53fU64ezFPI/UHMgee+Bo7j+z4vv5NlAOAFTh5tpa4oj\noMLpzfH9aAk8VRpKTx/A1qHph8j5C6KwTcAXFBQwa9YssrKyuOOOO1i2bFnN10pLS0lOTiYxMZHS\n0tI67UlJSXXaG3pscnIyFovF588QQlyk8pxnBdYmVY3X5WMFVuKVavNg9/HQdkjw55+L6KDDIXJh\n+Zty9uxZpk+fzrx585g4cSIAV111FXv27AFg586dZGRkMGDAAHJycqisrKSkpIQjR47Qu3dvBg8e\nzI4dO2oem56eTmJiIhaLhWPHjqFpGtnZ2WRkZDB48GCys7NrzqV3u90B3ZWIGFKwDbKn0OfX30P2\nlJisyBq0j4bDWx1V2Ze8zXWDpM0gGPCfcNt3cMcPMGipKmsiQRI7Gio4GSJhuTNZvXo1Fy5cYNWq\nVaxatQqA//iP/+CJJ55gxYoVpKWlMWrUKEwmE9OmTSMrKwtN05g7dy5Wq5WpU6cyf/58pk6disVi\nYfny5QAsXryYhx56CJfLRWZmJgMHDgQgIyODyZMn43a7WbhwYThekohWcmxvYE5/WuvCCB2GQrfx\n0H0c2C6LVK+EXnQoOGnQNM3XcToxLxbmG2LhNQQtewoUfQOVZ3E5yjFZWoG1HaSmw43r9OlDQ0th\ns3T436t6Bda2G/w/xmhV4dptPHS9I7wrsCL9fvjoR70z4PXsRzTInlKzMq+0tAybLUG1J/Vq9P8n\n/j53ZNPZ367vAAAUeUlEQVSiaN6KvlWb6TQnRs0NDgc4ywjTCG70cDvg1KdqD8iJd+oWmvTlrrNg\naeb7XkTT6VBwUsJENG9VReqDtZqmgeaAqsLI9SlcnKVQsFVNoPtbgeVR71/iEiQtW+dboTAHflxN\nfNUZMLVX59CHcChYwkQ0b75WJDXU3txUnlPBcWKTChKfK7B6qtVXh57Rv3+ieSjYpqoYxLenwmXD\nFp+grlPTQxYoEiaieTNa1Al8Neeca2AwNe+qtKXH1f6PE5vg9E7fZ7i3GeSZQB8Pra9W74GEyUWM\n+K7cHONDoL7ocHywhIlo3iyt6w9paS7V3pycP+SpgbUJCr+u/3WDEdpnQrcJsgIrUGYbOEt8t7c0\nOqzmkjARzVvFqca1RwvNDee+8gTI21DiY9u40QqdRqq7j653xN4phOFmuxzOf0fdQioG1d7S6HB8\nsISJaDpPeYaaE+SaWJ4hKL6q9TbUHkluB5zeoe4+TrztewWWJRm63K4CpPNosEg1hyazpoIxDjSn\nCm+DEQxmsAZUFCa2yGouEbVqbxYE2SzoTyArsKprYHUbp85DN1n172cscpapkviVZ3HX3oMUjf/Q\nCLfaxweXHVD7S0J8fLCEiWgaHSb0YsLG9uAqr99evQKr23gpXRIu1UM7lmQqam/UC+HQTrPiOT74\ncJg2O0uYiKaxHwXHBag8C65K9a9pa7uQTujFhNpB0maQuvvoPh5a91MrsET46DC0I7wkTETTmBOg\neL9nPFoDd6UaVmjVNdI900/1CqyGtB/muQMZB4mX69It4aHDRj3hJWEimqay0PfO88pmeLpgoDSt\n1gqst6Dkh0t/z8gd4e+X8E2HjXrCS8JENE15ntoYWH1nYjColTLleZHuWWi5HWrj4PFNKkQuVQNL\nRA+Z19OVhIkQF3OW1VqB9a7vFVjWDjWnEPLp7/TvYzQzxoG7yne7nnTYqCe8JExE0yR0heJaG8I0\n1Fr+hH+JZK+arrLQUwPrLTU84msFli1N7T7vPgHaXg9Gk/79bA7MiVB1HqhdBsak2vWkw0Y94SVh\nIprG33EQzel4nLITqnz7sY1wxk8NrJQBnmNsJ8gKrEC17g+FX9VvT+mvbz9kNZeuJExE05Tnqb0R\ntT+ADcbonzM5f1hNnp94S630qceo9n10v0utwkq8QvcuNnv9HoGv/1R/2fjVPj7Yw0mHjXrCS8JE\nNI2rvP6/5DWX7+GhSNI0VTjx2D/UBHrJj/UfY4xTO8+7T4RuYyG+g/79jCWdb4WMlepDvLrUTqQ+\nxMO8UU94SZiIpvE1wdpQeyR8ORPyNvtegWVOhM6joMfd0OU2qYEVap4PcdFySJiIpvE1v9BQeyT8\ntLrutbUddBkDPSZCp1ukBlY4RUMRUKErCRPRfFUV1bn0OTWecBl0uwN6TIJ2v5EVWHqQIqAtkoSJ\naF5KT8CxN1UJ97O7G37s6G+hzUBZgaU32SzYIkmYiOhXnAvHNkDeO1C01+/DNC66O0m9Jtw9E77I\nZsEWScJERB9NgzOfqTpKee/53nhmtED7oXDqY/37JxommwVbJAmTxoqGicXcJfDj37im/Awcbw+9\nZsLVD+vbB4yA2097E7iq4OSHag9I/vtQcbL+Y8w26DhCnQHSbRxY28BaGcLyCvGfSVPJZsEWScKk\nMaJhYjF3CXz3hPe68pz3Ws9AsSSB47zv9kBVnYf8LeoM9JMf+q6BFZeq3ttu49VKLEtC0/sc61pf\nDecP+Gjvp28/am8WjPQ+E6EbCZPGiIaJxR//5qd9tb5hYk70HSbmS4RJWb6aPM/brKrx+trkmNAN\nOv9O7UDvMALMDS3hNeC7tksLvGMZ/Ax88QeoPANuJxjNYG0Pg5fp3xfZZ9LiSJg0RjRMLFYW+m6v\n0vkcEXcV9T/IDeqQrNo0t9p1Xn0G+rk9qmz9xZL+Bbr8zltE0WQJrB8Xl3Sp3d7SdL4Vrn8Fjr5C\n2akD2Dr2lzsCoRsJk8aIholFa6rvA6ji2urXBwBXBSpMDPXb3Q616qp6/qN4v48fYIDUwdD5NhUg\nKf3Uv6Qby19hSV0LTkbJXAVI+RARMRImjdFzuu8CdnpOLPaaWXfOpKZ9hn59ALWaymDy3GVUf3Cb\n1B3Le33BfsT397S9QZUv6T4OEns2LUDq/Eyzn7MzdPyrnXI1FPuYq0jRea5CiAiSMGlurn5YDRv9\n+gZGrRJoBZdN1n81V3wXqPruokYXuF11g8SUAB1uUgHS7Q51RnwoP+hbdYXSn32362XQM/DF9Ppz\nFYMiMFchRIRImDTGkZfBkqx+1abnBHzBNjVs1LovZaVl2GwJ6rpgW/j74KpQd2X5W30v3a0Wl6qW\n8Ha9HTqPBmtbdVcSDuZEfM7d6HkQU+db4fqXZfWSaNEkTBojGibg9VxRprnVEbZl+VCwRYXIGT8r\nsKo/wOM7wKgvwNI6fAFSW3meKiGvOVV/DcbInEUvq5dECydh0hjRMAEf7kBzO8BZCiU/Qd4Wtf/D\n3woso9Vzp9ZaDWcZzZDcW80j6clgAoMJt9uNydgCV3EJEQUkTBojGnb2hiPQnOXgtKsz3fPfh5Mf\nQbGvGlgGaDMIOo2E+Hbw8+ueD/JaK7r03uWc0h/O+ToidoC+/RCihZMwaYxo2NkbikBzu8BVBo4S\n9UFc8IEKEF8hZbCoY2w7jYQuo9UKLHMimOLUB3ak5wmurnVErLu81hGxepeXEaJlkzBprEiPjTf1\nXGtXlbr7qDoPZ7LV8NWp7b5PIaxegdVppDpEKqGL2tluiqvfl0jPE9Q6IrZCNuoJETESJs1RIBvT\nNE1NlDvtapPjqU/V3cepj/3XwOo4QgVIh5vUMJavAIlGslFPiIiTMIklbqeaPHeWqqW7Jz9WAXJm\nlxrWulirrt67j3bXq4l0S5IcZyuEaLSYCRO3281f/vIXvv/+e+Li4njiiSe47LLLIt2t8HNVqrsP\nZymUHlNDVyc/grNf+KmB1dsTICPV5LUlWQJECBG0mAmTjz76iKqqKt544w327t3L008/zd/+5qfC\nbnOmaZ7JcztxVSfg1Dk1/3HJFVi3qF9JV6rhKwkQIUQIGTRN14p4YbNkyRIGDBjA7bffDsDQoUPZ\ntWuX38fn5OTo1bXgaU5M7nKMnl/xVUdJLttDcuke4h3H6z3cjZnSVv25kDCEkoRrcZjb4zYm4DIm\noBklQIQQwfE1NxkzdyZ2u53ERG8JDZPJhNPpxGz2/xKjerLWVQEOO7hKwVEKhT8EsAJrGHS6BWOH\nm0myppJkTvTcgcTr3/8IyJEJ+BryXtQl74dXsO+Fv3+Ix0yYJCYmUlpaWnPtdrsbDJKoU126pHr+\nw1kKZ3d7AuQTqCqq9y1OYzLmrtUT6DeCJVHtAWlBASKEiA7N6NO2YYMHD+aTTz7htttuY+/evfTu\n3TvSXbq06tIlTrvahe44713C2+AKrN9Cp5EcLrDSb0B6rTkQCRAhRGTETJiMHDmSzz77jClTpqBp\nGk899VSku+Sbs9wbIO4qqDhdawXWHtAc9b8nqbdnAn0kJPdVpxCaE6k6+6O+dcGEEMKPmAkTo9HI\n448/Hulu1Ke5vcNWzlJ1xGz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      "text/plain": [
       "<matplotlib.figure.Figure at 0x168cd663a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#As expected price rises with the quality.\n",
    "sns.regplot(x='OverallQual', y='SalePrice', data=data, color='Orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GACATmRaYf+Yzn9GnPvUpSdLRo0eVl5en3bt3a/LkyZKkadOmadeuXcrKytLE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\nMQBIYC6EEEIIIcQA8D+2oM+hOFMAlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x168cd717a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Price also varies depending on neighborhood.\n",
    "plt.figure(figsize = (12, 6))\n",
    "sns.boxplot(x='Neighborhood', y='SalePrice',  data=data)\n",
    "xt = plt.xticks(rotation=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Nnz7d07MBAAAAPqlKEV1RUXHWNxTybYUAAAC4kVVpn+h77rlHf/rTn9S1a1dJ0jfffKO7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\nREQDAAAABv1/AF/ydh3Dk1gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x168cdc7d400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#There are many little houses.\n",
    "plt.figure(figsize = (12, 6))\n",
    "sns.countplot(x='HouseStyle', data=data)\n",
    "xt = plt.xticks(rotation=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EGjduzLRp04iOjiYtLQ2A1NRUWrduTXh4OFarldzcXHJycsjKyiIsLMydpYqIlHlu7UEY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\n/n+93R3DA4kuowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x168cdaf7518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#And most of the houses are single-family, so it isn't surprising that most of the them aren't large.\n",
    "sns.countplot(x='BldgType', data=data)\n",
    "xt = plt.xticks(rotation=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>FireplaceQu</th>\n",
       "      <th>Ex</th>\n",
       "      <th>Fa</th>\n",
       "      <th>Gd</th>\n",
       "      <th>None</th>\n",
       "      <th>Po</th>\n",
       "      <th>TA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fireplaces</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>690</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>324</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "FireplaceQu  Ex  Fa   Gd  None  Po   TA\n",
       "Fireplaces                             \n",
       "0             0   0    0   690   0    0\n",
       "1            19  28  324     0  20  259\n",
       "2             4   4   54     0   0   53\n",
       "3             1   1    2     0   0    1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Most of fireplaces are of good or average quality. And nearly half of houses don't have fireplaces at all.\n",
    "pd.crosstab(data.Fireplaces, data.FireplaceQu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x168cdac82e8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2HFFoUty/fz9NmjQBbifIJk2ayGt+4oHUql6F13o2Y8Czgfxn7V427Lz35Fq3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\nVGZJcfXq1Xz11VfExcVRr14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      "text/plain": [
       "<matplotlib.figure.Figure at 0x168cddf4208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Wu+Sh+lJLQrEE6TOSWfzHGlKz0gCw1lrxjs9YKj3i+5//1r2hL04Vsif3MijG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x168cdc7d5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('HeatingQC', 'SalePrice', hue='CentralAir', data=data)\n",
    "sns.factorplot('Heating', 'SalePrice', hue='CentralAir', data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Houses with central air conditioning cost more. And it is interesting that houses with poor and good heating quality cost\n",
    "nearly the same if they have central air conditioning. Also only houses with gas heating have central air conditioning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x168cd7962e8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J7/dziBMALgKZDQDdL2Qr4v/+7/+uOXPm6Mc//rHOnTunuXPn6vrrr1dBQYGW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x168cda44ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#One more interesting point is that while pavement road access is valued more, for alley they quality isn't that important.\n",
    "fig, ax = plt.subplots(1, 2, figsize = (12, 5))\n",
    "sns.boxplot(x='Street', y='SalePrice', data=data, ax=ax[0])\n",
    "sns.boxplot(x='Alley', y='SalePrice', data=data, ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x168cd8d4860>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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viouLFRkZqYyMDLndbhmGoZycHIWHh9tcPQAEnmUh2ufzyTAMlZWV+bfNnTtX2dnZSklJ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\nP6bF1bEeNSrZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x168cda44b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We can say that while quality is normally distributed, overall condition of houses is mainly average.\n",
    "fig, ax = plt.subplots(1, 2, figsize = (12, 5))\n",
    "sns.countplot(x='OverallCond', data=data, ax=ax[0])\n",
    "sns.countplot(x='OverallQual', data=data, ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x168cdcabcc0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BWicnItcaPRwTERERqdu8+ookrZMTERERERGRq4lXR4K1Tk5ERERERESuJgaXy+XydRC+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\nvw5txYoV3HPPPdx777189tl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ASxGJyLnOzXWvvvqqct1l6BrZkAYhNk6ds9Tjp27orlEa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LZt2xg8eDBpaWl4PB7a2trwer3s2bOH1NRU0tPT2bJlCwA1NTVkZGTgcDiwWCzs378fwzCora0l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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x168cd99c240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 3, figsize = (16, 12))\n",
    "ax[0,0].set_title('Gable')\n",
    "ax[0,1].set_title('Hip')\n",
    "ax[0,2].set_title('Gambrel')\n",
    "ax[1,0].set_title('Mansard')\n",
    "ax[1,1].set_title('Flat')\n",
    "ax[1,2].set_title('Shed')\n",
    "sns.stripplot(x=\"RoofMatl\", y=\"SalePrice\", data=data[data.RoofStyle == 'Gable'], jitter=True, ax=ax[0,0])\n",
    "sns.stripplot(x=\"RoofMatl\", y=\"SalePrice\", data=data[data.RoofStyle == 'Hip'], jitter=True, ax=ax[0,1])\n",
    "sns.stripplot(x=\"RoofMatl\", y=\"SalePrice\", data=data[data.RoofStyle == 'Gambrel'], jitter=True, ax=ax[0,2])\n",
    "sns.stripplot(x=\"RoofMatl\", y=\"SalePrice\", data=data[data.RoofStyle == 'Mansard'], jitter=True, ax=ax[1,0])\n",
    "sns.stripplot(x=\"RoofMatl\", y=\"SalePrice\", data=data[data.RoofStyle == 'Flat'], jitter=True, ax=ax[1,1])\n",
    "sns.stripplot(x=\"RoofMatl\", y=\"SalePrice\", data=data[data.RoofStyle == 'Shed'], jitter=True, ax=ax[1,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These graphs show information about roof materials and style. Most houses have Gable and Hip style. And material for most roofs is standard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x168cd935e10>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9F7tLfpdLowmCcJVpVjKxWCzEx8e7Xnfr1g37BVvPCufZ7E4+Wn+ED6JuQWuv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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x168cda44a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x=\"GarageQual\", y=\"SalePrice\", data=data, hue='GarageFinish', jitter=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most finished garages gave average quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x168cdfa3cc0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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++eYbFi9ezOuvv87UqVNZuHAhzZs356WXXuLnn39GKcXu3bv5/PPPOXPmDGPH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\nN7dFREQQFRWF0+kkPj7eE7t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Hhg0bOHbsGLt370arld8bhWdJmAhRAVfOmVztes+FUUph\ns9muOf+hlMLhcLh+Lioq4uuvv0av17NhwwbX+OrVq13Lhnt5eQHgcDgICwvjgw8+AH5fWTknJ4ec\nnByeeeYZ+vfvT+fOnWnTpg3Lly+vnB0WohTy64oQlaxly5ZcvHjRdT7kP//5DwEBAQQEBNC8eXPW\nrVsH/L7K9Pnz50sstLhx40b8/f3ZunUrGzZsYMOGDcyYMYOVK1deE0QdO3YkJSWFY8eOAb8/fvet\nt94iLS0NrVbLqFGj6NatG8nJySUCSwhPkCMTISqZ0WjknXfeYebMmeTl5VGnTh3eeecdAObOncu0\nadNYuHAhBoOBhQsXYjQaXe9ds2aN65nyVzzxxBPMnz/ftWLyFQ0aNGDOnDmMGzcOp9NJo0aNmDt3\nLr6+vrRt25a+ffvi5eVF586dOX36tOd3XNzRZNVgIYQQFSbTXEIIISpMwkQIIUSFSZgIIYSoMAkT\nIYQQFSZhIoQQosIkTIQQQlSYhIkQQogK+/8BFJzv5msXRn0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x168cd9af5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(x=\"PoolArea\", y=\"SalePrice\", hue=\"PoolQC\", data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is worth noting that there are only 7 different pool areas. And while for most of them mean price is ~200000-300000$, pools with area 555 cost very much. Let's see."
   ]
  },
  {
   "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>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>1182</th>\n",
       "      <td>1183</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>15623</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>555</td>\n",
       "      <td>Ex</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>745000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "1182  1183          60       RL        160.0    15623   Pave  None      IR1   \n",
       "\n",
       "     LandContour Utilities    ...     PoolArea PoolQC  Fence MiscFeature  \\\n",
       "1182         Lvl    AllPub    ...          555     Ex  MnPrv        None   \n",
       "\n",
       "     MiscVal MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "1182       0      7   2007        WD        Abnorml     745000  \n",
       "\n",
       "[1 rows x 81 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There is only one such pool and sale condition for it is 'Abnorml'.\n",
    "data.loc[data.PoolArea == 555]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x168ce16ccf8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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24u0knSqGdO48xJgoiIlxquWw7v0nbPmvQ5bcCBo6FcHjfw1BEFTLQ0TUFbjcEla+thu5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\nzBjWpFyNJuVqt+3v9Kl1bSbhKnUI+sZSmhrL0PiEt9rG2YwWA6VNhVT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XwHzhAC9L6/yy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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x168ce0fea20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize = (12, 5))\n",
    "sns.stripplot(x=\"SaleType\", y=\"SalePrice\", data=data, jitter=True, ax=ax[0])\n",
    "sns.stripplot(x=\"SaleCondition\", y=\"SalePrice\", data=data, jitter=True, ax=ax[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the sold houses are either new or sold under Warranty Deed. And only a little number of houses are sales between family, adjoining land purchases or allocation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"datprep\">Data preparation</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 60,  20,  70,  50, 190,  45,  90, 120,  30,  85,  80, 160,  75,\n",
       "       180,  40], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#MSSubClass shows codes for the type of dwelling, it is clearly a categorical variable.\n",
    "data['MSSubClass'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['MSSubClass'] = data['MSSubClass'].astype(str)\n",
    "test['MSSubClass'] = test['MSSubClass'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transforming skewered data and dummifying categorical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for col in data.columns:\n",
    "    if data[col].dtype != 'object':\n",
    "        if skew(data[col]) > 0.75:\n",
    "            data[col] = np.log1p(data[col])\n",
    "        pass\n",
    "    else:\n",
    "        dummies = pd.get_dummies(data[col], drop_first=False)\n",
    "        dummies = dummies.add_prefix(\"{}_\".format(col))\n",
    "        data.drop(col, axis=1, inplace=True)\n",
    "        data = data.join(dummies)\n",
    "        \n",
    "for col in test.columns:\n",
    "    if test[col].dtype != 'object':\n",
    "        if skew(test[col]) > 0.75:\n",
    "            test[col] = np.log1p(test[col])\n",
    "        pass\n",
    "    else:\n",
    "        dummies = pd.get_dummies(test[col], drop_first=False)\n",
    "        dummies = dummies.add_prefix(\"{}_\".format(col))\n",
    "        test.drop(col, axis=1, inplace=True)\n",
    "        test = test.join(dummies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maybe a good idea would be to create some new features, but I decided to do without it. It is time-consuming and model is good enough without it. Besides, the number of features if quite high already."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>9.042040</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>5.283204</td>\n",
       "      <td>6.561031</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.394449</td>\n",
       "      <td>9.169623</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.886532</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.234107</td>\n",
       "      <td>9.328212</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>5.093750</td>\n",
       "      <td>6.188264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>9.164401</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.379897</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4.442651</td>\n",
       "      <td>9.565284</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>5.860786</td>\n",
       "      <td>6.486161</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 318 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  LotFrontage   LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0   1     4.189655  9.042040            7            5       2003   \n",
       "1   2     4.394449  9.169623            6            8       1976   \n",
       "2   3     4.234107  9.328212            7            5       2001   \n",
       "3   4     4.110874  9.164401            7            5       1915   \n",
       "4   5     4.442651  9.565284            8            5       2000   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2          ...            \\\n",
       "0          2003    5.283204    6.561031         0.0          ...             \n",
       "1          1976    0.000000    6.886532         0.0          ...             \n",
       "2          2002    5.093750    6.188264         0.0          ...             \n",
       "3          1970    0.000000    5.379897         0.0          ...             \n",
       "4          2000    5.860786    6.486161         0.0          ...             \n",
       "\n",
       "   SaleType_ConLw  SaleType_New  SaleType_Oth  SaleType_WD  \\\n",
       "0               0             0             0            1   \n",
       "1               0             0             0            1   \n",
       "2               0             0             0            1   \n",
       "3               0             0             0            1   \n",
       "4               0             0             0            1   \n",
       "\n",
       "   SaleCondition_Abnorml  SaleCondition_AdjLand  SaleCondition_Alloca  \\\n",
       "0                      0                      0                     0   \n",
       "1                      0                      0                     0   \n",
       "2                      0                      0                     0   \n",
       "3                      1                      0                     0   \n",
       "4                      0                      0                     0   \n",
       "\n",
       "   SaleCondition_Family  SaleCondition_Normal  SaleCondition_Partial  \n",
       "0                     0                     1                      0  \n",
       "1                     0                     1                      0  \n",
       "2                     0                     1                      0  \n",
       "3                     0                     0                      0  \n",
       "4                     0                     1                      0  \n",
       "\n",
       "[5 rows x 318 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This is how the data looks like now.\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = data.drop('SalePrice',axis=1)\n",
    "Y_train = data['SalePrice']\n",
    "X_test  = test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"model\">Model</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Function to measure accuracy.\n",
    "def rmlse(val, target):\n",
    "    return np.sqrt(np.sum(((np.log1p(val) - np.log1p(np.expm1(target)))**2) / len(target)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X_train, Y_train, test_size=0.33)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll try several models.\n",
    "\n",
    "Ridge is linear least squares model with l2 regularization (using squared difference).\n",
    "\n",
    "RidgeCV is Ridge regression with built-in cross-validation.\n",
    "\n",
    "Lasso is Linear Model trained with l1 regularization (using module).\n",
    "\n",
    "LassoCV is Lasso linear model with iterative fitting along a regularization path.\n",
    "\n",
    "Random Forest is usually good in cases with many features.\n",
    "\n",
    "And XGBoost is a library which is very popular lately and usually gives good results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13346260272941882"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge = Ridge(alpha=10, solver='auto').fit(Xtrain, ytrain)\n",
    "val_ridge = np.expm1(ridge.predict(Xtest))\n",
    "rmlse(val_ridge, ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13346260273214372"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_cv = RidgeCV(alphas=(0.01, 0.05, 0.1, 0.3, 1, 3, 5, 10))\n",
    "ridge_cv.fit(Xtrain, ytrain)\n",
    "val_ridge_cv = np.expm1(ridge_cv.predict(Xtest))\n",
    "rmlse(val_ridge_cv, ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12607216571928639"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "las = linear_model.Lasso(alpha=0.0005).fit(Xtrain, ytrain)\n",
    "las_ridge = np.expm1(las.predict(Xtest))\n",
    "rmlse(las_ridge, ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:484: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.12607216571928639"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "las_cv = LassoCV(alphas=(0.0001, 0.0005, 0.001, 0.01, 0.05, 0.1, 0.3, 1, 3, 5, 10))\n",
    "las_cv.fit(Xtrain, ytrain)\n",
    "val_las_cv = np.expm1(las_cv.predict(Xtest))\n",
    "rmlse(val_las_cv, ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13385573967805864"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_xgb = xgb.XGBRegressor(n_estimators=340, max_depth=2, learning_rate=0.2) #the params were tuned using xgb.cv\n",
    "model_xgb.fit(Xtrain, ytrain)\n",
    "xgb_preds = np.expm1(model_xgb.predict(Xtest))\n",
    "rmlse(xgb_preds, ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15645551722765741"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest = RandomForestRegressor(min_samples_split =5,\n",
    "                                min_weight_fraction_leaf = 0.0,\n",
    "                                max_leaf_nodes = None,\n",
    "                                max_depth = None,\n",
    "                                n_estimators = 300,\n",
    "                                max_features = 'auto')\n",
    "\n",
    "forest.fit(Xtrain, ytrain)\n",
    "Y_pred_RF = np.expm1(forest.predict(Xtest))\n",
    "rmlse(Y_pred_RF, ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So linear models perform better than the others. And lasso is the best.\n",
    "\n",
    "Lasso model has one nice feature - it provides feature selection, as it assignes zero weights to the least important variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "So we have 126 variables\n"
     ]
    }
   ],
   "source": [
    "coef = pd.Series(las_cv.coef_, index = X_train.columns)\n",
    "v = coef.loc[las_cv.coef_ != 0].count() \n",
    "print('So we have ' + str(v) + ' variables')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Basically I sort features by weights and take variables with max weights.\n",
    "indices = np.argsort(abs(las_cv.coef_))[::-1][0:v]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RoofMatl_ClyTile\n"
     ]
    }
   ],
   "source": [
    "#Features to be used. I do this because I want to see how good will other models perform with these features.\n",
    "features = X_train.columns[indices]\n",
    "for i in features:\n",
    "    if i not in X_test.columns:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is only one selected feature which isn't in test data. I'll simply add this column with zero values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_test['RoofMatl_ClyTile'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = X_train[features]\n",
    "Xt = X_test[features]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see whether something changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Xtrain1, Xtest1, ytrain1, ytest1 = train_test_split(X, Y_train, test_size=0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11924552653155912"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge = Ridge(alpha=5, solver='svd').fit(Xtrain1, ytrain1)\n",
    "val_ridge = np.expm1(ridge.predict(Xtest1))\n",
    "rmlse(val_ridge, ytest1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11565867162196054"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "las_cv = LassoCV(alphas=(0.0001, 0.0005, 0.001, 0.01, 0.05, 0.1, 0.3, 1, 3, 5, 10)).fit(Xtrain1, ytrain1)\n",
    "val_las = np.expm1(las_cv.predict(Xtest1))\n",
    "rmlse(val_las, ytest1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12409445447687704"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_xgb = xgb.XGBRegressor(n_estimators=340, max_depth=2, learning_rate=0.2) #the params were tuned using xgb.cv\n",
    "model_xgb.fit(Xtrain1, ytrain1)\n",
    "xgb_preds = np.expm1(model_xgb.predict(Xtest1))\n",
    "rmlse(xgb_preds, ytest1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1461884843752316"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest = RandomForestRegressor(min_samples_split =5,\n",
    "                                min_weight_fraction_leaf = 0.0,\n",
    "                                max_leaf_nodes = None,\n",
    "                                max_depth = 100,\n",
    "                                n_estimators = 300,\n",
    "                                max_features = None)\n",
    "\n",
    "forest.fit(Xtrain1, ytrain1)\n",
    "Y_pred_RF = np.expm1(forest.predict(Xtest1))\n",
    "rmlse(Y_pred_RF, ytest1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy got worse, but it is due to random seed while splitting the data. It's time for prediction!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "las_cv1 = LassoCV(alphas=(0.0001, 0.0005, 0.001, 0.01, 0.05, 0.1, 0.3, 1, 3, 5, 10))\n",
    "las_cv1.fit(X, Y_train)\n",
    "lasso_preds = np.expm1(las_cv1.predict(Xt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#I added XGBoost as it usually improves the predictions.\n",
    "model_xgb = xgb.XGBRegressor(n_estimators=340, max_depth=2, learning_rate=0.1)\n",
    "model_xgb.fit(X, Y_train)\n",
    "xgb_preds = np.expm1(model_xgb.predict(Xt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "preds = 0.7 * lasso_preds + 0.3 * xgb_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        'Id': test['Id'].astype(int),\n",
    "        'SalePrice': preds\n",
    "    })\n",
    "submission.to_csv('home.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But the result wasn't very good. I thought for some time and then decided that the problem could lie in feature selection - maybe I selected bad features or Maybe random seed gave bad results. I decided to try selecting features based on full training dataset (not just on part of the data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "So we have 120 variables\n"
     ]
    }
   ],
   "source": [
    "model_lasso = LassoCV(alphas=(0.0001, 0.0005, 0.001, 0.01, 0.05, 0.1, 0.3, 1, 3, 5, 10, 100))\n",
    "model_lasso.fit(X_train, Y_train)\n",
    "coef = pd.Series(model_lasso.coef_, index = X_train.columns)\n",
    "v1 = coef.loc[model_lasso.coef_ != 0].count()\n",
    "print('So we have ' + str(v1) + ' variables')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "indices = np.argsort(abs(model_lasso.coef_))[::-1][0:v1]\n",
    "features_f=X_train.columns[indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features in full, but not in val:\n",
      "1stFlrSF\n",
      "GarageCond_Fa\n",
      "SaleType_New\n",
      "Functional_Maj2\n",
      "Foundation_BrkTil\n",
      "MSSubClass_120\n",
      "SaleType_COD\n",
      "LandSlope_Mod\n",
      "SaleCondition_Family\n",
      "KitchenQual_TA\n",
      "LotShape_IR1\n",
      "Heating_Grav\n",
      "MasVnrType_BrkCmn\n",
      "BsmtFinType1_Rec\n",
      "BedroomAbvGr\n",
      "HeatingQC_TA\n",
      "Exterior2nd_VinylSd\n",
      "MasVnrType_Stone\n",
      "\n",
      "Features in val, but not in full:\n",
      "SaleCondition_Partial\n",
      "LandSlope_Gtl\n",
      "Neighborhood_MeadowV\n",
      "Alley_None\n",
      "MSZoning_RL\n",
      "LandContour_Lvl\n",
      "MSSubClass_60\n",
      "BsmtFinType1_GLQ\n",
      "Foundation_CBlock\n",
      "SaleType_ConLD\n",
      "Exterior2nd_HdBoard\n",
      "Exterior2nd_Wd Shng\n",
      "BsmtQual_Fa\n",
      "BsmtFinType1_BLQ\n",
      "BsmtFinType2_ALQ\n",
      "Electrical_SBrkr\n",
      "BsmtFinType1_ALQ\n",
      "Neighborhood_Gilbert\n",
      "SaleCondition_Alloca\n",
      "ExterQual_Gd\n",
      "BsmtCond_TA\n",
      "Fence_None\n",
      "HeatingQC_Gd\n",
      "LotShape_Reg\n"
     ]
    }
   ],
   "source": [
    "print('Features in full, but not in val:')\n",
    "for i in features_f:\n",
    "    if i not in features:\n",
    "        print(i)\n",
    "print('\\n' + 'Features in val, but not in full:')\n",
    "for i in features:\n",
    "    if i not in features_f:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A lot of difference between the selected features. I suppose that the reason for this is that there was too little data relatively to the number of features in the first case. So I'll use the features obtain with the analysis of the whole train dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in features_f:\n",
    "    if i not in X_test.columns:\n",
    "        X_test[i] = 0\n",
    "        print(i)\n",
    "X = X_train[features_f]\n",
    "Xt = X_test[features_f]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now all necessary features are present in both train and test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_lasso = LassoCV(alphas=(0.0001, 0.0005, 0.001, 0.01, 0.05, 0.1, 0.3, 1, 3, 5, 10))\n",
    "model_lasso.fit(X, Y_train)\n",
    "lasso_preds = np.expm1(model_lasso.predict(Xt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_xgb = xgb.XGBRegressor(n_estimators=340, max_depth=2, learning_rate=0.1) #the params were tuned using xgb.cv\n",
    "model_xgb.fit(X, Y_train)\n",
    "xgb_preds = np.expm1(model_xgb.predict(Xt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "solution = pd.DataFrame({\"id\":test.Id, \"SalePrice\":0.7*lasso_preds + 0.3*xgb_preds})\n",
    "solution.to_csv(\"House_price.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The best result I got with this model was 0.12922. Currently top results are 0.10-0.11."
   ]
  }
 ],
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