task0/House_prices.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Houses prices prediction\n",
    "#### Attempt some feature engineering and outlier removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "%matplotlib inline\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the data\n",
    "\n",
    "train = pd.read_csv('data/train.csv', index_col=['Id'])\n",
    "test = pd.read_csv('data/test.csv', index_col=['Id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1460.000000\n",
       "mean     180921.195890\n",
       "std       79442.502883\n",
       "min       34900.000000\n",
       "25%      129975.000000\n",
       "50%      163000.000000\n",
       "75%      214000.000000\n",
       "max      755000.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check target label\n",
    "\n",
    "train.SalePrice.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSSubClass         int64\n",
      "LotFrontage      float64\n",
      "LotArea            int64\n",
      "OverallQual        int64\n",
      "OverallCond        int64\n",
      "YearBuilt          int64\n",
      "YearRemodAdd       int64\n",
      "MasVnrArea       float64\n",
      "BsmtFinSF1         int64\n",
      "BsmtFinSF2         int64\n",
      "BsmtUnfSF          int64\n",
      "TotalBsmtSF        int64\n",
      "1stFlrSF           int64\n",
      "2ndFlrSF           int64\n",
      "LowQualFinSF       int64\n",
      "GrLivArea          int64\n",
      "BsmtFullBath       int64\n",
      "BsmtHalfBath       int64\n",
      "FullBath           int64\n",
      "HalfBath           int64\n",
      "BedroomAbvGr       int64\n",
      "KitchenAbvGr       int64\n",
      "TotRmsAbvGrd       int64\n",
      "Fireplaces         int64\n",
      "GarageYrBlt      float64\n",
      "GarageCars         int64\n",
      "GarageArea         int64\n",
      "WoodDeckSF         int64\n",
      "OpenPorchSF        int64\n",
      "EnclosedPorch      int64\n",
      "3SsnPorch          int64\n",
      "ScreenPorch        int64\n",
      "PoolArea           int64\n",
      "MiscVal            int64\n",
      "MoSold             int64\n",
      "YrSold             int64\n",
      "SalePrice          int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Check numeric features\n",
    "numeric_features = train.select_dtypes(include=[pd.np.number])\n",
    "print(numeric_features.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SalePrice      1.000000\n",
      "OverallQual    0.790982\n",
      "GrLivArea      0.708624\n",
      "GarageCars     0.640409\n",
      "GarageArea     0.623431\n",
      "Name: SalePrice, dtype: float64\n",
      "YrSold          -0.028923\n",
      "OverallCond     -0.077856\n",
      "MSSubClass      -0.084284\n",
      "EnclosedPorch   -0.128578\n",
      "KitchenAbvGr    -0.135907\n",
      "Name: SalePrice, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x267d9cb7390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check correlation betwen target label and the numeric columns\n",
    "\n",
    "corr = numeric_features.corr()\n",
    "print (corr['SalePrice'].sort_values(ascending=False)[:5])\n",
    "print (corr['SalePrice'].sort_values(ascending=False)[-5:])\n",
    "\n",
    "sns.heatmap(corr, vmax=.8, square=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x267dbcf2fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check how to quality affects the saleprice\n",
    "\n",
    "sns.barplot(x='OverallQual', y='SalePrice', data=train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Null Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PoolQC</th>\n",
       "      <td>1453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature</th>\n",
       "      <td>1406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>1369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>1179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <td>690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Null Count\n",
       "Feature                 \n",
       "PoolQC              1453\n",
       "MiscFeature         1406\n",
       "Alley               1369\n",
       "Fence               1179\n",
       "FireplaceQu          690\n",
       "LotFrontage          259\n",
       "GarageType            81\n",
       "GarageCond            81\n",
       "GarageFinish          81\n",
       "GarageQual            81\n",
       "GarageYrBlt           81\n",
       "BsmtFinType2          38\n",
       "BsmtExposure          38\n",
       "BsmtQual              37\n",
       "BsmtCond              37\n",
       "BsmtFinType1          37\n",
       "MasVnrArea             8\n",
       "MasVnrType             8\n",
       "Electrical             1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulls = pd.DataFrame(train.isnull().sum().sort_values(ascending=False)[:19])\n",
    "nulls.columns = ['Null Count']\n",
    "nulls.index.name = 'Feature'\n",
    "nulls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>Functional</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>91</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>1452</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>1423</td>\n",
       "      <td>1423</td>\n",
       "      <td>1422</td>\n",
       "      <td>1423</td>\n",
       "      <td>1422</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>1459</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>770</td>\n",
       "      <td>1379</td>\n",
       "      <td>1379</td>\n",
       "      <td>1379</td>\n",
       "      <td>1379</td>\n",
       "      <td>1460</td>\n",
       "      <td>7</td>\n",
       "      <td>281</td>\n",
       "      <td>54</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Unf</td>\n",
       "      <td>Unf</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>TA</td>\n",
       "      <td>Typ</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>Unf</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>Gd</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1151</td>\n",
       "      <td>1454</td>\n",
       "      <td>50</td>\n",
       "      <td>925</td>\n",
       "      <td>1311</td>\n",
       "      <td>1459</td>\n",
       "      <td>1052</td>\n",
       "      <td>1382</td>\n",
       "      <td>225</td>\n",
       "      <td>1260</td>\n",
       "      <td>1445</td>\n",
       "      <td>1220</td>\n",
       "      <td>726</td>\n",
       "      <td>1141</td>\n",
       "      <td>1434</td>\n",
       "      <td>515</td>\n",
       "      <td>504</td>\n",
       "      <td>864</td>\n",
       "      <td>906</td>\n",
       "      <td>1282</td>\n",
       "      <td>647</td>\n",
       "      <td>649</td>\n",
       "      <td>1311</td>\n",
       "      <td>953</td>\n",
       "      <td>430</td>\n",
       "      <td>1256</td>\n",
       "      <td>1428</td>\n",
       "      <td>741</td>\n",
       "      <td>1365</td>\n",
       "      <td>1334</td>\n",
       "      <td>735</td>\n",
       "      <td>1360</td>\n",
       "      <td>380</td>\n",
       "      <td>870</td>\n",
       "      <td>605</td>\n",
       "      <td>1311</td>\n",
       "      <td>1326</td>\n",
       "      <td>1340</td>\n",
       "      <td>3</td>\n",
       "      <td>157</td>\n",
       "      <td>49</td>\n",
       "      <td>1267</td>\n",
       "      <td>1198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MSZoning Street Alley LotShape LandContour Utilities LotConfig  \\\n",
       "count      1460   1460    91     1460        1460      1460      1460   \n",
       "unique        5      2     2        4           4         2         5   \n",
       "top          RL   Pave  Grvl      Reg         Lvl    AllPub    Inside   \n",
       "freq       1151   1454    50      925        1311      1459      1052   \n",
       "\n",
       "       LandSlope Neighborhood Condition1 Condition2 BldgType HouseStyle  \\\n",
       "count       1460         1460       1460       1460     1460       1460   \n",
       "unique         3           25          9          8        5          8   \n",
       "top          Gtl        NAmes       Norm       Norm     1Fam     1Story   \n",
       "freq        1382          225       1260       1445     1220        726   \n",
       "\n",
       "       RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType ExterQual  \\\n",
       "count       1460     1460        1460        1460       1452      1460   \n",
       "unique         6        8          15          16          4         4   \n",
       "top        Gable  CompShg     VinylSd     VinylSd       None        TA   \n",
       "freq        1141     1434         515         504        864       906   \n",
       "\n",
       "       ExterCond Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1  \\\n",
       "count       1460       1460     1423     1423         1422         1423   \n",
       "unique         5          6        4        4            4            6   \n",
       "top           TA      PConc       TA       TA           No          Unf   \n",
       "freq        1282        647      649     1311          953          430   \n",
       "\n",
       "       BsmtFinType2 Heating HeatingQC CentralAir Electrical KitchenQual  \\\n",
       "count          1422    1460      1460       1460       1459        1460   \n",
       "unique            6       6         5          2          5           4   \n",
       "top             Unf    GasA        Ex          Y      SBrkr          TA   \n",
       "freq           1256    1428       741       1365       1334         735   \n",
       "\n",
       "       Functional FireplaceQu GarageType GarageFinish GarageQual GarageCond  \\\n",
       "count        1460         770       1379         1379       1379       1379   \n",
       "unique          7           5          6            3          5          5   \n",
       "top           Typ          Gd     Attchd          Unf         TA         TA   \n",
       "freq         1360         380        870          605       1311       1326   \n",
       "\n",
       "       PavedDrive PoolQC  Fence MiscFeature SaleType SaleCondition  \n",
       "count        1460      7    281          54     1460          1460  \n",
       "unique          3      3      4           4        9             6  \n",
       "top             Y     Gd  MnPrv        Shed       WD        Normal  \n",
       "freq         1340      3    157          49     1267          1198  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categoricals = train.select_dtypes(include=[pd.np.object])\n",
    "categoricals.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The bottom cell will visualize the relationships between SalePrice and the rest of the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this if you have enough memory\n",
    "plt.rcParams.update({'figure.max_open_warning': 50})\n",
    "\n",
    "# for i, c in enumerate(categoricals):\n",
    "#     plt.figure(i)\n",
    "#     sns.barplot(x=c, y='SalePrice', data=train);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Candidates for separate feature, who have high relationships with SalePrice are:\n",
    "* SaleCondition - Partial\n",
    "* MiscFeature - TenC\n",
    "* FireplaceQu - Ex\n",
    "* Electrical - SBrkr\n",
    "* RoofMatl - WdShngl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's see what happenes when we make a new feature\n",
    "# results - 0.00001 improvement\n",
    "\n",
    "def encode(x): return 1 if x == 'Partial' else 0\n",
    "train['enc_condition'] = train.SaleCondition.apply(encode)\n",
    "train = train.drop(labels=['SaleCondition'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's see what happenes when we make a new feature\n",
    "# results - 0.000001 improvement\n",
    "\n",
    "def encode(x): return 1 if x == 'TenC' else 0\n",
    "train['enc_misc'] = train.MiscFeature.apply(encode)\n",
    "train = train.drop(labels=['MiscFeature'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's see what happenes when we make a new feature\n",
    "# results - 0.0001 improvement\n",
    "\n",
    "def encode(x): return 1 if x == 'Ex' else 0\n",
    "train['enc_fireplace'] = train.FireplaceQu.apply(encode)\n",
    "train = train.drop(labels=['FireplaceQu'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's see what happenes when we make a new feature\n",
    "# results - 0.001 improvement\n",
    "\n",
    "def encode(x): return 1 if x == 'SBrkr' else 0\n",
    "train['enc_elec'] = train.Electrical.apply(encode)\n",
    "train = train.drop(labels=['Electrical'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's see what happenes when we make a new feature\n",
    "# results - 0.0015 improvement\n",
    "\n",
    "def encode(x): return 1 if x == 'WdShngl' else 0\n",
    "train['enc_roof'] = train.RoofMatl.apply(encode)\n",
    "train = train.drop(labels=['RoofMatl'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify and remove outliers\n",
    "#### The commented code will draw scatter plots between SalePrice and all other columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i, n in enumerate(numeric_features.columns):\n",
    "#     print(n)\n",
    "#     plt.figure(i)\n",
    "#     plt.scatter(x=train[n], y=train['SalePrice'])\n",
    "#     plt.ylabel('Sale Price')\n",
    "#     plt.xlabel(n)\n",
    "#     plt.show()\n",
    "\n",
    "def clear_outliers(data):\n",
    "    data = data[data['LotArea'] < 100000]\n",
    "    data = data[data['BsmtFinSF1'] < 1800]\n",
    "    data = data[data['BsmtFinSF2'] < 1200]\n",
    "    data = data[data['TotalBsmtSF'] < 2500]\n",
    "    \n",
    "    return data\n",
    "\n",
    "train = clear_outliers(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1010, 79)\n",
      "(433, 79)\n",
      "(1010,)\n",
      "(433,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "def split_data_by(data, target):\n",
    "\n",
    "    # split the dataset to with and without labels\n",
    "    train_no_sale_price = data.drop(labels=[target], axis=1)\n",
    "    train_sale_price = data[target]\n",
    "\n",
    "    # split the train set to 70/30 train/test\n",
    "    return train_test_split(train_no_sale_price, \n",
    "                            train_sale_price, \n",
    "                            test_size=0.3, \n",
    "                            random_state=4330)\n",
    "\n",
    "x_train, x_test, y_train, y_test = split_data_by(train, 'SalePrice')\n",
    "# Make sure correct data fell int the correct variables\n",
    "for df in [x_train, x_test, y_train, y_test]:\n",
    "    print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handle null values\n",
    "numeric_columns = x_train.select_dtypes(include=[pd.np.number]).columns\n",
    "x_train[numeric_columns] = x_train[numeric_columns].fillna(0)\n",
    "x_test[numeric_columns] = x_test[numeric_columns].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "def encode_categorical(data, columns, encoders):\n",
    "    data = data.fillna(\"\")\n",
    "    return pd.DataFrame({col: encoders[col].transform(data[col]) for col in columns},\n",
    "                        index = data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns = x_train.select_dtypes(include=[pd.np.object]).columns\n",
    "train = train.fillna(\"\")\n",
    "encoders = {col: LabelEncoder().fit(train[col]) for col in categorical_columns}\n",
    "\n",
    "\n",
    "train_encoded = encode_categorical(train, categorical_columns, encoders)\n",
    "\n",
    "one_hot_encoder = OneHotEncoder().fit(train_encoded)\n",
    "one_hot_x_train = one_hot_encoder.transform(encode_categorical(x_train[categorical_columns], categorical_columns, encoders))\n",
    "one_hot_x_test = one_hot_encoder.transform(encode_categorical(x_test[categorical_columns], categorical_columns, encoders))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_x_train = pd.np.concatenate([one_hot_x_train.todense(), x_train[numeric_columns]], axis=1)\n",
    "new_x_test = pd.np.concatenate([one_hot_x_test.todense(), x_test[numeric_columns]], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The bottom cell is giving the weird values we were talking about in the slack channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.linear_model import LinearRegression\n",
    "# lr = LinearRegression().fit(new_x_train, y_train)\n",
    "# lr.score(new_x_test, y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check how data is skewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6696605533259425 0.04948892365856159\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x267dbe0fa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.distplot(train['SalePrice'])\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.distplot(pd.np.log10(train['SalePrice']))\n",
    "\n",
    "print(train.SalePrice.skew(), pd.np.log10(train['SalePrice']).skew())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I found Ridge to give the best results of the linear algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha: 0.01\n",
      "0.9423401636947232 0.8898012448940393\n",
      "\n",
      "alpha: 0.1\n",
      "0.9421859462413315 0.8919960480999863\n",
      "\n",
      "alpha: 1\n",
      "0.9404488551520288 0.8983901551175418\n",
      "\n",
      "alpha: 10\n",
      "0.9335917568976061 0.9049612512482914\n",
      "\n",
      "alpha: 100\n",
      "0.9138141222777566 0.8995291233570234\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# regularization\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "alpha = [0.01, 0.1, 1, 10, 100]\n",
    "\n",
    "for a in alpha:\n",
    "    all_data_lr = Ridge(alpha = a).fit(new_x_train, y_train)\n",
    "    print('alpha:', a)\n",
    "    print(all_data_lr.score(new_x_train, y_train), all_data_lr.score(new_x_test, y_test))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha: 0.01\n",
      "0.9466239333091557 0.9069865194122935\n",
      "\n",
      "alpha: 0.1\n",
      "0.9464824193458047 0.9098514023819393\n",
      "\n",
      "alpha: 1\n",
      "0.9444714372465681 0.9207627255646759\n",
      "\n",
      "alpha: 10\n",
      "0.9344372027621587 0.9314846967081457\n",
      "\n",
      "alpha: 100\n",
      "0.9165750263469484 0.9296886054060456\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# try with log10\n",
    "\n",
    "for a in alpha:\n",
    "    all_data_lr = Ridge(alpha = a).fit(new_x_train, pd.np.log10(y_train))\n",
    "    print('alpha:', a)\n",
    "    print(all_data_lr.score(new_x_train, pd.np.log10(y_train)), all_data_lr.score(new_x_test, pd.np.log10(y_test)))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x267dbcf2e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = Ridge(10).fit(new_x_train, pd.np.log10(y_train))\n",
    "predictions = 10 ** lr.predict(new_x_test)\n",
    "\n",
    "plt.figure(figsize=(12,12))\n",
    "sns.regplot(y_test, predictions)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Score with Ridge and alpha = 10:\n",
    "### Train: 0.9344\n",
    "### Test:  0.9315"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The next step I'd take is to create separate models to predict the missing null values, but I found it to take too mich time and I couldn't fit it in my time window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_features(train):\n",
    "    def encode(x): return 1 if x == 'Partial' else 0\n",
    "    train['enc_condition'] = train.SaleCondition.apply(encode)\n",
    "    train = train.drop(labels=['SaleCondition'], axis=1)\n",
    "\n",
    "    def encode(x): return 1 if x == 'TenC' else 0\n",
    "    train['enc_misc'] = train.MiscFeature.apply(encode)\n",
    "    train = train.drop(labels=['MiscFeature'], axis=1)\n",
    "    \n",
    "    def encode(x): return 1 if x == 'Ex' else 0\n",
    "    train['enc_fireplace'] = train.FireplaceQu.apply(encode)\n",
    "    train = train.drop(labels=['FireplaceQu'], axis=1)\n",
    "    \n",
    "    def encode(x): return 1 if x == 'SBrkr' else 0\n",
    "    train['enc_elec'] = train.Electrical.apply(encode)\n",
    "    train = train.drop(labels=['Electrical'], axis=1)\n",
    "    \n",
    "    def encode(x): return 1 if x == 'WdShngl' else 0\n",
    "    train['enc_roof'] = train.RoofMatl.apply(encode)\n",
    "    train = train.drop(labels=['RoofMatl'], axis=1)\n",
    "    \n",
    "    return train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model submission\n",
    "train = pd.read_csv('data/train.csv', index_col=['Id'])\n",
    "test = pd.read_csv('data/test.csv', index_col=['Id'])\n",
    "\n",
    "def pipeline_data(data):\n",
    "    data = create_features(data)\n",
    "    _numeric = data[numeric_columns].fillna(0)\n",
    "    _categorical = data[categorical_columns].fillna(\"\") \n",
    "    _encoded = encode_categorical(_categorical, categorical_columns, encoders)\n",
    "    _one_hot_encoded = one_hot_encoder.transform(_encoded)\n",
    "    _merged = pd.np.concatenate([_one_hot_encoded.todense(), _numeric], axis=1)\n",
    "    return _merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1443, 278) (1443,)\n"
     ]
    }
   ],
   "source": [
    "train = clear_outliers(train)\n",
    "\n",
    "full_x = pipeline_data(train)\n",
    "full_y = train['SalePrice']\n",
    "\n",
    "# Fix test set missing data problems\n",
    "test['MSZoning'].fillna('RL', inplace=True)\n",
    "test['Utilities'].fillna('AllPub', inplace=True)\n",
    "test['Exterior1st'].fillna('VinylSd', inplace=True)\n",
    "test['Exterior2nd'].fillna('VinylSd', inplace=True)\n",
    "test['KitchenQual'].fillna('TA', inplace=True)\n",
    "test['Functional'].fillna('Typ', inplace=True)\n",
    "test['SaleType'].fillna('WD', inplace=True)\n",
    "\n",
    "test_set = pipeline_data(test)\n",
    "\n",
    "print (full_x.shape, full_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1459, 278) (1459,) (1459,)\n"
     ]
    }
   ],
   "source": [
    "ridge = Ridge(alpha=10).fit(full_x, pd.np.log10(full_y))\n",
    "predictions = 10**ridge.predict(test_set)\n",
    "print(test_set.shape, test.index.shape, predictions.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>114265.240275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>152130.007205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>181483.357426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>196949.646922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>193520.801687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id      SalePrice\n",
       "0  1461  114265.240275\n",
       "1  1462  152130.007205\n",
       "2  1463  181483.357426\n",
       "3  1464  196949.646922\n",
       "4  1465  193520.801687"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_df = pd.DataFrame({\"Id\":test.index, \"SalePrice\":predictions})\n",
    "predictions_df.to_csv(\"submit_me.csv\", index=False)\n",
    "predictions_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 926 място в Kaggle"
   ]
  }
 ],
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