Data preparation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoding categoracl features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./Data/house_prices_kaggle/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def miss(df):\n",
    "    # it would be cool to add all types of columns into table\n",
    "    missing = df.isnull().sum()\n",
    "    missing = missing[missing > 0].sort_values(ascending = False)\n",
    "    perc_miss = 100 * missing / len(df)\n",
    "    missing = pd.concat([missing, perc_miss], axis = 1)\n",
    "    missing.columns = [['Total missed values', '% of missed values']]\n",
    "    print(\"This DataFrame has \" + str(missing.shape[0]) + \" columns with NaN values from \" + \n",
    "          str(df.shape[1]) + \" columns \");\n",
    "    return missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This DataFrame has 19 columns with NaN values from 81 columns \n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Total missed values</th>\n",
       "      <th>% of missed values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PoolQC</th>\n",
       "      <td>1453</td>\n",
       "      <td>99.520548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature</th>\n",
       "      <td>1406</td>\n",
       "      <td>96.301370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>1369</td>\n",
       "      <td>93.767123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>1179</td>\n",
       "      <td>80.753425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <td>690</td>\n",
       "      <td>47.260274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>259</td>\n",
       "      <td>17.739726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>81</td>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType</th>\n",
       "      <td>81</td>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish</th>\n",
       "      <td>81</td>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual</th>\n",
       "      <td>81</td>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond</th>\n",
       "      <td>81</td>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <td>38</td>\n",
       "      <td>2.602740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure</th>\n",
       "      <td>38</td>\n",
       "      <td>2.602740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <td>37</td>\n",
       "      <td>2.534247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond</th>\n",
       "      <td>37</td>\n",
       "      <td>2.534247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>37</td>\n",
       "      <td>2.534247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>8</td>\n",
       "      <td>0.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>8</td>\n",
       "      <td>0.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>1</td>\n",
       "      <td>0.068493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total missed values % of missed values\n",
       "PoolQC                      1453          99.520548\n",
       "MiscFeature                 1406          96.301370\n",
       "Alley                       1369          93.767123\n",
       "Fence                       1179          80.753425\n",
       "FireplaceQu                  690          47.260274\n",
       "LotFrontage                  259          17.739726\n",
       "GarageYrBlt                   81           5.547945\n",
       "GarageType                    81           5.547945\n",
       "GarageFinish                  81           5.547945\n",
       "GarageQual                    81           5.547945\n",
       "GarageCond                    81           5.547945\n",
       "BsmtFinType2                  38           2.602740\n",
       "BsmtExposure                  38           2.602740\n",
       "BsmtFinType1                  37           2.534247\n",
       "BsmtCond                      37           2.534247\n",
       "BsmtQual                      37           2.534247\n",
       "MasVnrArea                     8           0.547945\n",
       "MasVnrType                     8           0.547945\n",
       "Electrical                     1           0.068493"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miss(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of this DataFrame is (1338, 38)\n"
     ]
    },
    {
     "data": {
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       "<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>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>...</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>Functional</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
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       "      <th>2</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "      <td>...</td>\n",
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       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSZoning  Street  LotShape  LandContour  Utilities  LotConfig  LandSlope  \\\n",
       "0         3       1         3            3          0          4          0   \n",
       "1         3       1         3            3          0          2          0   \n",
       "2         3       1         0            3          0          4          0   \n",
       "3         3       1         0            3          0          0          0   \n",
       "4         3       1         0            3          0          2          0   \n",
       "\n",
       "   Neighborhood  Condition1  Condition2      ...        Electrical  \\\n",
       "0             5           2           2      ...                 4   \n",
       "1            24           1           2      ...                 4   \n",
       "2             5           2           2      ...                 4   \n",
       "3             6           2           2      ...                 4   \n",
       "4            15           2           2      ...                 4   \n",
       "\n",
       "   KitchenQual  Functional  GarageType  GarageFinish  GarageQual  GarageCond  \\\n",
       "0            2           6           1             1           4           4   \n",
       "1            3           6           1             1           4           4   \n",
       "2            2           6           1             1           4           4   \n",
       "3            2           6           5             2           4           4   \n",
       "4            2           6           1             1           4           4   \n",
       "\n",
       "   PavedDrive  SaleType  SaleCondition  \n",
       "0           2         8              4  \n",
       "1           2         8              4  \n",
       "2           2         8              4  \n",
       "3           2         8              0  \n",
       "4           2         8              4  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LabelEncoding categorical features using scikit-learn\n",
    "label = LabelEncoder()\n",
    "categ_features = data.select_dtypes('object')\n",
    "categ_features = categ_features.drop(columns = ['Alley', 'PoolQC', 'MiscFeature', 'Fence', 'FireplaceQu'])\n",
    "categ_features = categ_features.dropna()\n",
    "X = categ_features.apply(label.fit_transform)\n",
    "print(\"The shape of this DataFrame is \" + str(X.shape))\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In the X were 229 unique values and 38 columns\n",
      "\n",
      "The shape of X2 is (1338, 229)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 1., 0.],\n",
       "       [0., 0., 0., ..., 0., 1., 0.],\n",
       "       [0., 0., 0., ..., 0., 1., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 1., 0.],\n",
       "       [0., 0., 0., ..., 0., 1., 0.],\n",
       "       [0., 0., 0., ..., 0., 1., 0.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One-Hot Encoding categorical features using scikit-learn\n",
    "onehot = OneHotEncoder()\n",
    "onehot.fit(X)\n",
    "X2 = onehot.transform(X).toarray()\n",
    "print(\"In the X were \" + str(X.nunique().sum()) + \" unique values and \" + str(X.shape[1]) + \" columns\\n\")\n",
    "print(\"The shape of X2 is \" + str(X2.shape))\n",
    "X2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values of column 'LotConfig' are: \n",
      "['Inside' 'FR2' 'Corner' 'CulDSac' 'FR3']\n",
      "\n",
      "Encoded values: \n",
      "{0: 'Corner', 1: 'CulDSac', 2: 'FR2', 3: 'FR3', 4: 'Inside'}\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>...</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>4</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>RFn</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>...</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>RFn</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>4</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>RFn</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>0</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>Unf</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>RFn</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  MSZoning Street Alley LotShape LandContour Utilities  LotConfig LandSlope  \\\n",
       "0       RL   Pave   NaN      Reg         Lvl    AllPub          4       Gtl   \n",
       "1       RL   Pave   NaN      Reg         Lvl    AllPub          2       Gtl   \n",
       "2       RL   Pave   NaN      IR1         Lvl    AllPub          4       Gtl   \n",
       "3       RL   Pave   NaN      IR1         Lvl    AllPub          0       Gtl   \n",
       "4       RL   Pave   NaN      IR1         Lvl    AllPub          2       Gtl   \n",
       "\n",
       "  Neighborhood Condition1      ...      GarageType GarageFinish GarageQual  \\\n",
       "0      CollgCr       Norm      ...          Attchd          RFn         TA   \n",
       "1      Veenker      Feedr      ...          Attchd          RFn         TA   \n",
       "2      CollgCr       Norm      ...          Attchd          RFn         TA   \n",
       "3      Crawfor       Norm      ...          Detchd          Unf         TA   \n",
       "4      NoRidge       Norm      ...          Attchd          RFn         TA   \n",
       "\n",
       "  GarageCond PavedDrive PoolQC Fence MiscFeature SaleType SaleCondition  \n",
       "0         TA          Y    NaN   NaN         NaN       WD        Normal  \n",
       "1         TA          Y    NaN   NaN         NaN       WD        Normal  \n",
       "2         TA          Y    NaN   NaN         NaN       WD        Normal  \n",
       "3         TA          Y    NaN   NaN         NaN       WD       Abnorml  \n",
       "4         TA          Y    NaN   NaN         NaN       WD        Normal  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categ_features = data.select_dtypes('object')\n",
    "print(\"Unique values of column 'LotConfig' are: \\n\" + str(categ_features['LotConfig'].unique()))\n",
    "label = LabelEncoder()\n",
    "label.fit(categ_features['LotConfig'].unique())\n",
    "categ_features['LotConfig'] = pd.Series(label.fit_transform(data['LotConfig']))\n",
    "print(\"\\nEncoded values: \")\n",
    "print(dict(enumerate(label.classes_)))\n",
    "categ_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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