RealDealOnRealEstate.ipynb

{
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   },
   "outputs": [],
   "source": [
    "# import visualizing tools\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "pd.options.display.max_rows = 99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 256
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    "colab_type": "code",
    "id": "I6OBpPY36_5v",
    "outputId": "266d3771-3fdc-4389-eee6-caad253ba0a5"
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities    ...     PoolArea PoolQC Fence MiscFeature MiscVal  \\\n",
       "0         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "1         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "2         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "3         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "4         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "\n",
       "  MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0      2   2008        WD         Normal     208500  \n",
       "1      5   2007        WD         Normal     181500  \n",
       "2      9   2008        WD         Normal     223500  \n",
       "3      2   2006        WD        Abnorml     140000  \n",
       "4     12   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# read data\n",
    "train_df = pd.read_csv(r'C:\\Users\\samee\\Google Drive\\College\\COMP 562\\Final Project\\house-prices-advanced-regression-techniques\\train.csv')\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "S1VNCttT7IY4",
    "outputId": "f6854552-22d2-4a60-a98e-f39c9f211e80"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view how many fields are present\n",
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
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       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0  1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
       "1  1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
       "2  1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
       "3  1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
       "4  1465         120       RL         43.0     5005   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities      ...       ScreenPorch PoolArea PoolQC  Fence  \\\n",
       "0         Lvl    AllPub      ...               120        0    NaN  MnPrv   \n",
       "1         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "2         Lvl    AllPub      ...                 0        0    NaN  MnPrv   \n",
       "3         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "4         HLS    AllPub      ...               144        0    NaN    NaN   \n",
       "\n",
       "  MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0         NaN       0      6    2010        WD         Normal  \n",
       "1        Gar2   12500      6    2010        WD         Normal  \n",
       "2         NaN       0      3    2010        WD         Normal  \n",
       "3         NaN       0      6    2010        WD         Normal  \n",
       "4         NaN       0      1    2010        WD         Normal  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read test data\n",
    "test_df = pd.read_csv('https://raw.githubusercontent.com/hybrezz54/comp562-final-proj/master/data/test.csv')\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 330
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    "colab_type": "code",
    "id": "ceAVlDTU7dzo",
    "outputId": "9d021f8d-e951-414d-b63f-f4489377ffdd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
       "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
       "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
       "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
       "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
       "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
       "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
       "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
       "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
       "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
       "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
       "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
       "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
       "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
       "       'SaleCondition', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# features present in the data\n",
    "# info on features can be found in the github repo\n",
    "# TODO: remove columns with a lot of missing data\n",
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1441
    },
    "colab_type": "code",
    "id": "0FV2KGz17fu-",
    "outputId": "c360d559-fc47-43a0-a77e-869bd157f3cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PoolQC           1453\n",
       "MiscFeature      1406\n",
       "Alley            1369\n",
       "Fence            1179\n",
       "FireplaceQu       690\n",
       "LotFrontage       259\n",
       "GarageCond         81\n",
       "GarageType         81\n",
       "GarageYrBlt        81\n",
       "GarageFinish       81\n",
       "GarageQual         81\n",
       "BsmtExposure       38\n",
       "BsmtFinType2       38\n",
       "BsmtFinType1       37\n",
       "BsmtCond           37\n",
       "BsmtQual           37\n",
       "MasVnrArea          8\n",
       "MasVnrType          8\n",
       "Electrical          1\n",
       "Utilities           0\n",
       "YearRemodAdd        0\n",
       "MSSubClass          0\n",
       "Foundation          0\n",
       "ExterCond           0\n",
       "ExterQual           0\n",
       "Exterior2nd         0\n",
       "Exterior1st         0\n",
       "RoofMatl            0\n",
       "RoofStyle           0\n",
       "YearBuilt           0\n",
       "LotConfig           0\n",
       "OverallCond         0\n",
       "OverallQual         0\n",
       "HouseStyle          0\n",
       "BldgType            0\n",
       "Condition2          0\n",
       "BsmtFinSF1          0\n",
       "MSZoning            0\n",
       "LotArea             0\n",
       "Street              0\n",
       "Condition1          0\n",
       "Neighborhood        0\n",
       "LotShape            0\n",
       "LandContour         0\n",
       "LandSlope           0\n",
       "SalePrice           0\n",
       "HeatingQC           0\n",
       "BsmtFinSF2          0\n",
       "EnclosedPorch       0\n",
       "Fireplaces          0\n",
       "GarageCars          0\n",
       "GarageArea          0\n",
       "PavedDrive          0\n",
       "WoodDeckSF          0\n",
       "OpenPorchSF         0\n",
       "3SsnPorch           0\n",
       "BsmtUnfSF           0\n",
       "ScreenPorch         0\n",
       "PoolArea            0\n",
       "MiscVal             0\n",
       "MoSold              0\n",
       "YrSold              0\n",
       "SaleType            0\n",
       "Functional          0\n",
       "TotRmsAbvGrd        0\n",
       "KitchenQual         0\n",
       "KitchenAbvGr        0\n",
       "BedroomAbvGr        0\n",
       "HalfBath            0\n",
       "FullBath            0\n",
       "BsmtHalfBath        0\n",
       "BsmtFullBath        0\n",
       "GrLivArea           0\n",
       "LowQualFinSF        0\n",
       "2ndFlrSF            0\n",
       "1stFlrSF            0\n",
       "CentralAir          0\n",
       "SaleCondition       0\n",
       "Heating             0\n",
       "TotalBsmtSF         0\n",
       "Id                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see which columns contain mostly missing data\n",
    "train_df.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1441
    },
    "colab_type": "code",
    "id": "_sy0wkoB7hah",
    "outputId": "8b965121-587c-4987-e060-9c3adfcf38cb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                 int64\n",
       "MSSubClass         int64\n",
       "MSZoning          object\n",
       "LotFrontage      float64\n",
       "LotArea            int64\n",
       "Street            object\n",
       "Alley             object\n",
       "LotShape          object\n",
       "LandContour       object\n",
       "Utilities         object\n",
       "LotConfig         object\n",
       "LandSlope         object\n",
       "Neighborhood      object\n",
       "Condition1        object\n",
       "Condition2        object\n",
       "BldgType          object\n",
       "HouseStyle        object\n",
       "OverallQual        int64\n",
       "OverallCond        int64\n",
       "YearBuilt          int64\n",
       "YearRemodAdd       int64\n",
       "RoofStyle         object\n",
       "RoofMatl          object\n",
       "Exterior1st       object\n",
       "Exterior2nd       object\n",
       "MasVnrType        object\n",
       "MasVnrArea       float64\n",
       "ExterQual         object\n",
       "ExterCond         object\n",
       "Foundation        object\n",
       "BsmtQual          object\n",
       "BsmtCond          object\n",
       "BsmtExposure      object\n",
       "BsmtFinType1      object\n",
       "BsmtFinSF1         int64\n",
       "BsmtFinType2      object\n",
       "BsmtFinSF2         int64\n",
       "BsmtUnfSF          int64\n",
       "TotalBsmtSF        int64\n",
       "Heating           object\n",
       "HeatingQC         object\n",
       "CentralAir        object\n",
       "Electrical        object\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",
       "KitchenQual       object\n",
       "TotRmsAbvGrd       int64\n",
       "Functional        object\n",
       "Fireplaces         int64\n",
       "FireplaceQu       object\n",
       "GarageType        object\n",
       "GarageYrBlt      float64\n",
       "GarageFinish      object\n",
       "GarageCars         int64\n",
       "GarageArea         int64\n",
       "GarageQual        object\n",
       "GarageCond        object\n",
       "PavedDrive        object\n",
       "WoodDeckSF         int64\n",
       "OpenPorchSF        int64\n",
       "EnclosedPorch      int64\n",
       "3SsnPorch          int64\n",
       "ScreenPorch        int64\n",
       "PoolArea           int64\n",
       "PoolQC            object\n",
       "Fence             object\n",
       "MiscFeature       object\n",
       "MiscVal            int64\n",
       "MoSold             int64\n",
       "YrSold             int64\n",
       "SaleType          object\n",
       "SaleCondition     object\n",
       "SalePrice          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see which columns are numerical and categorical\n",
    "train_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 208
    },
    "colab_type": "code",
    "id": "uUNSqj8nBEra",
    "outputId": "d3571ccc-61ad-4e1e-e5a3-9a023c807559"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PoolQC          0.995205\n",
       "MiscFeature     0.963014\n",
       "Alley           0.937671\n",
       "Fence           0.807534\n",
       "FireplaceQu     0.472603\n",
       "LotFrontage     0.177397\n",
       "GarageCond      0.055479\n",
       "GarageType      0.055479\n",
       "GarageYrBlt     0.055479\n",
       "GarageFinish    0.055479\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(train_df.isnull().sum().sort_values(ascending=False)/1460)[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 208
    },
    "colab_type": "code",
    "id": "GTNRdCAn8HvW",
    "outputId": "4e4d9d93-248d-4e09-8947-f6df7fc25e53"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GarageType      0.055479\n",
       "GarageYrBlt     0.055479\n",
       "GarageFinish    0.055479\n",
       "GarageCond      0.055479\n",
       "GarageQual      0.055479\n",
       "BsmtExposure    0.026027\n",
       "BsmtFinType2    0.026027\n",
       "BsmtFinType1    0.025342\n",
       "BsmtCond        0.025342\n",
       "BsmtQual        0.025342\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop columns that are missing more than 10% of training data\n",
    "new_train_df = train_df.dropna(axis='columns', thresh=train_df.shape[0]*0.9)\n",
    "(new_train_df.isnull().sum().sort_values(ascending=False)/new_train_df.shape[0])[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 208
    },
    "colab_type": "code",
    "id": "x0-HlXmrOj33",
    "outputId": "9c29b5ea-6e2a-4d65-9653-a13d6eaf1173"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GarageYrBlt     0.053461\n",
       "GarageFinish    0.053461\n",
       "GarageCond      0.053461\n",
       "GarageQual      0.053461\n",
       "GarageType      0.052090\n",
       "BsmtCond        0.030843\n",
       "BsmtQual        0.030158\n",
       "BsmtExposure    0.030158\n",
       "BsmtFinType1    0.028787\n",
       "BsmtFinType2    0.028787\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop columns that are missing more than 10% of test data\n",
    "new_test_df = test_df.dropna(axis='columns', thresh=test_df.shape[0]*0.9)\n",
    "(new_test_df.isnull().sum().sort_values(ascending=False)/new_test_df.shape[0])[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 955
    },
    "colab_type": "code",
    "id": "SNbdEDmX-pYT",
    "outputId": "f3016bdf-c9cf-4832-c0c5-753c8028fd4e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSZoning  categories:  Index(['C (all)', 'FV', 'RH', 'RL', 'RM'], dtype='object')\n",
      "Street  categories:  Index(['Grvl', 'Pave'], dtype='object')\n",
      "LotShape  categories:  Index(['IR1', 'IR2', 'IR3', 'Reg'], dtype='object')\n",
      "LandContour  categories:  Index(['Bnk', 'HLS', 'Low', 'Lvl'], dtype='object')\n",
      "Utilities  categories:  Index(['AllPub', 'NoSeWa'], dtype='object')\n",
      "LotConfig  categories:  Index(['Corner', 'CulDSac', 'FR2', 'FR3', 'Inside'], dtype='object')\n",
      "LandSlope  categories:  Index(['Gtl', 'Mod', 'Sev'], dtype='object')\n",
      "Neighborhood  categories:  Index(['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr',\n",
      "       'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel',\n",
      "       'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU',\n",
      "       'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker'],\n",
      "      dtype='object')\n",
      "Condition1  categories:  Index(['Artery', 'Feedr', 'Norm', 'PosA', 'PosN', 'RRAe', 'RRAn', 'RRNe',\n",
      "       'RRNn'],\n",
      "      dtype='object')\n",
      "Condition2  categories:  Index(['Artery', 'Feedr', 'Norm', 'PosA', 'PosN', 'RRAe', 'RRAn', 'RRNn'], dtype='object')\n",
      "BldgType  categories:  Index(['1Fam', '2fmCon', 'Duplex', 'Twnhs', 'TwnhsE'], dtype='object')\n",
      "HouseStyle  categories:  Index(['1.5Fin', '1.5Unf', '1Story', '2.5Fin', '2.5Unf', '2Story', 'SFoyer',\n",
      "       'SLvl'],\n",
      "      dtype='object')\n",
      "RoofStyle  categories:  Index(['Flat', 'Gable', 'Gambrel', 'Hip', 'Mansard', 'Shed'], dtype='object')\n",
      "RoofMatl  categories:  Index(['ClyTile', 'CompShg', 'Membran', 'Metal', 'Roll', 'Tar&Grv', 'WdShake',\n",
      "       'WdShngl'],\n",
      "      dtype='object')\n",
      "Exterior1st  categories:  Index(['AsbShng', 'AsphShn', 'BrkComm', 'BrkFace', 'CBlock', 'CemntBd',\n",
      "       'HdBoard', 'ImStucc', 'MetalSd', 'Plywood', 'Stone', 'Stucco',\n",
      "       'VinylSd', 'Wd Sdng', 'WdShing'],\n",
      "      dtype='object')\n",
      "Exterior2nd  categories:  Index(['AsbShng', 'AsphShn', 'Brk Cmn', 'BrkFace', 'CBlock', 'CmentBd',\n",
      "       'HdBoard', 'ImStucc', 'MetalSd', 'Other', 'Plywood', 'Stone', 'Stucco',\n",
      "       'VinylSd', 'Wd Sdng', 'Wd Shng'],\n",
      "      dtype='object')\n",
      "MasVnrType  categories:  Index(['BrkCmn', 'BrkFace', 'None', 'Stone'], dtype='object')\n",
      "ExterQual  categories:  Index(['Ex', 'Fa', 'Gd', 'TA'], dtype='object')\n",
      "ExterCond  categories:  Index(['Ex', 'Fa', 'Gd', 'Po', 'TA'], dtype='object')\n",
      "Foundation  categories:  Index(['BrkTil', 'CBlock', 'PConc', 'Slab', 'Stone', 'Wood'], dtype='object')\n",
      "BsmtQual  categories:  Index(['Ex', 'Fa', 'Gd', 'TA'], dtype='object')\n",
      "BsmtCond  categories:  Index(['Fa', 'Gd', 'Po', 'TA'], dtype='object')\n",
      "BsmtExposure  categories:  Index(['Av', 'Gd', 'Mn', 'No'], dtype='object')\n",
      "BsmtFinType1  categories:  Index(['ALQ', 'BLQ', 'GLQ', 'LwQ', 'Rec', 'Unf'], dtype='object')\n",
      "BsmtFinType2  categories:  Index(['ALQ', 'BLQ', 'GLQ', 'LwQ', 'Rec', 'Unf'], dtype='object')\n",
      "Heating  categories:  Index(['Floor', 'GasA', 'GasW', 'Grav', 'OthW', 'Wall'], dtype='object')\n",
      "HeatingQC  categories:  Index(['Ex', 'Fa', 'Gd', 'Po', 'TA'], dtype='object')\n",
      "CentralAir  categories:  Index(['N', 'Y'], dtype='object')\n",
      "Electrical  categories:  Index(['FuseA', 'FuseF', 'FuseP', 'Mix', 'SBrkr'], dtype='object')\n",
      "KitchenQual  categories:  Index(['Ex', 'Fa', 'Gd', 'TA'], dtype='object')\n",
      "Functional  categories:  Index(['Maj1', 'Maj2', 'Min1', 'Min2', 'Mod', 'Sev', 'Typ'], dtype='object')\n",
      "GarageType  categories:  Index(['2Types', 'Attchd', 'Basment', 'BuiltIn', 'CarPort', 'Detchd'], dtype='object')\n",
      "GarageFinish  categories:  Index(['Fin', 'RFn', 'Unf'], dtype='object')\n",
      "GarageQual  categories:  Index(['Ex', 'Fa', 'Gd', 'Po', 'TA'], dtype='object')\n",
      "GarageCond  categories:  Index(['Ex', 'Fa', 'Gd', 'Po', 'TA'], dtype='object')\n",
      "PavedDrive  categories:  Index(['N', 'P', 'Y'], dtype='object')\n",
      "SaleType  categories:  Index(['COD', 'CWD', 'Con', 'ConLD', 'ConLI', 'ConLw', 'New', 'Oth', 'WD'], dtype='object')\n",
      "SaleCondition  categories:  Index(['Abnorml', 'AdjLand', 'Alloca', 'Family', 'Normal', 'Partial'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# find all categories for nonnumerical data\n",
    "from pandas.api.types import is_object_dtype\n",
    "for (colName, colData) in new_train_df.iteritems():\n",
    "    if is_object_dtype(colData):\n",
    "        print(colName, ' categories: ', colData.astype(\"category\").cat.categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 625
    },
    "colab_type": "code",
    "id": "ICt38YhjPL3q",
    "outputId": "6c5d7230-bc9e-436f-e81e-27e376b05399"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a717280438>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation matrix\n",
    "corrmat = new_train_df.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "sns.heatmap(corrmat, vmax=.8, square=True, cmap='BrBG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 368
    },
    "colab_type": "code",
    "id": "Rmp67xdoUlJ3",
    "outputId": "64c73b69-3bd8-4d99-cee1-8ca8c76e4112"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# saleprice correlation matrix with top 10 correlated features\n",
    "import numpy as np\n",
    "k = 10\n",
    "cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index\n",
    "cm = np.corrcoef(new_train_df[cols].values.T)\n",
    "sns.set(font_scale=1.25)\n",
    "hm = sns.heatmap(cm, cmap=\"BrBG\", cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 6155
    },
    "colab_type": "code",
    "id": "-5sJ9RRLQgTv",
    "outputId": "ec18e1e2-0be2-4fde-fe60-88f10800d11b",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sns.pairplot(data=new_train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 416
    },
    "colab_type": "code",
    "id": "Z7fIxNZMZgLl",
    "outputId": "9bbcb855-bfd1-47be-aff6-4d49f72503f0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\samee\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\samee\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SalePrice       0.0\n",
       "YearRemodAdd    0.0\n",
       "RoofMatl        0.0\n",
       "Exterior1st     0.0\n",
       "Exterior2nd     0.0\n",
       "MasVnrType      0.0\n",
       "MasVnrArea      0.0\n",
       "ExterQual       0.0\n",
       "ExterCond       0.0\n",
       "Foundation      0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill NA or missing values in data\n",
    "from pandas.api.types import is_numeric_dtype\n",
    "for (colName, colData) in new_train_df.iteritems():\n",
    "    if is_numeric_dtype(colData):\n",
    "        new_train_df[colName] = colData.fillna(colData.median())\n",
    "    else:\n",
    "        new_train_df[colName] = colData.fillna('Missing')\n",
    "        \n",
    "# verify no missing values left\n",
    "(new_train_df.isnull().sum().sort_values(ascending=False)/new_train_df.shape[0])[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 208
    },
    "colab_type": "code",
    "id": "AlawOYd_Zsp6",
    "outputId": "f40f3d36-6ad7-479b-9c5c-7ed8af7826ff"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\samee\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\samee\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3697: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "# create new feature to represent total square feet of house\n",
    "new_train_df['TotalSF'] = new_train_df['TotalBsmtSF'] + new_train_df['GrLivArea']\n",
    "\n",
    "# drop unneeded columns esp those that are redundant\n",
    "# drop GarageArea bc colinear w GarageCars and has lower correlation; same w 1stFlrSF and TotalBsmtSF\n",
    "# drop GrLivArea and TotalBsmtSF because TotalSF has a higher correlation (0.78) than both of them\n",
    "new_train_df.drop(['GarageArea', '1stFlrSF', 'GrLivArea', 'TotalBsmtSF'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 642
    },
    "colab_type": "code",
    "id": "J8Psqh0SZjbT",
    "outputId": "23077761-2a8b-44f3-89c8-27e05e5d6431"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation matrix\n",
    "corrmat = new_train_df.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "sns.heatmap(corrmat, cmap=\"YlGnBu\", vmax=.8, square=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "colab_type": "code",
    "id": "CzicaUMGZmD7",
    "outputId": "283d7b75-7c6e-46db-ce59-ff848a96c665"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = 10\n",
    "cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index\n",
    "cm = np.corrcoef(new_train_df[cols].values.T)\n",
    "sns.set(font_scale=1.25)\n",
    "hm = sns.heatmap(cm, cmap=\"YlGnBu\", cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 12}, yticklabels=cols.values, xticklabels=cols.values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 2409
    },
    "colab_type": "code",
    "id": "RsgElQQdZw_h",
    "outputId": "c71dc0c7-56cb-43dc-c565-d1187586525e"
   },
   "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>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>...</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>TotalSF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>0.216503</td>\n",
       "      <td>-0.359325</td>\n",
       "      <td>-0.116339</td>\n",
       "      <td>-0.270208</td>\n",
       "      <td>-0.068692</td>\n",
       "      <td>-0.087688</td>\n",
       "      <td>-1.599111</td>\n",
       "      <td>0.138777</td>\n",
       "      <td>0.347273</td>\n",
       "      <td>-0.008372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.704483</td>\n",
       "      <td>-0.359325</td>\n",
       "      <td>-0.116339</td>\n",
       "      <td>-0.270208</td>\n",
       "      <td>-0.068692</td>\n",
       "      <td>-0.087688</td>\n",
       "      <td>-0.489110</td>\n",
       "      <td>-0.614439</td>\n",
       "      <td>0.007288</td>\n",
       "      <td>-0.059386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.070361</td>\n",
       "      <td>-0.359325</td>\n",
       "      <td>-0.116339</td>\n",
       "      <td>-0.270208</td>\n",
       "      <td>-0.068692</td>\n",
       "      <td>-0.087688</td>\n",
       "      <td>0.990891</td>\n",
       "      <td>0.138777</td>\n",
       "      <td>0.536154</td>\n",
       "      <td>0.161672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.176048</td>\n",
       "      <td>4.092524</td>\n",
       "      <td>-0.116339</td>\n",
       "      <td>-0.270208</td>\n",
       "      <td>-0.068692</td>\n",
       "      <td>-0.087688</td>\n",
       "      <td>-1.599111</td>\n",
       "      <td>-1.367655</td>\n",
       "      <td>-0.515281</td>\n",
       "      <td>-0.121330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>0.563760</td>\n",
       "      <td>-0.359325</td>\n",
       "      <td>-0.116339</td>\n",
       "      <td>-0.270208</td>\n",
       "      <td>-0.068692</td>\n",
       "      <td>-0.087688</td>\n",
       "      <td>2.100892</td>\n",
       "      <td>0.138777</td>\n",
       "      <td>0.869843</td>\n",
       "      <td>0.935372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id MSZoning Street LotShape LandContour Utilities LotConfig LandSlope  \\\n",
       "0   1       RL   Pave      Reg         Lvl    AllPub    Inside       Gtl   \n",
       "1   2       RL   Pave      Reg         Lvl    AllPub       FR2       Gtl   \n",
       "2   3       RL   Pave      IR1         Lvl    AllPub    Inside       Gtl   \n",
       "3   4       RL   Pave      IR1         Lvl    AllPub    Corner       Gtl   \n",
       "4   5       RL   Pave      IR1         Lvl    AllPub       FR2       Gtl   \n",
       "\n",
       "  Neighborhood Condition1    ...    OpenPorchSF EnclosedPorch 3SsnPorch  \\\n",
       "0      CollgCr       Norm    ...       0.216503     -0.359325 -0.116339   \n",
       "1      Veenker      Feedr    ...      -0.704483     -0.359325 -0.116339   \n",
       "2      CollgCr       Norm    ...      -0.070361     -0.359325 -0.116339   \n",
       "3      Crawfor       Norm    ...      -0.176048      4.092524 -0.116339   \n",
       "4      NoRidge       Norm    ...       0.563760     -0.359325 -0.116339   \n",
       "\n",
       "  ScreenPorch  PoolArea   MiscVal    MoSold    YrSold SalePrice   TotalSF  \n",
       "0   -0.270208 -0.068692 -0.087688 -1.599111  0.138777  0.347273 -0.008372  \n",
       "1   -0.270208 -0.068692 -0.087688 -0.489110 -0.614439  0.007288 -0.059386  \n",
       "2   -0.270208 -0.068692 -0.087688  0.990891  0.138777  0.536154  0.161672  \n",
       "3   -0.270208 -0.068692 -0.087688 -1.599111 -1.367655 -0.515281 -0.121330  \n",
       "4   -0.270208 -0.068692 -0.087688  2.100892  0.138777  0.869843  0.935372  \n",
       "\n",
       "[5 rows x 72 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# standardize only numerical features first\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "dfs = []\n",
    "final_train_df = new_train_df.copy()\n",
    "for (colName, colData) in final_train_df.iteritems():\n",
    "    if is_numeric_dtype(colData) and colName != 'Id':\n",
    "        dfs.append(pd.DataFrame(scaler.fit_transform(final_train_df[[colName]]), columns=[colName]))\n",
    "        final_train_df.drop([colName], axis=1, inplace=True)\n",
    "\n",
    "dfs.insert(0, final_train_df)\n",
    "final_train_df = pd.concat(dfs, axis=1, sort=True)\n",
    "final_train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 256
    },
    "colab_type": "code",
    "id": "toJWl-sHZ4ad",
    "outputId": "8e7e116f-db7d-46b5-f225-b455c7ac8c7a"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</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>BsmtUnfSF</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",
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       "      <td>60</td>\n",
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       "      <td>20</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
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       "      <th>3</th>\n",
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       "      <td>9550</td>\n",
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       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 278 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  LotArea  OverallQual  OverallCond  YearBuilt  YearRemodAdd  \\\n",
       "0          60     8450            7            5       2003          2003   \n",
       "1          20     9600            6            8       1976          1976   \n",
       "2          60    11250            7            5       2001          2002   \n",
       "3          70     9550            7            5       1915          1970   \n",
       "4          60    14260            8            5       2000          2000   \n",
       "\n",
       "   MasVnrArea  BsmtFinSF1  BsmtFinSF2  BsmtUnfSF          ...            \\\n",
       "0       196.0         706           0        150          ...             \n",
       "1         0.0         978           0        284          ...             \n",
       "2       162.0         486           0        434          ...             \n",
       "3         0.0         216           0        540          ...             \n",
       "4       350.0         655           0        490          ...             \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 278 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encode all the categorial vars. Basically create dummy variables and create a 1, and 0 if they exist or not.\n",
    "final_train_df = pd.get_dummies(new_train_df.drop(['Id'], axis=1))\n",
    "final_train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 256
    },
    "colab_type": "code",
    "id": "SzYueb-tZ7CN",
    "outputId": "4b012bcf-5718-4cf2-e748-51f35c4acd43"
   },
   "outputs": [
    {
     "data": {
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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",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
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       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1461</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
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       "      <td>43.0</td>\n",
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       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0  1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
       "1  1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
       "2  1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
       "3  1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
       "4  1465         120       RL         43.0     5005   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities      ...       ScreenPorch PoolArea PoolQC  Fence  \\\n",
       "0         Lvl    AllPub      ...               120        0    NaN  MnPrv   \n",
       "1         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "2         Lvl    AllPub      ...                 0        0    NaN  MnPrv   \n",
       "3         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "4         HLS    AllPub      ...               144        0    NaN    NaN   \n",
       "\n",
       "  MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0         NaN       0      6    2010        WD         Normal  \n",
       "1        Gar2   12500      6    2010        WD         Normal  \n",
       "2         NaN       0      3    2010        WD         Normal  \n",
       "3         NaN       0      6    2010        WD         Normal  \n",
       "4         NaN       0      1    2010        WD         Normal  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read testing data\n",
    "test_df = pd.read_csv('https://raw.githubusercontent.com/hybrezz54/comp562-final-proj/master/data/test.csv')\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1853
    },
    "colab_type": "code",
    "id": "s3j-wFz4aFwr",
    "outputId": "0eb97460-8737-4d37-c309-1938b21a78fe"
   },
   "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",
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       "    }\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>MSSubClass</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>BsmtUnfSF</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_Missing</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",
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       "      <td>-0.874711</td>\n",
       "      <td>0.897861</td>\n",
       "      <td>-0.054877</td>\n",
       "      <td>0.400766</td>\n",
       "      <td>-0.439695</td>\n",
       "      <td>-1.214908</td>\n",
       "      <td>0.047057</td>\n",
       "      <td>1.063511</td>\n",
       "      <td>-0.297689</td>\n",
       "      <td>-0.339225</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.809646</td>\n",
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       "      <td>-0.497418</td>\n",
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       "      <td>0.773377</td>\n",
       "      <td>-0.297689</td>\n",
       "      <td>-0.954831</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <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>0.061351</td>\n",
       "      <td>0.032064</td>\n",
       "      <td>-0.054877</td>\n",
       "      <td>0.400766</td>\n",
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       "      <td>-0.526882</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.465443</td>\n",
       "      <td>-0.971808</td>\n",
       "      <td>1.337571</td>\n",
       "      <td>-0.497418</td>\n",
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       "      <td>0.394694</td>\n",
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       "      <td>-0.387160</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 267 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass   LotArea  OverallQual  OverallCond  YearBuilt  YearRemodAdd  \\\n",
       "0   -0.874711  0.363929    -0.751101     0.400766  -0.340945     -1.072885   \n",
       "1   -0.874711  0.897861    -0.054877     0.400766  -0.439695     -1.214908   \n",
       "2    0.061351  0.809646    -0.751101    -0.497418   0.844059      0.678742   \n",
       "3    0.061351  0.032064    -0.054877     0.400766   0.876976      0.678742   \n",
       "4    1.465443 -0.971808     1.337571    -0.497418   0.679475      0.394694   \n",
       "\n",
       "   MasVnrArea  BsmtFinSF1  BsmtFinSF2  BsmtUnfSF          ...            \\\n",
       "0   -0.563316    0.063428    0.517537  -0.650461          ...             \n",
       "1    0.047057    1.063511   -0.297689  -0.339225          ...             \n",
       "2   -0.563316    0.773377   -0.297689  -0.954831          ...             \n",
       "3   -0.450284    0.357958   -0.297689  -0.526882          ...             \n",
       "4   -0.563316   -0.387160   -0.297689   1.059048          ...             \n",
       "\n",
       "   SaleType_Missing  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                      0                      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                     1                      0  \n",
       "4                     0                     1                      0  \n",
       "\n",
       "[5 rows x 267 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preprocess the testing data\n",
    "# drop cols with more than 10% missing data\n",
    "# final_test_df = test_df.dropna(axis='columns', thresh=test_df.shape[0]*0.9)\n",
    "final_test_df = test_df.drop(['PoolQC', 'Alley', 'Fence', 'MiscFeature', 'FireplaceQu', 'LotFrontage'], axis='columns')\n",
    "\n",
    "# fill NA or missing values in data\n",
    "for (colName, colData) in final_test_df.iteritems():\n",
    "    if is_numeric_dtype(colData):\n",
    "        final_test_df[colName] = colData.fillna(colData.median())\n",
    "    else:\n",
    "        final_test_df[colName] = colData.fillna('Missing')\n",
    "        \n",
    "# create new feature to represent total square feet of house\n",
    "final_test_df['TotalSF'] = final_test_df['TotalBsmtSF'] + final_test_df['GrLivArea']\n",
    "\n",
    "# drop unneeded columns esp those that are redundant\n",
    "final_test_df.drop(['GarageArea', '1stFlrSF', 'GrLivArea', 'TotalBsmtSF'], axis=1, inplace=True)\n",
    "\n",
    "# standardize only numerical features first\n",
    "dfs = []\n",
    "for (colName, colData) in final_test_df.iteritems():\n",
    "    if is_numeric_dtype(colData) and colName != 'Id':\n",
    "        dfs.append(pd.DataFrame(scaler.fit_transform(final_test_df[[colName]]), columns=[colName]))\n",
    "        final_test_df.drop([colName], axis=1, inplace=True)\n",
    "\n",
    "dfs.insert(0, final_test_df)\n",
    "final_test_df = pd.concat(dfs, axis=1, sort=True)\n",
    "\n",
    "# one hot encode all the categorial vars\n",
    "final_test_df = pd.get_dummies(final_test_df.drop(['Id'], axis=1))\n",
    "final_test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 330
    },
    "colab_type": "code",
    "id": "DrHFu0VLaJ9k",
    "outputId": "88214291-b89d-4ec6-ab64-3039e4b153a0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Heating_Floor',\n",
       " 'Electrical_Missing',\n",
       " 'RoofMatl_Metal',\n",
       " 'Condition2_RRAe',\n",
       " 'Condition2_RRAn',\n",
       " 'Exterior1st_Stone',\n",
       " 'SalePrice',\n",
       " 'Exterior1st_ImStucc',\n",
       " 'Condition2_RRNn',\n",
       " 'RoofMatl_ClyTile',\n",
       " 'Heating_OthW',\n",
       " 'Exterior2nd_Other',\n",
       " 'RoofMatl_Membran',\n",
       " 'Utilities_NoSeWa',\n",
       " 'Electrical_Mix',\n",
       " 'HouseStyle_2.5Fin',\n",
       " 'RoofMatl_Roll',\n",
       " 'GarageQual_Ex']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print cols present in train data and not present in test data\n",
    "missing_test_cols = list(set(final_train_df.columns.values).difference(set(final_test_df.columns.values)))\n",
    "missing_test_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "colab_type": "code",
    "id": "Yjybh6CBaM6E",
    "outputId": "77f2d645-8c41-4d36-9191-e11bdab16837"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Functional_Missing',\n",
       " 'Exterior1st_Missing',\n",
       " 'MSZoning_Missing',\n",
       " 'Utilities_Missing',\n",
       " 'KitchenQual_Missing',\n",
       " 'Exterior2nd_Missing',\n",
       " 'SaleType_Missing']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print cols present in test data and not present in train data\n",
    "missing_train_cols = list(set(final_test_df.columns.values).difference(set(final_train_df.columns.values)))\n",
    "missing_train_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "EZ_9WPmRaPBY",
    "outputId": "c93dce07-6116-4666-f96f-e43554a04c3d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 278)\n",
      "(1459, 277)\n"
     ]
    }
   ],
   "source": [
    "# add cols present in train and not in test\n",
    "for col in missing_test_cols:\n",
    "    final_test_df[col] = '0'\n",
    "    \n",
    "# drop cols present in test and not in train\n",
    "final_test_df.drop(missing_train_cols, axis=1, inplace=True)\n",
    "final_test_df.drop(['SalePrice'], axis=1, inplace=True)\n",
    "\n",
    "# verify shape is same except for SalePrice col\n",
    "print(final_train_df.shape)\n",
    "print(final_test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 191
    },
    "colab_type": "code",
    "id": "33FYKED2aRa7",
    "outputId": "0e8a69f9-5f24-41ef-d40b-4a19c6909366"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-324838.83913859],\n",
       "       [-335926.01449806],\n",
       "       [-368992.59649292],\n",
       "       [-350493.33125977],\n",
       "       [ -30676.79490051],\n",
       "       [-287490.29182197],\n",
       "       [-263264.70698266],\n",
       "       [-360835.1090492 ],\n",
       "       [-282580.91355999],\n",
       "       [-253666.74880342]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run regression models\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lin_regr = LinearRegression()\n",
    "lin_regr.fit(final_train_df.drop(['SalePrice'], axis=1), final_train_df[['SalePrice']])\n",
    "predictions = lin_regr.predict(final_test_df)\n",
    "\n",
    "# The coefficients\n",
    "# print('Coefficients: \\n', lin_regr.coef_)\n",
    "\n",
    "# print first ten predictions\n",
    "predictions[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "rpIPxw73f6Qd",
    "outputId": "5aa150ec-5bb2-4de8-eb2d-3dbeb133e2f3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.88779435, 0.89555727, 0.82114421, 0.74748082, 0.88199316,\n",
       "       0.51806822])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# perform 6-fold cross validation\n",
    "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
    "from sklearn import metrics\n",
    "scores = cross_val_score(lin_regr, final_train_df.drop(['SalePrice'], axis=1), final_train_df[['SalePrice']], cv=6)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "5a35iLkwf86s",
    "outputId": "b8c0cd82-3400-4bf4-8e1f-973ad2292b61"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2a71bfce550>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make cross validated predictions\n",
    "predictions = cross_val_predict(lin_regr, final_train_df.drop(['SalePrice'], axis=1), final_train_df[['SalePrice']], cv=6)\n",
    "plt.scatter(final_train_df[['SalePrice']], predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "2f95QqcLf_FT",
    "outputId": "78e7e7b9-2a8e-4e79-bdf7-d3b572aeb368"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8021799607635589"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get accuracy of model from cross validation\n",
    "accuracy = metrics.r2_score(final_train_df[['SalePrice']], predictions)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>We will try to attack the classification problem using Ridge Regression and LASSO. We'll use some of the pre-processing and feature engineering we have already done.</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotArea</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>...</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>TotalSF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1778.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2658.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2557.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2560.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning  LotArea Street LotShape LandContour Utilities  \\\n",
       "0  1461          20       RH    11622   Pave      Reg         Lvl    AllPub   \n",
       "1  1462          20       RL    14267   Pave      IR1         Lvl    AllPub   \n",
       "2  1463          60       RL    13830   Pave      IR1         Lvl    AllPub   \n",
       "3  1464          60       RL     9978   Pave      IR1         Lvl    AllPub   \n",
       "4  1465         120       RL     5005   Pave      IR1         HLS    AllPub   \n",
       "\n",
       "  LotConfig LandSlope   ...   EnclosedPorch 3SsnPorch ScreenPorch PoolArea  \\\n",
       "0    Inside       Gtl   ...               0         0         120        0   \n",
       "1    Corner       Gtl   ...               0         0           0        0   \n",
       "2    Inside       Gtl   ...               0         0           0        0   \n",
       "3    Inside       Gtl   ...               0         0           0        0   \n",
       "4    Inside       Gtl   ...               0         0         144        0   \n",
       "\n",
       "  MiscVal  MoSold  YrSold  SaleType  SaleCondition TotalSF  \n",
       "0       0       6    2010        WD         Normal  1778.0  \n",
       "1   12500       6    2010        WD         Normal  2658.0  \n",
       "2       0       3    2010        WD         Normal  2557.0  \n",
       "3       0       6    2010        WD         Normal  2530.0  \n",
       "4       0       1    2010        WD         Normal  2560.0  \n",
       "\n",
       "[5 rows x 71 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preprocess the testing and training data, using previous feature engineering we did.\n",
    "# drop cols with more than 10% missing data\n",
    "# final_test_df = test_df.dropna(axis='columns', thresh=test_df.shape[0]*0.9)\n",
    "final_test_df = test_df.drop(['PoolQC', 'Alley', 'Fence', 'MiscFeature', 'FireplaceQu', 'LotFrontage'], axis='columns')\n",
    "\n",
    "# fill NA or missing values in data\n",
    "for (colName, colData) in final_test_df.iteritems():\n",
    "    if is_numeric_dtype(colData):\n",
    "        final_test_df[colName] = colData.fillna(colData.median())\n",
    "    else:\n",
    "        final_test_df[colName] = colData.fillna('Missing')\n",
    "        \n",
    "# create new feature to represent total square feet of house\n",
    "final_test_df['TotalSF'] = final_test_df['TotalBsmtSF'] + final_test_df['GrLivArea']\n",
    "\n",
    "# drop unneeded columns esp those that are redundant\n",
    "final_test_df.drop(['GarageArea', '1stFlrSF', 'GrLivArea', 'TotalBsmtSF'], axis=1, inplace=True)\n",
    "\n",
    "final_test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotArea</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>...</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>TotalSF</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>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>2566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>2524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>2706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>2473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>3343</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotArea Street LotShape LandContour Utilities  \\\n",
       "0   1          60       RL     8450   Pave      Reg         Lvl    AllPub   \n",
       "1   2          20       RL     9600   Pave      Reg         Lvl    AllPub   \n",
       "2   3          60       RL    11250   Pave      IR1         Lvl    AllPub   \n",
       "3   4          70       RL     9550   Pave      IR1         Lvl    AllPub   \n",
       "4   5          60       RL    14260   Pave      IR1         Lvl    AllPub   \n",
       "\n",
       "  LotConfig LandSlope   ...   3SsnPorch ScreenPorch PoolArea MiscVal MoSold  \\\n",
       "0    Inside       Gtl   ...           0           0        0       0      2   \n",
       "1       FR2       Gtl   ...           0           0        0       0      5   \n",
       "2    Inside       Gtl   ...           0           0        0       0      9   \n",
       "3    Corner       Gtl   ...           0           0        0       0      2   \n",
       "4       FR2       Gtl   ...           0           0        0       0     12   \n",
       "\n",
       "   YrSold  SaleType  SaleCondition  SalePrice TotalSF  \n",
       "0    2008        WD         Normal     208500    2566  \n",
       "1    2007        WD         Normal     181500    2524  \n",
       "2    2008        WD         Normal     223500    2706  \n",
       "3    2006        WD        Abnorml     140000    2473  \n",
       "4    2008        WD         Normal     250000    3343  \n",
       "\n",
       "[5 rows x 72 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_train_df = train_df.drop(['PoolQC', 'Alley', 'Fence', 'MiscFeature', 'FireplaceQu', 'LotFrontage'], axis='columns')\n",
    "\n",
    "# fill NA or missing values in data\n",
    "for (colName, colData) in final_train_df.iteritems():\n",
    "    if is_numeric_dtype(colData):\n",
    "        final_train_df[colName] = colData.fillna(colData.median())\n",
    "    else:\n",
    "        final_train_df[colName] = colData.fillna('Missing')\n",
    "        \n",
    "# create new feature to represent total square feet of house\n",
    "final_train_df['TotalSF'] = final_train_df['TotalBsmtSF'] + final_train_df['GrLivArea']\n",
    "\n",
    "# drop unneeded columns esp those that are redundant\n",
    "final_train_df.drop(['GarageArea', '1stFlrSF', 'GrLivArea', 'TotalBsmtSF'], axis=1, inplace=True)\n",
    "\n",
    "final_train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a71bef9e48>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_train_df['SalePrice'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a71bf89160>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.log1p(final_train_df['SalePrice']).hist() #aiming for nomality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import skew\n",
    "from scipy.stats.stats import pearsonr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We have both the testing and training datasets using the previous feature engineering.\n",
    "#We will implement a log transform to eliminate skewness in variables that are not normal.\n",
    "\n",
    "#transform the target variable\n",
    "final_train_df['SalePrice'] = np.log1p(final_train_df['SalePrice'])\n",
    "\n",
    "#log transform the skewed numeric features, doing the training dataset first.\n",
    "numeric_feats = final_train_df.dtypes[final_train_df.dtypes != \"object\"].index\n",
    "\n",
    "skewed_feats = final_train_df[numeric_feats].apply(lambda x: skew(x.dropna())) #compute skewness\n",
    "skewed_feats = skewed_feats[skewed_feats > 0.6] #if skewness is greater than .6, then it's skewed\n",
    "skewed_feats = skewed_feats.index\n",
    "\n",
    "final_train_df[skewed_feats] = np.log1p(final_train_df[skewed_feats]) #calculate for the skewed for features > .75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#do the transformation of the test data\n",
    "\n",
    "#log transform the skewed numeric features, doing the training dataset first.\n",
    "numeric_feats = final_test_df.dtypes[final_test_df.dtypes != \"object\"].index\n",
    "\n",
    "skewed_feats = final_test_df[numeric_feats].apply(lambda x: skew(x.dropna())) #compute skewness\n",
    "skewed_feats = skewed_feats[skewed_feats > 0.6] #if skewness is greater than .6, then it's skewed\n",
    "skewed_feats = skewed_feats.index\n",
    "\n",
    "final_test_df[skewed_feats] = np.log1p(final_test_df[skewed_feats]) #calculate for the skewed for features > .75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the dummy variables for the categorical variables\n",
    "final_train_df = pd.get_dummies(final_train_df)\n",
    "final_test_df = pd.get_dummies(final_test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>MSSubClass</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_Missing</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>1461</td>\n",
       "      <td>3.044522</td>\n",
       "      <td>9.360741</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1961</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.150603</td>\n",
       "      <td>4.976734</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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>3.044522</td>\n",
       "      <td>9.565775</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1958</td>\n",
       "      <td>1958</td>\n",
       "      <td>4.691348</td>\n",
       "      <td>6.828712</td>\n",
       "      <td>0.000000</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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>9.534668</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1997</td>\n",
       "      <td>1998</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.674561</td>\n",
       "      <td>0.000000</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>1464</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>9.208238</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1998</td>\n",
       "      <td>3.044522</td>\n",
       "      <td>6.401917</td>\n",
       "      <td>0.000000</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",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>4.795791</td>\n",
       "      <td>8.518392</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1992</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.575949</td>\n",
       "      <td>0.000000</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 × 268 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass   LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0  1461    3.044522  9.360741            5            6       1961   \n",
       "1  1462    3.044522  9.565775            6            6       1958   \n",
       "2  1463    4.110874  9.534668            5            5       1997   \n",
       "3  1464    4.110874  9.208238            6            6       1998   \n",
       "4  1465    4.795791  8.518392            8            5       1992   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2          ...            \\\n",
       "0          1961    0.000000    6.150603    4.976734          ...             \n",
       "1          1958    4.691348    6.828712    0.000000          ...             \n",
       "2          1998    0.000000    6.674561    0.000000          ...             \n",
       "3          1998    3.044522    6.401917    0.000000          ...             \n",
       "4          1992    0.000000    5.575949    0.000000          ...             \n",
       "\n",
       "   SaleType_Missing  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                      0                      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                     1                      0  \n",
       "4                     0                     1                      0  \n",
       "\n",
       "[5 rows x 268 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jLiVyf1-JlPR"
   },
   "outputs": [],
   "source": [
    "#creating matrices for sklearn:\n",
    "X_train = final_train_df.drop(['SalePrice'], axis=1)\n",
    "X_test = final_test_df\n",
    "y = final_train_df.SalePrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5qeENl1QKRre"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "def rmse_cv(model):\n",
    "    rmse= np.sqrt(-cross_val_score(model, X_train, y, scoring=\"neg_mean_squared_error\", cv = 6))\n",
    "    return(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab_type": "text",
    "id": "PektO4XQR_UN"
   },
   "outputs": [],
   "source": [
    "model_ridge = Ridge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sgG338f3ao5M"
   },
   "outputs": [],
   "source": [
    "alphas = [0.05, 0.1, 0.3, 1, 3, 5, 10, 15, 30] #we're testing different values of alpha\n",
    "\n",
    "cv_ridge = [rmse_cv(Ridge(alpha = i)).mean() \n",
    "            for i in alphas]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05     0.134385\n",
       "0.10     0.133572\n",
       "0.30     0.131503\n",
       "1.00     0.128741\n",
       "3.00     0.126937\n",
       "5.00     0.126672\n",
       "10.00    0.127312\n",
       "15.00    0.128379\n",
       "30.00    0.131510\n",
       "dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_ridge = pd.Series(cv_ridge, index = alphas)\n",
    "cv_ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12667224873878621"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_ridge.min() #we see that the alpha value here is 5 that gives us the lowest alpha error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now we're going to test LASSO. LASSO has the freedom to choose it's own predictors.\n",
    "#So from the predictors we have let's see what it will use.\n",
    "lasso_model = LassoCV(alphas = [1, 0.1, 0.001, 0.0005]).fit(X_train, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12363222833761948"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_cv(lasso_model).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "coef = pd.Series(lasso_model.coef_, index = X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso used 111 variables and eliminated 167 variables\n"
     ]
    }
   ],
   "source": [
    "#using this function we'll see which values lasso did and did not pick\n",
    "print(\"Lasso used \" + str(sum(coef != 0)) + \" variables and eliminated \" +  str(sum(coef == 0)) + \" variables\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_bottom_coefficients = pd.concat([coef.sort_values().head(10),\n",
    "                     coef.sort_values().tail(10)])\n",
    "\n",
    "#let's just take a look at the top 10 of the coefficients, and the bottom 10 of the coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'The coeffiecients used in the lasso model')"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure.figsize = (10.0, 10)\n",
    "\n",
    "top_bottom_coefficients.plot(kind = \"barh\")\n",
    "plt.title(\"The coeffiecients used in the lasso model\")\n",
    "\n",
    "#we can see here that TotalSquareFeet is extremely important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a7218f5ef0>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets analyze the residuals and the residuals look scattered and there's no pattern to them. Thats a good thing.\n",
    "#Suggests normality.\n",
    "plt.figure.figsize = (10,10)\n",
    "\n",
    "predictions = pd.DataFrame({\"predictions\":lasso_model.predict(X_train), \"true\":y})\n",
    "predictions[\"residuals\"] = predictions[\"true\"] - predictions[\"predictions\"]\n",
    "predictions.plot(x = \"predictions\", y = \"residuals\",kind = \"scatter\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "RealDealOnRealEstate.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}