Kaggle Competition - Predicting House Prices Using PCA.ipynb

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    "# House Price Prediction : Kaggle\n",
    "\n",
    "Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.\n",
    "\n",
    "With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.\n",
    "\n",
    "### Goal\n",
    "It is your job to predict the sales price for each house. For each Id in the test set, you must predict the value of the SalePrice variable. \n",
    "\n",
    "### Metric\n",
    "Submissions are evaluated on Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted value and the logarithm of the observed sales price.\n",
    "\n",
    "### Data fields\n",
    "Here's a brief version of what you'll find in the data description file.\n",
    "\n",
    "1. SalePrice - the property's sale price in dollars. This is the target variable that you're trying to predict.\n",
    "2. MSSubClass: The building class\n",
    "3. MSZoning: The general zoning classification\n",
    "4. LotFrontage: Linear feet of street connected to property\n",
    "5. LotArea: Lot size in square feet\n",
    "6. Street: Type of road access\n",
    "7. Alley: Type of alley access\n",
    "8. LotShape: General shape of property\n",
    "9. LandContour: Flatness of the property\n",
    "10. Utilities: Type of utilities available\n",
    "11. LotConfig: Lot configuration\n",
    "12. LandSlope: Slope of property\n",
    "13. Neighborhood: Physical locations within Ames city limits\n",
    "14. Condition1: Proximity to main road or railroad\n",
    "15. Condition2: Proximity to main road or railroad (if a second is present)\n",
    "\n",
    "BldgType: Type of dwelling\n",
    "\n",
    "HouseStyle: Style of dwelling\n",
    "\n",
    "OverallQual: Overall material and finish quality\n",
    "\n",
    "OverallCond: Overall condition rating\n",
    "\n",
    "YearBuilt: Original construction date\n",
    "\n",
    "YearRemodAdd: Remodel date\n",
    "\n",
    "RoofStyle: Type of roof\n",
    "\n",
    "RoofMatl: Roof material\n",
    "\n",
    "Exterior1st: Exterior covering on house\n",
    "\n",
    "Exterior2nd: Exterior covering on house (if more than one material)\n",
    "\n",
    "MasVnrType: Masonry veneer type\n",
    "\n",
    "MasVnrArea: Masonry veneer area in square feet\n",
    "\n",
    "ExterQual: Exterior material quality\n",
    "\n",
    "ExterCond: Present condition of the material on the exterior\n",
    "\n",
    "Foundation: Type of foundation\n",
    "\n",
    "BsmtQual: Height of the basement\n",
    "\n",
    "\n",
    "BsmtCond: General condition of the basement\n",
    "\n",
    "BsmtExposure: Walkout or garden level basement walls\n",
    "\n",
    "BsmtFinType1: Quality of basement finished area\n",
    "\n",
    "BsmtFinSF1: Type 1 finished square feet\n",
    "\n",
    "BsmtFinType2: Quality of second finished area (if present)\n",
    "\n",
    "BsmtFinSF2: Type 2 finished square feet\n",
    "\n",
    "BsmtUnfSF: Unfinished square feet of basement area\n",
    "\n",
    "TotalBsmtSF: Total square feet of basement area\n",
    "\n",
    "Heating: Type of heating\n",
    "\n",
    "HeatingQC: Heating quality and condition\n",
    "\n",
    "CentralAir: Central air conditioning\n",
    "\n",
    "Electrical: Electrical system\n",
    "\n",
    "1stFlrSF: First Floor square feet\n",
    "\n",
    "2ndFlrSF: Second floor square feet\n",
    "\n",
    "LowQualFinSF: Low quality finished square feet (all floors)\n",
    "\n",
    "GrLivArea: Above grade (ground) living area square feet\n",
    "\n",
    "BsmtFullBath: Basement full bathrooms\n",
    "\n",
    "BsmtHalfBath: Basement half bathrooms\n",
    "\n",
    "FullBath: Full bathrooms above grade\n",
    "\n",
    "HalfBath: Half baths above grade\n",
    "\n",
    "Bedroom: Number of bedrooms above basement level\n",
    "\n",
    "Kitchen: Number of kitchens\n",
    "\n",
    "KitchenQual: Kitchen quality\n",
    "\n",
    "TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)\n",
    "\n",
    "Functional: Home functionality rating\n",
    "\n",
    "Fireplaces: Number of fireplaces\n",
    "\n",
    "FireplaceQu: Fireplace quality\n",
    "\n",
    "GarageType: Garage location\n",
    "\n",
    "GarageYrBlt: Year garage was built\n",
    "\n",
    "GarageFinish: Interior finish of the garage\n",
    "\n",
    "GarageCars: Size of garage in car capacity\n",
    "\n",
    "GarageArea: Size of garage in square feet\n",
    "\n",
    "GarageQual: Garage quality\n",
    "\n",
    "GarageCond: Garage condition\n",
    "\n",
    "PavedDrive: Paved driveway\n",
    "\n",
    "WoodDeckSF: Wood deck area in square feet\n",
    "\n",
    "OpenPorchSF: Open porch area in square feet\n",
    "\n",
    "EnclosedPorch: Enclosed porch area in square feet\n",
    "\n",
    "3SsnPorch: Three season porch area in square feet\n",
    "\n",
    "ScreenPorch: Screen porch area in square feet\n",
    "\n",
    "PoolArea: Pool area in square feet\n",
    "\n",
    "PoolQC: Pool quality\n",
    "\n",
    "Fence: Fence quality\n",
    "\n",
    "MiscFeature: Miscellaneous feature not covered in other categories\n",
    "\n",
    "MiscVal: $Value of miscellaneous feature\n",
    "\n",
    "MoSold: Month Sold\n",
    "\n",
    "YrSold: Year Sold\n",
    "\n",
    "SaleType: Type of sale\n",
    "\n",
    "SaleCondition: Condition of sale"
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder # Col Normalization\n",
    "from sklearn.decomposition import PCA # Reducing the Dimensions and Finding the Optimal Components\n",
    "from sklearn.ensemble import BaggingRegressor, RandomForestRegressor, AdaBoostRegressor # Ensemble\n",
    "from sklearn.tree import DecisionTreeRegressor # Decision Tree\n",
    "from sklearn.linear_model import LinearRegression # Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the train and test dataset\n",
    "train = pd.read_csv(\"C://Users//Administrator//Downloads//Unsupervised ML - Mukul Chauhan//train.csv\")\n",
    "test = pd.read_csv(\"C://Users//Administrator//Downloads//Unsupervised ML - Mukul Chauhan//test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb50acf8>"
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the Missing Values\n",
    "plt.figure(figsize=[8,5])\n",
    "sns.heatmap(train.isnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding on Missing Values\n",
    "\n",
    "The columns that have missing values are Lot Frontage, Pool QC, Fireplace QC, Alley, Basement FinType 01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb514a90>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test Data Missing Values\n",
    "sns.heatmap(test.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find if the categorical variables have \"?\" or not.Generally Cat Variables have the \"?\" as missing values and hence we should find if any of the variable has missing values in form of Q Mark\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(train.MSZoning==\"@\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding if the cols have QMark\n",
    "cols = ['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']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSZoning : 0\n",
      "Street : 0\n",
      "Alley : 0\n",
      "LotShape : 0\n",
      "LandContour : 0\n",
      "Utilities : 0\n",
      "LotConfig : 0\n",
      "LandSlope : 0\n",
      "Neighborhood : 0\n",
      "Condition1 : 0\n",
      "Condition2 : 0\n",
      "BldgType : 0\n",
      "HouseStyle : 0\n",
      "RoofStyle : 0\n",
      "RoofMatl : 0\n",
      "Exterior1st : 0\n",
      "Exterior2nd : 0\n",
      "MasVnrType : 0\n",
      "ExterQual : 0\n",
      "ExterCond : 0\n",
      "Foundation : 0\n",
      "BsmtQual : 0\n",
      "BsmtCond : 0\n",
      "BsmtExposure : 0\n",
      "BsmtFinType1 : 0\n",
      "BsmtFinType2 : 0\n",
      "Heating : 0\n",
      "HeatingQC : 0\n",
      "CentralAir : 0\n",
      "Electrical : 0\n",
      "KitchenQual : 0\n",
      "Functional : 0\n",
      "FireplaceQu : 0\n",
      "GarageType : 0\n",
      "GarageFinish : 0\n",
      "GarageQual : 0\n",
      "GarageCond : 0\n",
      "PavedDrive : 0\n",
      "PoolQC : 0\n",
      "Fence : 0\n",
      "MiscFeature : 0\n",
      "SaleType : 0\n",
      "SaleCondition : 0\n"
     ]
    }
   ],
   "source": [
    "for i in cols:\n",
    "    if np.dtype(train[i]).name==\"object\":\n",
    "        print(i, \":\", sum(train[i]==\"?\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remove Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>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": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Grvl    50\n",
       "Pave    41\n",
       "Name: Alley, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Alley.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gd    3\n",
       "Fa    2\n",
       "Ex    2\n",
       "Name: PoolQC, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.PoolQC.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id                  0\n",
      "MSSubClass          0\n",
      "MSZoning            0\n",
      "LotFrontage         0\n",
      "LotArea             0\n",
      "Street              0\n",
      "Alley            1369\n",
      "LotShape            0\n",
      "LandContour         0\n",
      "Utilities           0\n",
      "LotConfig           0\n",
      "LandSlope           0\n",
      "Neighborhood        0\n",
      "Condition1          0\n",
      "Condition2          0\n",
      "BldgType            0\n",
      "HouseStyle          0\n",
      "OverallQual         0\n",
      "OverallCond         0\n",
      "YearBuilt           0\n",
      "YearRemodAdd        0\n",
      "RoofStyle           0\n",
      "RoofMatl            0\n",
      "Exterior1st         0\n",
      "Exterior2nd         0\n",
      "MasVnrType          8\n",
      "MasVnrArea          0\n",
      "ExterQual           0\n",
      "ExterCond           0\n",
      "Foundation          0\n",
      "                 ... \n",
      "BedroomAbvGr        0\n",
      "KitchenAbvGr        0\n",
      "KitchenQual         0\n",
      "TotRmsAbvGrd        0\n",
      "Functional          0\n",
      "Fireplaces          0\n",
      "FireplaceQu       690\n",
      "GarageType         81\n",
      "GarageYrBlt         0\n",
      "GarageFinish       81\n",
      "GarageCars          0\n",
      "GarageArea          0\n",
      "GarageQual         81\n",
      "GarageCond         81\n",
      "PavedDrive          0\n",
      "WoodDeckSF          0\n",
      "OpenPorchSF         0\n",
      "EnclosedPorch       0\n",
      "3SsnPorch           0\n",
      "ScreenPorch         0\n",
      "PoolArea            0\n",
      "PoolQC           1453\n",
      "Fence            1179\n",
      "MiscFeature      1406\n",
      "MiscVal             0\n",
      "MoSold              0\n",
      "YrSold              0\n",
      "SaleType            0\n",
      "SaleCondition       0\n",
      "SalePrice           0\n",
      "Length: 81, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Grvl    50\n",
       "Pave    41\n",
       "Name: Alley, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Alley.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MnPrv    157\n",
       "GdPrv     59\n",
       "GdWo      54\n",
       "MnWw      11\n",
       "Name: Fence, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Fence.value_counts() # Keep Fence and Impute MnPrv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        41.000000\n",
       "mean     168000.585366\n",
       "std       38370.375243\n",
       "min       40000.000000\n",
       "25%      151000.000000\n",
       "50%      172500.000000\n",
       "75%      185000.000000\n",
       "max      265979.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train.Alley==\"Pave\"][\"SalePrice\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        59.000000\n",
       "mean     178927.457627\n",
       "std       56757.237563\n",
       "min      108000.000000\n",
       "25%      144950.000000\n",
       "50%      167500.000000\n",
       "75%      189475.000000\n",
       "max      475000.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train.Fence==\"GdPrv\"][\"SalePrice\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imputing the Missing Values with Median | Numerical Columns\n",
    "train.fillna(train.median(), inplace=True)\n",
    "test.fillna(test.median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_copy = train # making back up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Alley\n",
    "plt.figure(figsize=[10,10])\n",
    "sns.boxplot(x = \"YearBuilt\", y = \"SalePrice\", data = train[train.YearBuilt>1980], \n",
    "           hue = \"Alley\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:543: 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",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "# Imputting Alley as Pave\n",
    "train_copy = train\n",
    "train[train.YearBuilt>1999].loc[:, \"Alley\"] = train[\"Alley\"].fillna(\"Pave\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train_copy\n",
    "#train.Alley.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "cndtn = train[train.YearBuilt>1999].loc[:, \"Alley\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[train.YearBuilt<=1999, \"Alley\"] = train.loc[train.YearBuilt<=1999, \"Alley\"].fillna(\"Grvl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pave    1389\n",
       "Grvl      70\n",
       "Name: Alley, dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.Alley.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[train.YearBuilt>1999, \"Alley\"] = train.loc[train.YearBuilt>1999, \"Alley\"].fillna(\"Pave\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.loc[test.YearBuilt>1999, \"Alley\"] = test.loc[test.YearBuilt>1999, \"Alley\"].fillna(\"Pave\")\n",
    "test.loc[test.YearBuilt<=1999, \"Alley\"] = test.loc[test.YearBuilt<=1999, \"Alley\"].fillna(\"Grvl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train.isnull().sum() \n",
    "\n",
    "# FireplaceQu, GarageType, Garage Finish, GarageQual, GarageCond"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TA    1326\n",
       "Fa      35\n",
       "Gd       9\n",
       "Po       7\n",
       "Ex       2\n",
       "Name: GarageCond, dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.GarageCond.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute gd in train.fireplaceqc\n",
    "train.FireplaceQu.fillna(\"Gd\", inplace=True)\n",
    "test.FireplaceQu.fillna(\"Gd\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute Attchd in train.garagetype\n",
    "train.GarageType.fillna(\"Attchd\", inplace=True)\n",
    "test.GarageType.fillna(\"Attchd\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute Attchd in train.garagefinish\n",
    "train.GarageFinish.fillna(\"Unf\", inplace=True)\n",
    "test.GarageFinish.fillna(\"Unf\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute Attchd in train.garageQual\n",
    "train.GarageQual.fillna(\"TA\", inplace=True)\n",
    "test.GarageQual.fillna(\"TA\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute Attchd in train.garageQual\n",
    "train.GarageCond.fillna(\"TA\", inplace=True)\n",
    "test.GarageCond.fillna(\"TA\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(newtrain.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "newtrain = train.drop([\"Id\",\"PoolQC\", \"Fence\", \"MiscFeature\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "newtest = test.drop([\"Id\",\"PoolQC\", \"Fence\", \"MiscFeature\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 77)\n",
      "(1459, 76)\n"
     ]
    }
   ],
   "source": [
    "print(newtrain.shape)\n",
    "print(newtest.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No    953\n",
       "Av    221\n",
       "Gd    134\n",
       "Mn    114\n",
       "Name: BsmtExposure, dtype: int64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newtrain.BsmtExposure.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute Attchd in newtrain.garageQual\n",
    "newtrain.BsmtQual.fillna(\"TA\", inplace=True)\n",
    "newtest.BsmtQual.fillna(\"TA\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute Attchd in newtrain.garageQual\n",
    "newtrain.MasVnrType.fillna(\"None\", inplace=True)\n",
    "newtest.MasVnrType.fillna(\"None\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute Attchd in newtrain.Bsmt\n",
    "newtrain.BsmtFinType1.fillna(\"Unf\", inplace=True)\n",
    "newtest.BsmtFinType1.fillna(\"Unf\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "newtrain.BsmtFinType2.fillna(\"Unf\", inplace=True)\n",
    "newtest.BsmtFinType2.fillna(\"Unf\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "newtrain.Electrical.fillna(\"SBrkr\", inplace=True)\n",
    "newtest.Electrical.fillna(\"SBrkr\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "newtrain.BsmtCond.fillna(\"TA\", inplace=True)\n",
    "newtest.BsmtCond.fillna(\"TA\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "newtrain.BsmtExposure.fillna(\"No\", inplace=True)\n",
    "newtest.BsmtExposure.fillna(\"No\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(newtest.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "newtest.fillna(newtest.median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSSubClass       0\n",
       "MSZoning         4\n",
       "LotFrontage      0\n",
       "LotArea          0\n",
       "Street           0\n",
       "Alley            0\n",
       "LotShape         0\n",
       "LandContour      0\n",
       "Utilities        0\n",
       "LotConfig        0\n",
       "LandSlope        0\n",
       "Neighborhood     0\n",
       "Condition1       0\n",
       "Condition2       0\n",
       "BldgType         0\n",
       "HouseStyle       0\n",
       "OverallQual      0\n",
       "OverallCond      0\n",
       "YearBuilt        0\n",
       "YearRemodAdd     0\n",
       "RoofStyle        0\n",
       "RoofMatl         0\n",
       "Exterior1st      0\n",
       "Exterior2nd      0\n",
       "MasVnrType       0\n",
       "MasVnrArea       0\n",
       "ExterQual        0\n",
       "ExterCond        0\n",
       "Foundation       0\n",
       "BsmtQual         0\n",
       "                ..\n",
       "BsmtFullBath     0\n",
       "BsmtHalfBath     0\n",
       "FullBath         0\n",
       "HalfBath         0\n",
       "BedroomAbvGr     0\n",
       "KitchenAbvGr     0\n",
       "KitchenQual      0\n",
       "TotRmsAbvGrd     0\n",
       "Functional       0\n",
       "Fireplaces       0\n",
       "FireplaceQu      0\n",
       "GarageType       0\n",
       "GarageYrBlt      0\n",
       "GarageFinish     0\n",
       "GarageCars       0\n",
       "GarageArea       0\n",
       "GarageQual       0\n",
       "GarageCond       0\n",
       "PavedDrive       0\n",
       "WoodDeckSF       0\n",
       "OpenPorchSF      0\n",
       "EnclosedPorch    0\n",
       "3SsnPorch        0\n",
       "ScreenPorch      0\n",
       "PoolArea         0\n",
       "MiscVal          0\n",
       "MoSold           0\n",
       "YrSold           0\n",
       "SaleType         0\n",
       "SaleCondition    0\n",
       "Length: 76, dtype: int64"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newtest.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "newtest.MSZoning.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "newtest.MSZoning.fillna(\"RL\", inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply Standard Scaling and PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply Standard Scaling\n",
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying OHE\n",
    "newtrain_dummy = pd.get_dummies(newtrain)\n",
    "newtest_dummy = pd.get_dummies(newtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 278)\n",
      "(1459, 261)\n"
     ]
    }
   ],
   "source": [
    "print(newtrain_dummy.shape)\n",
    "print(newtest_dummy.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling the Dataset\n",
    "scaledtrain = sc.fit_transform(newtrain_dummy)\n",
    "scaledtest = sc.fit_transform(newtest_dummy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaledtrain_df = pd.DataFrame(scaledtrain, columns=newtrain_dummy.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaledtest_df = pd.DataFrame(scaledtest, columns=newtest_dummy.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "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>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.874711</td>\n",
       "      <td>0.567330</td>\n",
       "      <td>0.363929</td>\n",
       "      <td>-0.751101</td>\n",
       "      <td>0.400766</td>\n",
       "      <td>-0.340945</td>\n",
       "      <td>-1.072885</td>\n",
       "      <td>-0.563316</td>\n",
       "      <td>0.063428</td>\n",
       "      <td>0.517537</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.045392</td>\n",
       "      <td>-0.295268</td>\n",
       "      <td>-0.052432</td>\n",
       "      <td>0.398568</td>\n",
       "      <td>-0.254879</td>\n",
       "      <td>-0.074253</td>\n",
       "      <td>-0.091066</td>\n",
       "      <td>-0.134699</td>\n",
       "      <td>0.460211</td>\n",
       "      <td>-0.299365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.874711</td>\n",
       "      <td>0.615963</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>...</td>\n",
       "      <td>-0.045392</td>\n",
       "      <td>-0.295268</td>\n",
       "      <td>-0.052432</td>\n",
       "      <td>0.398568</td>\n",
       "      <td>-0.254879</td>\n",
       "      <td>-0.074253</td>\n",
       "      <td>-0.091066</td>\n",
       "      <td>-0.134699</td>\n",
       "      <td>0.460211</td>\n",
       "      <td>-0.299365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.061351</td>\n",
       "      <td>0.275532</td>\n",
       "      <td>0.809646</td>\n",
       "      <td>-0.751101</td>\n",
       "      <td>-0.497418</td>\n",
       "      <td>0.844059</td>\n",
       "      <td>0.678742</td>\n",
       "      <td>-0.563316</td>\n",
       "      <td>0.773377</td>\n",
       "      <td>-0.297689</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.045392</td>\n",
       "      <td>-0.295268</td>\n",
       "      <td>-0.052432</td>\n",
       "      <td>0.398568</td>\n",
       "      <td>-0.254879</td>\n",
       "      <td>-0.074253</td>\n",
       "      <td>-0.091066</td>\n",
       "      <td>-0.134699</td>\n",
       "      <td>0.460211</td>\n",
       "      <td>-0.299365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.061351</td>\n",
       "      <td>0.470064</td>\n",
       "      <td>0.032064</td>\n",
       "      <td>-0.054877</td>\n",
       "      <td>0.400766</td>\n",
       "      <td>0.876976</td>\n",
       "      <td>0.678742</td>\n",
       "      <td>-0.450284</td>\n",
       "      <td>0.357958</td>\n",
       "      <td>-0.297689</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.045392</td>\n",
       "      <td>-0.295268</td>\n",
       "      <td>-0.052432</td>\n",
       "      <td>0.398568</td>\n",
       "      <td>-0.254879</td>\n",
       "      <td>-0.074253</td>\n",
       "      <td>-0.091066</td>\n",
       "      <td>-0.134699</td>\n",
       "      <td>0.460211</td>\n",
       "      <td>-0.299365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.465443</td>\n",
       "      <td>-1.232092</td>\n",
       "      <td>-0.971808</td>\n",
       "      <td>1.337571</td>\n",
       "      <td>-0.497418</td>\n",
       "      <td>0.679475</td>\n",
       "      <td>0.394694</td>\n",
       "      <td>-0.563316</td>\n",
       "      <td>-0.387160</td>\n",
       "      <td>-0.297689</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.045392</td>\n",
       "      <td>-0.295268</td>\n",
       "      <td>-0.052432</td>\n",
       "      <td>0.398568</td>\n",
       "      <td>-0.254879</td>\n",
       "      <td>-0.074253</td>\n",
       "      <td>-0.091066</td>\n",
       "      <td>-0.134699</td>\n",
       "      <td>0.460211</td>\n",
       "      <td>-0.299365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 261 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  LotFrontage   LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0   -0.874711     0.567330  0.363929    -0.751101     0.400766  -0.340945   \n",
       "1   -0.874711     0.615963  0.897861    -0.054877     0.400766  -0.439695   \n",
       "2    0.061351     0.275532  0.809646    -0.751101    -0.497418   0.844059   \n",
       "3    0.061351     0.470064  0.032064    -0.054877     0.400766   0.876976   \n",
       "4    1.465443    -1.232092 -0.971808     1.337571    -0.497418   0.679475   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2          ...            \\\n",
       "0     -1.072885   -0.563316    0.063428    0.517537          ...             \n",
       "1     -1.214908    0.047057    1.063511   -0.297689          ...             \n",
       "2      0.678742   -0.563316    0.773377   -0.297689          ...             \n",
       "3      0.678742   -0.450284    0.357958   -0.297689          ...             \n",
       "4      0.394694   -0.563316   -0.387160   -0.297689          ...             \n",
       "\n",
       "   SaleType_ConLw  SaleType_New  SaleType_Oth  SaleType_WD  \\\n",
       "0       -0.045392     -0.295268     -0.052432     0.398568   \n",
       "1       -0.045392     -0.295268     -0.052432     0.398568   \n",
       "2       -0.045392     -0.295268     -0.052432     0.398568   \n",
       "3       -0.045392     -0.295268     -0.052432     0.398568   \n",
       "4       -0.045392     -0.295268     -0.052432     0.398568   \n",
       "\n",
       "   SaleCondition_Abnorml  SaleCondition_AdjLand  SaleCondition_Alloca  \\\n",
       "0              -0.254879              -0.074253             -0.091066   \n",
       "1              -0.254879              -0.074253             -0.091066   \n",
       "2              -0.254879              -0.074253             -0.091066   \n",
       "3              -0.254879              -0.074253             -0.091066   \n",
       "4              -0.254879              -0.074253             -0.091066   \n",
       "\n",
       "   SaleCondition_Family  SaleCondition_Normal  SaleCondition_Partial  \n",
       "0             -0.134699              0.460211              -0.299365  \n",
       "1             -0.134699              0.460211              -0.299365  \n",
       "2             -0.134699              0.460211              -0.299365  \n",
       "3             -0.134699              0.460211              -0.299365  \n",
       "4             -0.134699              0.460211              -0.299365  \n",
       "\n",
       "[5 rows x 261 columns]"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaledtest_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Y values\n",
    "#xtrain = scaledtrain_df.drop(\"SalePrice\", axis = 1)\n",
    "y = scaledtrain_df.SalePrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc = PCA(n_components=120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_train = pd.DataFrame(pc.fit_transform(scaledtrain_df))\n",
    "pca_test = pd.DataFrame(pc.fit_transform(scaledtest_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xee1a0b8>]"
      ]
     },
     "execution_count": 244,
     "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": [
    "plt.plot(np.cumsum(pc.explained_variance_ratio_), linestyle = \"dashed\", \n",
    "         markerfacecolor = \"red\", markersize = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply Linear Regression\n",
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(pca_train, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_model = lr.predict(pca_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled = sc.fit_transform(pd.DataFrame(train.SalePrice))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "actual_values  = sc.inverse_transform(lr_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the Data frame for Submission\n",
    "Id = test.Id\n",
    "SalePrice = actual_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = pd.DataFrame({\"Id\":Id, \"SalePrice\":SalePrice})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution.to_csv(\"LR Model.csv\", index = False) # RMSE 0.31682"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree = DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(pca_train, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_tree = dtree.predict(pca_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the Data frame for Submission\n",
    "actual_values  = sc.inverse_transform(model_tree)\n",
    "Id = test.Id\n",
    "SalePrice = actual_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = pd.DataFrame({\"Id\":Id, \"SalePrice\":SalePrice})\n",
    "solution.to_csv(\"Tree Model.csv\", index = False) # RMSE 0.26446"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor()\n",
    "rf.fit(pca_train, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_rf = rf.predict(pca_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the Data frame for Submission\n",
    "actual_values  = sc.inverse_transform(model_rf)\n",
    "Id = test.Id\n",
    "SalePrice = actual_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = pd.DataFrame({\"Id\":Id, \"SalePrice\":SalePrice})\n",
    "solution.to_csv(\"RF Model.csv\", index = False) # RMSE 0.21080"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "bagg = BaggingRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingRegressor(base_estimator=None, bootstrap=True,\n",
       "         bootstrap_features=False, max_features=1.0, max_samples=1.0,\n",
       "         n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
       "         verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagg.fit(pca_train, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_bagg = bagg.predict(pca_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the Data frame for Submission\n",
    "actual_values  = sc.inverse_transform(model_bagg)\n",
    "Id = test.Id\n",
    "SalePrice = actual_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = pd.DataFrame({\"Id\":Id, \"SalePrice\":SalePrice})\n",
    "solution.to_csv(\"Bagging Model.csv\", index = False) # RMSE 0.20331"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Boosting Model\n",
    "ada = AdaBoostRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear',\n",
       "         n_estimators=50, random_state=None)"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada.fit(pca_train, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ada = ada.predict(pca_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the Data frame for Submission\n",
    "actual_values  = sc.inverse_transform(model_ada)\n",
    "Id = test.Id\n",
    "SalePrice = actual_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = pd.DataFrame({\"Id\":Id, \"SalePrice\":SalePrice})\n",
    "solution.to_csv(\"Adaboost Model.csv\", index = False) # RMSE 0.20331"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensemble of base Models\n",
    "ensemble_models = (model_bagg+\n",
    "                   model_rf+model_tree+lr_model)/4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.79256878,  0.01079412, -0.20498297, ...,  0.03090633,\n",
       "       -0.73202939,  0.47893948])"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensemble_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the Data frame for Submission\n",
    "actual_values  = sc.inverse_transform(ensemble_models)\n",
    "Id = test.Id\n",
    "SalePrice = actual_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = pd.DataFrame({\"Id\":Id, \"SalePrice\":SalePrice})\n",
    "solution.to_csv(\"Ensemble Model.csv\", index = False) # RMSE 0.20331"
   ]
  }
 ],
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