Project Predicting House Sale Prices with Linear Regression.ipynb

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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project - Machine Learning :Predicting House Sale Prices using Linear Regression \n",
    "\n",
    "**Background and Introduction**: In this project, we will apply the machine learning algorithm linear regression to a practical problem i.e. prediction of house sale prices. The following equation is the general form of the simple or univariate linear regression model.\n",
    " $$\\hat{y} = a_1x_1 + a_0$$\n",
    " \n",
    " y represents the target column while x1 represents the feature column we choose to use in our model. These values are independent of the dataset. On the other hand, a0 and a1 represent the parameter values that are specific to the dataset. The goal of simple linear regression is to find the optimal parameter values that best describe the relationship between the feature column and the target column. We can extend this logic to the multivariate case (often called multiple linear regression). A multiple linear regression model allows us to capture the relationship between multiple feature columns and the target column. Here's what the formula looks like:\n",
    "$$\\hat{y} = a_0 + a_1x_1 + a_2x_2 + ... + a_nx_n$$\n",
    "\n",
    "**Dataset**: We will work with housing data for the city of Ames, Iowa, United States from 2006 to 2010. We can read more about why the data was collected [here](https://www.tandfonline.com/doi/abs/10.1080/10691898.2011.11889627). We can also find details about the different columns in the data [here](https://s3.amazonaws.com/dq-content/307/data_description.txt).\n",
    "\n",
    "**Prediction Strategy**: Since we are interested in predicting house prices based on available data, we will follow following strategy:\n",
    "\n",
    "Cleaning and normalizing the data.\n",
    "Feature Engineering\n",
    "Selection of features most likely to impact house prices.\n",
    "training, testing and cross-validation\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "**Model Building** We will work with the [`sklearn.linear_model.LinearRegression`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html) class. `LinearRegression` fits a linear model with coefficients `w = (w1, …, wp)` to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation.The LinearRegression class also has it's own `fit()` method. Specific to this model, however, are the coef_ and intercept_ attributes, which return a1 (a1 to an if it were a multivariate regression model) and a0 accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "\n",
    "# Setting pandas display options for large data\n",
    "pd.options.display.max_rows = 200\n",
    "pd.options.display.max_columns = 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the dataset, it's a tab delimited text file\n",
    "housing_dataset = pd.read_csv(\"AmesHousing.tsv\", delimiter=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2930 entries, 0 to 2929\n",
      "Data columns (total 82 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Order            2930 non-null   int64  \n",
      " 1   PID              2930 non-null   int64  \n",
      " 2   MS SubClass      2930 non-null   int64  \n",
      " 3   MS Zoning        2930 non-null   object \n",
      " 4   Lot Frontage     2440 non-null   float64\n",
      " 5   Lot Area         2930 non-null   int64  \n",
      " 6   Street           2930 non-null   object \n",
      " 7   Alley            198 non-null    object \n",
      " 8   Lot Shape        2930 non-null   object \n",
      " 9   Land Contour     2930 non-null   object \n",
      " 10  Utilities        2930 non-null   object \n",
      " 11  Lot Config       2930 non-null   object \n",
      " 12  Land Slope       2930 non-null   object \n",
      " 13  Neighborhood     2930 non-null   object \n",
      " 14  Condition 1      2930 non-null   object \n",
      " 15  Condition 2      2930 non-null   object \n",
      " 16  Bldg Type        2930 non-null   object \n",
      " 17  House Style      2930 non-null   object \n",
      " 18  Overall Qual     2930 non-null   int64  \n",
      " 19  Overall Cond     2930 non-null   int64  \n",
      " 20  Year Built       2930 non-null   int64  \n",
      " 21  Year Remod/Add   2930 non-null   int64  \n",
      " 22  Roof Style       2930 non-null   object \n",
      " 23  Roof Matl        2930 non-null   object \n",
      " 24  Exterior 1st     2930 non-null   object \n",
      " 25  Exterior 2nd     2930 non-null   object \n",
      " 26  Mas Vnr Type     2907 non-null   object \n",
      " 27  Mas Vnr Area     2907 non-null   float64\n",
      " 28  Exter Qual       2930 non-null   object \n",
      " 29  Exter Cond       2930 non-null   object \n",
      " 30  Foundation       2930 non-null   object \n",
      " 31  Bsmt Qual        2850 non-null   object \n",
      " 32  Bsmt Cond        2850 non-null   object \n",
      " 33  Bsmt Exposure    2847 non-null   object \n",
      " 34  BsmtFin Type 1   2850 non-null   object \n",
      " 35  BsmtFin SF 1     2929 non-null   float64\n",
      " 36  BsmtFin Type 2   2849 non-null   object \n",
      " 37  BsmtFin SF 2     2929 non-null   float64\n",
      " 38  Bsmt Unf SF      2929 non-null   float64\n",
      " 39  Total Bsmt SF    2929 non-null   float64\n",
      " 40  Heating          2930 non-null   object \n",
      " 41  Heating QC       2930 non-null   object \n",
      " 42  Central Air      2930 non-null   object \n",
      " 43  Electrical       2929 non-null   object \n",
      " 44  1st Flr SF       2930 non-null   int64  \n",
      " 45  2nd Flr SF       2930 non-null   int64  \n",
      " 46  Low Qual Fin SF  2930 non-null   int64  \n",
      " 47  Gr Liv Area      2930 non-null   int64  \n",
      " 48  Bsmt Full Bath   2928 non-null   float64\n",
      " 49  Bsmt Half Bath   2928 non-null   float64\n",
      " 50  Full Bath        2930 non-null   int64  \n",
      " 51  Half Bath        2930 non-null   int64  \n",
      " 52  Bedroom AbvGr    2930 non-null   int64  \n",
      " 53  Kitchen AbvGr    2930 non-null   int64  \n",
      " 54  Kitchen Qual     2930 non-null   object \n",
      " 55  TotRms AbvGrd    2930 non-null   int64  \n",
      " 56  Functional       2930 non-null   object \n",
      " 57  Fireplaces       2930 non-null   int64  \n",
      " 58  Fireplace Qu     1508 non-null   object \n",
      " 59  Garage Type      2773 non-null   object \n",
      " 60  Garage Yr Blt    2771 non-null   float64\n",
      " 61  Garage Finish    2771 non-null   object \n",
      " 62  Garage Cars      2929 non-null   float64\n",
      " 63  Garage Area      2929 non-null   float64\n",
      " 64  Garage Qual      2771 non-null   object \n",
      " 65  Garage Cond      2771 non-null   object \n",
      " 66  Paved Drive      2930 non-null   object \n",
      " 67  Wood Deck SF     2930 non-null   int64  \n",
      " 68  Open Porch SF    2930 non-null   int64  \n",
      " 69  Enclosed Porch   2930 non-null   int64  \n",
      " 70  3Ssn Porch       2930 non-null   int64  \n",
      " 71  Screen Porch     2930 non-null   int64  \n",
      " 72  Pool Area        2930 non-null   int64  \n",
      " 73  Pool QC          13 non-null     object \n",
      " 74  Fence            572 non-null    object \n",
      " 75  Misc Feature     106 non-null    object \n",
      " 76  Misc Val         2930 non-null   int64  \n",
      " 77  Mo Sold          2930 non-null   int64  \n",
      " 78  Yr Sold          2930 non-null   int64  \n",
      " 79  Sale Type        2930 non-null   object \n",
      " 80  Sale Condition   2930 non-null   object \n",
      " 81  SalePrice        2930 non-null   int64  \n",
      "dtypes: float64(11), int64(28), object(43)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Preliminary Data Exploration\n",
    "housing_dataset.info()\n",
    "# null_count = housing_dataset.isnull().sum()\n",
    "# print(len(null_count.index))\n",
    "# print(null_count)\n",
    "# (null_count.index)[5:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Order</th>\n",
       "      <th>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Overall Qual</th>\n",
       "      <th>Overall Cond</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>Year Remod/Add</th>\n",
       "      <th>Mas Vnr Area</th>\n",
       "      <th>BsmtFin SF 1</th>\n",
       "      <th>BsmtFin SF 2</th>\n",
       "      <th>Bsmt Unf SF</th>\n",
       "      <th>Total Bsmt SF</th>\n",
       "      <th>1st Flr SF</th>\n",
       "      <th>2nd Flr SF</th>\n",
       "      <th>Low Qual Fin SF</th>\n",
       "      <th>Gr Liv Area</th>\n",
       "      <th>Bsmt Full Bath</th>\n",
       "      <th>Bsmt Half Bath</th>\n",
       "      <th>Full Bath</th>\n",
       "      <th>Half Bath</th>\n",
       "      <th>Bedroom AbvGr</th>\n",
       "      <th>Kitchen AbvGr</th>\n",
       "      <th>TotRms AbvGrd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>Garage Yr Blt</th>\n",
       "      <th>Garage Cars</th>\n",
       "      <th>Garage Area</th>\n",
       "      <th>Wood Deck SF</th>\n",
       "      <th>Open Porch SF</th>\n",
       "      <th>Enclosed Porch</th>\n",
       "      <th>3Ssn Porch</th>\n",
       "      <th>Screen Porch</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2930.00000</td>\n",
       "      <td>2.930000e+03</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2440.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2907.000000</td>\n",
       "      <td>2929.000000</td>\n",
       "      <td>2929.000000</td>\n",
       "      <td>2929.000000</td>\n",
       "      <td>2929.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2928.000000</td>\n",
       "      <td>2928.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2771.000000</td>\n",
       "      <td>2929.000000</td>\n",
       "      <td>2929.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1465.50000</td>\n",
       "      <td>7.144645e+08</td>\n",
       "      <td>57.387372</td>\n",
       "      <td>69.224590</td>\n",
       "      <td>10147.921843</td>\n",
       "      <td>6.094881</td>\n",
       "      <td>5.563140</td>\n",
       "      <td>1971.356314</td>\n",
       "      <td>1984.266553</td>\n",
       "      <td>101.896801</td>\n",
       "      <td>442.629566</td>\n",
       "      <td>49.722431</td>\n",
       "      <td>559.262547</td>\n",
       "      <td>1051.614544</td>\n",
       "      <td>1159.557679</td>\n",
       "      <td>335.455973</td>\n",
       "      <td>4.676792</td>\n",
       "      <td>1499.690444</td>\n",
       "      <td>0.431352</td>\n",
       "      <td>0.061134</td>\n",
       "      <td>1.566553</td>\n",
       "      <td>0.379522</td>\n",
       "      <td>2.854266</td>\n",
       "      <td>1.044369</td>\n",
       "      <td>6.443003</td>\n",
       "      <td>0.599317</td>\n",
       "      <td>1978.132443</td>\n",
       "      <td>1.766815</td>\n",
       "      <td>472.819734</td>\n",
       "      <td>93.751877</td>\n",
       "      <td>47.533447</td>\n",
       "      <td>23.011604</td>\n",
       "      <td>2.592491</td>\n",
       "      <td>16.002048</td>\n",
       "      <td>2.243345</td>\n",
       "      <td>50.635154</td>\n",
       "      <td>6.216041</td>\n",
       "      <td>2007.790444</td>\n",
       "      <td>180796.060068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>845.96247</td>\n",
       "      <td>1.887308e+08</td>\n",
       "      <td>42.638025</td>\n",
       "      <td>23.365335</td>\n",
       "      <td>7880.017759</td>\n",
       "      <td>1.411026</td>\n",
       "      <td>1.111537</td>\n",
       "      <td>30.245361</td>\n",
       "      <td>20.860286</td>\n",
       "      <td>179.112611</td>\n",
       "      <td>455.590839</td>\n",
       "      <td>169.168476</td>\n",
       "      <td>439.494153</td>\n",
       "      <td>440.615067</td>\n",
       "      <td>391.890885</td>\n",
       "      <td>428.395715</td>\n",
       "      <td>46.310510</td>\n",
       "      <td>505.508887</td>\n",
       "      <td>0.524820</td>\n",
       "      <td>0.245254</td>\n",
       "      <td>0.552941</td>\n",
       "      <td>0.502629</td>\n",
       "      <td>0.827731</td>\n",
       "      <td>0.214076</td>\n",
       "      <td>1.572964</td>\n",
       "      <td>0.647921</td>\n",
       "      <td>25.528411</td>\n",
       "      <td>0.760566</td>\n",
       "      <td>215.046549</td>\n",
       "      <td>126.361562</td>\n",
       "      <td>67.483400</td>\n",
       "      <td>64.139059</td>\n",
       "      <td>25.141331</td>\n",
       "      <td>56.087370</td>\n",
       "      <td>35.597181</td>\n",
       "      <td>566.344288</td>\n",
       "      <td>2.714492</td>\n",
       "      <td>1.316613</td>\n",
       "      <td>79886.692357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>5.263011e+08</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>334.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>334.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1895.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>12789.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>733.25000</td>\n",
       "      <td>5.284770e+08</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>7440.250000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1954.000000</td>\n",
       "      <td>1965.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>219.000000</td>\n",
       "      <td>793.000000</td>\n",
       "      <td>876.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>129500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1465.50000</td>\n",
       "      <td>5.354536e+08</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>9436.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>466.000000</td>\n",
       "      <td>990.000000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1442.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1979.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>160000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2197.75000</td>\n",
       "      <td>9.071811e+08</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11555.250000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>734.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>1302.000000</td>\n",
       "      <td>1384.000000</td>\n",
       "      <td>703.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1742.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>213500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2930.00000</td>\n",
       "      <td>1.007100e+09</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>215245.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5644.000000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>2336.000000</td>\n",
       "      <td>6110.000000</td>\n",
       "      <td>5095.000000</td>\n",
       "      <td>2065.000000</td>\n",
       "      <td>1064.000000</td>\n",
       "      <td>5642.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>1424.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>1012.000000</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Order           PID  MS SubClass  Lot Frontage       Lot Area  \\\n",
       "count  2930.00000  2.930000e+03  2930.000000   2440.000000    2930.000000   \n",
       "mean   1465.50000  7.144645e+08    57.387372     69.224590   10147.921843   \n",
       "std     845.96247  1.887308e+08    42.638025     23.365335    7880.017759   \n",
       "min       1.00000  5.263011e+08    20.000000     21.000000    1300.000000   \n",
       "25%     733.25000  5.284770e+08    20.000000     58.000000    7440.250000   \n",
       "50%    1465.50000  5.354536e+08    50.000000     68.000000    9436.500000   \n",
       "75%    2197.75000  9.071811e+08    70.000000     80.000000   11555.250000   \n",
       "max    2930.00000  1.007100e+09   190.000000    313.000000  215245.000000   \n",
       "\n",
       "       Overall Qual  Overall Cond   Year Built  Year Remod/Add  Mas Vnr Area  \\\n",
       "count   2930.000000   2930.000000  2930.000000     2930.000000   2907.000000   \n",
       "mean       6.094881      5.563140  1971.356314     1984.266553    101.896801   \n",
       "std        1.411026      1.111537    30.245361       20.860286    179.112611   \n",
       "min        1.000000      1.000000  1872.000000     1950.000000      0.000000   \n",
       "25%        5.000000      5.000000  1954.000000     1965.000000      0.000000   \n",
       "50%        6.000000      5.000000  1973.000000     1993.000000      0.000000   \n",
       "75%        7.000000      6.000000  2001.000000     2004.000000    164.000000   \n",
       "max       10.000000      9.000000  2010.000000     2010.000000   1600.000000   \n",
       "\n",
       "       BsmtFin SF 1  BsmtFin SF 2  Bsmt Unf SF  Total Bsmt SF   1st Flr SF  \\\n",
       "count   2929.000000   2929.000000  2929.000000    2929.000000  2930.000000   \n",
       "mean     442.629566     49.722431   559.262547    1051.614544  1159.557679   \n",
       "std      455.590839    169.168476   439.494153     440.615067   391.890885   \n",
       "min        0.000000      0.000000     0.000000       0.000000   334.000000   \n",
       "25%        0.000000      0.000000   219.000000     793.000000   876.250000   \n",
       "50%      370.000000      0.000000   466.000000     990.000000  1084.000000   \n",
       "75%      734.000000      0.000000   802.000000    1302.000000  1384.000000   \n",
       "max     5644.000000   1526.000000  2336.000000    6110.000000  5095.000000   \n",
       "\n",
       "        2nd Flr SF  Low Qual Fin SF  Gr Liv Area  Bsmt Full Bath  \\\n",
       "count  2930.000000      2930.000000  2930.000000     2928.000000   \n",
       "mean    335.455973         4.676792  1499.690444        0.431352   \n",
       "std     428.395715        46.310510   505.508887        0.524820   \n",
       "min       0.000000         0.000000   334.000000        0.000000   \n",
       "25%       0.000000         0.000000  1126.000000        0.000000   \n",
       "50%       0.000000         0.000000  1442.000000        0.000000   \n",
       "75%     703.750000         0.000000  1742.750000        1.000000   \n",
       "max    2065.000000      1064.000000  5642.000000        3.000000   \n",
       "\n",
       "       Bsmt Half Bath    Full Bath    Half Bath  Bedroom AbvGr  Kitchen AbvGr  \\\n",
       "count     2928.000000  2930.000000  2930.000000    2930.000000    2930.000000   \n",
       "mean         0.061134     1.566553     0.379522       2.854266       1.044369   \n",
       "std          0.245254     0.552941     0.502629       0.827731       0.214076   \n",
       "min          0.000000     0.000000     0.000000       0.000000       0.000000   \n",
       "25%          0.000000     1.000000     0.000000       2.000000       1.000000   \n",
       "50%          0.000000     2.000000     0.000000       3.000000       1.000000   \n",
       "75%          0.000000     2.000000     1.000000       3.000000       1.000000   \n",
       "max          2.000000     4.000000     2.000000       8.000000       3.000000   \n",
       "\n",
       "       TotRms AbvGrd   Fireplaces  Garage Yr Blt  Garage Cars  Garage Area  \\\n",
       "count    2930.000000  2930.000000    2771.000000  2929.000000  2929.000000   \n",
       "mean        6.443003     0.599317    1978.132443     1.766815   472.819734   \n",
       "std         1.572964     0.647921      25.528411     0.760566   215.046549   \n",
       "min         2.000000     0.000000    1895.000000     0.000000     0.000000   \n",
       "25%         5.000000     0.000000    1960.000000     1.000000   320.000000   \n",
       "50%         6.000000     1.000000    1979.000000     2.000000   480.000000   \n",
       "75%         7.000000     1.000000    2002.000000     2.000000   576.000000   \n",
       "max        15.000000     4.000000    2207.000000     5.000000  1488.000000   \n",
       "\n",
       "       Wood Deck SF  Open Porch SF  Enclosed Porch   3Ssn Porch  Screen Porch  \\\n",
       "count   2930.000000    2930.000000     2930.000000  2930.000000   2930.000000   \n",
       "mean      93.751877      47.533447       23.011604     2.592491     16.002048   \n",
       "std      126.361562      67.483400       64.139059    25.141331     56.087370   \n",
       "min        0.000000       0.000000        0.000000     0.000000      0.000000   \n",
       "25%        0.000000       0.000000        0.000000     0.000000      0.000000   \n",
       "50%        0.000000      27.000000        0.000000     0.000000      0.000000   \n",
       "75%      168.000000      70.000000        0.000000     0.000000      0.000000   \n",
       "max     1424.000000     742.000000     1012.000000   508.000000    576.000000   \n",
       "\n",
       "         Pool Area      Misc Val      Mo Sold      Yr Sold      SalePrice  \n",
       "count  2930.000000   2930.000000  2930.000000  2930.000000    2930.000000  \n",
       "mean      2.243345     50.635154     6.216041  2007.790444  180796.060068  \n",
       "std      35.597181    566.344288     2.714492     1.316613   79886.692357  \n",
       "min       0.000000      0.000000     1.000000  2006.000000   12789.000000  \n",
       "25%       0.000000      0.000000     4.000000  2007.000000  129500.000000  \n",
       "50%       0.000000      0.000000     6.000000  2008.000000  160000.000000  \n",
       "75%       0.000000      0.000000     8.000000  2009.000000  213500.000000  \n",
       "max     800.000000  17000.000000    12.000000  2010.000000  755000.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting up a Pipeline\n",
    "In accordance with our strategy, we will set up a pipeline to facilitate repeated iterations for model tuning as under:\n",
    "1. **Dealing with Missing Values**\n",
    "    - Write a function for missing values\n",
    "        - Categorize columns as 'Categorical' and 'numeric'\n",
    "        - Identify numeric 'Categorical' columns\n",
    "        - Identify date columns and change them to numeric values (ratio columns)\n",
    "        - Select a threshhold for when to remove columns (missing values>40%) \n",
    "        - Select a critieria for fillin in missing values for both categorical and numeric columns (mode and mean)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def update_missing_values(df):\n",
    "    ''' Cleans the dataset, removes columns with excessive missing values\n",
    "    replaces missing values in numerical and categorical values with means and modes respectively\n",
    "    Args\n",
    "    df(dataframe): array or matrix like, input dataset\n",
    "    Returns df (dataframe): array or matrix like, cleaned dataset'''\n",
    "    \n",
    "    cols = df.columns\n",
    "    # Identify numeric cols\n",
    "    numeric_cols = []\n",
    "    for col in cols:\n",
    "        if (df[col].dtype == 'int64') or (df[col].dtype == 'float64'):\n",
    "            numeric_cols.append(col)\n",
    "    # Identify categorical cols\n",
    "    cat_cols = list(df.select_dtypes(include =['object']).columns)\n",
    "    # Identify numeric categorical columns\n",
    "    # Make a list of possible keywords (regex pattern) which indicate that the column does not have ordinal data\n",
    "    # Use regex and loop over the numeric columns list and regex pattern list\n",
    "    import re\n",
    "    pattern_list = ['type', 'cond', 'val', 'qual', 'rank', 'year', 'order', 'id', 'class', 'yr', r'mo\\s']\n",
    "    numeric_cat_cols = []\n",
    "    for col in numeric_cols:\n",
    "        for pattern in pattern_list:\n",
    "            if re.search(pattern, col, flags = re.I):\n",
    "                numeric_cat_cols.append(col)\n",
    "    # Identify date columns in the same manner using regex\n",
    "    date_pattern_list = ['year', 'yr', r'mo\\s']\n",
    "    date_cols = []\n",
    "    for col in numeric_cols:\n",
    "        for pattern in date_pattern_list:\n",
    "            if re.search(pattern, col, flags = re.I):\n",
    "                date_cols.append(col)\n",
    "    \n",
    "   \n",
    "    null_count = df.isnull().sum()\n",
    "    cols = df.columns # Reassign variable 'cols' after adding two new columns above\n",
    "    \n",
    "    # drop columns with more than 40% missing values and those containing date values \n",
    "    df_new = df[cols[null_count <= 0.4*len(df.index)]].copy()\n",
    "    df_new = df.drop(date_cols, axis = 1).copy()\n",
    "    # Replace date columns with ratio columns \n",
    "    # 'age_sold' = (year sold - year built), 'age_rebuilt' = (year remodeled - year built) \n",
    "    df_new['age_sold'] = df[date_cols[-1]] - df[date_cols[0]]\n",
    "    df_new['age_rebuilt'] = df[date_cols[1]] - df[date_cols[0]]\n",
    "    # Fill missing values\n",
    "    # First reassign the different columns lists due to changes in columns above\n",
    "    # All categorical columns (text and numeric) missing values to be filled with mode\n",
    "    # All numeric columns (except numeric categorical columns) missing values to be filled with mode\n",
    "    new_numeric_cols = list(df.select_dtypes(include =['int64', 'float64']).columns)\n",
    "    new_cat_cols = list(df.select_dtypes(include =['object']).columns)\n",
    "    new_numeric_cat_cols = []\n",
    "    for col in new_numeric_cols:\n",
    "        for pattern in pattern_list:\n",
    "            if re.search(pattern, col, flags = re.I):\n",
    "                new_numeric_cat_cols.append(col)\n",
    "    \n",
    "    # Remove numerical categorical columns from the categorical columns list\n",
    "    for col in new_numeric_cat_cols:\n",
    "        new_numeric_cols.remove(col)\n",
    "    # Replace missing values in categorical columns with the most frequently occuring value(mode)\n",
    "    for col in new_cat_cols:   \n",
    "        df_new[col] = df_new[col].fillna(df[col].value_counts(ascending=False).index[0])\n",
    "    # Replace missing values in numerical columns with mean    \n",
    "    df_new[new_numeric_cols] = df[new_numeric_cols].fillna(df[new_numeric_cols].mean())\n",
    "    return df_new\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order              0\n",
       "PID                0\n",
       "MS SubClass        0\n",
       "MS Zoning          0\n",
       "Lot Frontage       0\n",
       "Lot Area           0\n",
       "Street             0\n",
       "Alley              0\n",
       "Lot Shape          0\n",
       "Land Contour       0\n",
       "Utilities          0\n",
       "Lot Config         0\n",
       "Land Slope         0\n",
       "Neighborhood       0\n",
       "Condition 1        0\n",
       "Condition 2        0\n",
       "Bldg Type          0\n",
       "House Style        0\n",
       "Overall Qual       0\n",
       "Overall Cond       0\n",
       "Roof Style         0\n",
       "Roof Matl          0\n",
       "Exterior 1st       0\n",
       "Exterior 2nd       0\n",
       "Mas Vnr Type       0\n",
       "Mas Vnr Area       0\n",
       "Exter Qual         0\n",
       "Exter Cond         0\n",
       "Foundation         0\n",
       "Bsmt Qual          0\n",
       "Bsmt Cond          0\n",
       "Bsmt Exposure      0\n",
       "BsmtFin Type 1     0\n",
       "BsmtFin SF 1       0\n",
       "BsmtFin Type 2     0\n",
       "BsmtFin SF 2       0\n",
       "Bsmt Unf SF        0\n",
       "Total Bsmt SF      0\n",
       "Heating            0\n",
       "Heating QC         0\n",
       "Central Air        0\n",
       "Electrical         0\n",
       "1st Flr SF         0\n",
       "2nd Flr SF         0\n",
       "Low Qual Fin SF    0\n",
       "Gr Liv Area        0\n",
       "Bsmt Full Bath     0\n",
       "Bsmt Half Bath     0\n",
       "Full Bath          0\n",
       "Half Bath          0\n",
       "Bedroom AbvGr      0\n",
       "Kitchen AbvGr      0\n",
       "Kitchen Qual       0\n",
       "TotRms AbvGrd      0\n",
       "Functional         0\n",
       "Fireplaces         0\n",
       "Fireplace Qu       0\n",
       "Garage Type        0\n",
       "Garage Finish      0\n",
       "Garage Cars        0\n",
       "Garage Area        0\n",
       "Garage Qual        0\n",
       "Garage Cond        0\n",
       "Paved Drive        0\n",
       "Wood Deck SF       0\n",
       "Open Porch SF      0\n",
       "Enclosed Porch     0\n",
       "3Ssn Porch         0\n",
       "Screen Porch       0\n",
       "Pool Area          0\n",
       "Pool QC            0\n",
       "Fence              0\n",
       "Misc Feature       0\n",
       "Misc Val           0\n",
       "Sale Type          0\n",
       "Sale Condition     0\n",
       "SalePrice          0\n",
       "age_sold           0\n",
       "age_rebuilt        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check missing values\n",
    "update_missing_values(housing_dataset).isnull().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. **Feature Selection - I (Split and Scale - Transform Features)**\n",
    "    - First, we will write a function to split the dataframe into training and test sets according to desired number of folds (k folds) and carry out min-max scaling of numerical columns    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_features(df, k=2):\n",
    "    ''' Splits the dataframe into training and test sets as per number of folds, \n",
    "    scales numeric-feature values between 0 and 1\n",
    "    Args\n",
    "    df: dataframe, matrix like\n",
    "    k (int): number of folds; default = 2 (50% split between training and test)\n",
    "    Returns (dataframes): Cleaned and min_max scaled training dataset \n",
    "    and test dataset, split in proportion to number of folds'''\n",
    "    # use the update_missing_values function to clean dataset and replace missing values\n",
    "    \n",
    "    # generate various combinations of trainining and test sets in accordance with number of folds\n",
    "    # Applly the function 'update_missing_values' along with splitting to ensure cleaning of data and imputing missing values\n",
    "    \n",
    "    train = update_missing_values(df.head(int((1-1/k)*len(df.index))))\n",
    "    test = update_missing_values(df.tail(len(df.index) - len(train.index)))\n",
    "    # rescale train dataset using min-max scaling on numeric cols of train dataset except SalePrice column\n",
    "    # Also remove numeric_cat columns from the numeric columns list\n",
    "    numeric_cols = list(train.select_dtypes(include =['int64', 'float64']).columns)\n",
    "    # Remove SalePrice column from column list\n",
    "    numeric_cols.remove('SalePrice')\n",
    "    pattern_list = ['type', 'cond', 'val', 'qual', 'rank', 'year', 'order', 'id', 'class', 'yr', r'mo\\s']\n",
    "    import re\n",
    "    numeric_cat_cols = []\n",
    "    for col in numeric_cols:\n",
    "        for pattern in pattern_list:\n",
    "            if re.search(pattern, col, flags = re.I):\n",
    "                numeric_cat_cols.append(col)\n",
    "    # Remove numeric categorical columns from numeric columns list   \n",
    "    for col in numeric_cat_cols:            \n",
    "        numeric_cols.remove(col)\n",
    "     # Apply min-max scaling to numeric columns of training and test set\n",
    "    scaled_train = (train[numeric_cols] - train[numeric_cols].min()) / (train[numeric_cols].max() - train[numeric_cols].min())\n",
    "    scaled_test = (test[numeric_cols] - test[numeric_cols].min()) / (test[numeric_cols].max() - test[numeric_cols].min())\n",
    "    # Add 'SalePrice' column\n",
    "    scaled_train['SalePrice'] = train['SalePrice']\n",
    "    scaled_test['SalePrice'] = test['SalePrice']\n",
    "    # Return a list of training and test dataframes in which first item is training set and second item is test set\n",
    "    # Apply the function to clean the respective datasets and update missing values\n",
    "    return [scaled_train, scaled_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "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>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Mas Vnr Area</th>\n",
       "      <th>BsmtFin SF 1</th>\n",
       "      <th>BsmtFin SF 2</th>\n",
       "      <th>Bsmt Unf SF</th>\n",
       "      <th>Total Bsmt SF</th>\n",
       "      <th>1st Flr SF</th>\n",
       "      <th>2nd Flr SF</th>\n",
       "      <th>Gr Liv Area</th>\n",
       "      <th>Bsmt Full Bath</th>\n",
       "      <th>Bsmt Half Bath</th>\n",
       "      <th>Full Bath</th>\n",
       "      <th>Half Bath</th>\n",
       "      <th>Bedroom AbvGr</th>\n",
       "      <th>Kitchen AbvGr</th>\n",
       "      <th>TotRms AbvGrd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>Garage Cars</th>\n",
       "      <th>Garage Area</th>\n",
       "      <th>Wood Deck SF</th>\n",
       "      <th>Open Porch SF</th>\n",
       "      <th>Enclosed Porch</th>\n",
       "      <th>3Ssn Porch</th>\n",
       "      <th>Screen Porch</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>age_sold</th>\n",
       "      <th>age_rebuilt</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.164050</td>\n",
       "      <td>0.040069</td>\n",
       "      <td>0.079589</td>\n",
       "      <td>0.195996</td>\n",
       "      <td>0.033653</td>\n",
       "      <td>0.234964</td>\n",
       "      <td>0.327096</td>\n",
       "      <td>0.335612</td>\n",
       "      <td>0.184996</td>\n",
       "      <td>0.351834</td>\n",
       "      <td>0.149590</td>\n",
       "      <td>0.056011</td>\n",
       "      <td>0.391809</td>\n",
       "      <td>0.193174</td>\n",
       "      <td>0.474061</td>\n",
       "      <td>0.024915</td>\n",
       "      <td>0.341570</td>\n",
       "      <td>0.198635</td>\n",
       "      <td>0.354130</td>\n",
       "      <td>0.318574</td>\n",
       "      <td>0.138093</td>\n",
       "      <td>0.085031</td>\n",
       "      <td>0.051139</td>\n",
       "      <td>0.006502</td>\n",
       "      <td>0.028164</td>\n",
       "      <td>0.000683</td>\n",
       "      <td>0.277996</td>\n",
       "      <td>0.113983</td>\n",
       "      <td>179878.498976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.070614</td>\n",
       "      <td>0.033377</td>\n",
       "      <td>0.135144</td>\n",
       "      <td>0.194242</td>\n",
       "      <td>0.110672</td>\n",
       "      <td>0.184177</td>\n",
       "      <td>0.133468</td>\n",
       "      <td>0.160598</td>\n",
       "      <td>0.232006</td>\n",
       "      <td>0.152059</td>\n",
       "      <td>0.176864</td>\n",
       "      <td>0.229943</td>\n",
       "      <td>0.136373</td>\n",
       "      <td>0.254511</td>\n",
       "      <td>0.138331</td>\n",
       "      <td>0.111927</td>\n",
       "      <td>0.155453</td>\n",
       "      <td>0.215655</td>\n",
       "      <td>0.150313</td>\n",
       "      <td>0.142651</td>\n",
       "      <td>0.181691</td>\n",
       "      <td>0.117929</td>\n",
       "      <td>0.137547</td>\n",
       "      <td>0.058724</td>\n",
       "      <td>0.099966</td>\n",
       "      <td>0.026126</td>\n",
       "      <td>0.223423</td>\n",
       "      <td>0.195275</td>\n",
       "      <td>78400.026435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12789.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.133562</td>\n",
       "      <td>0.028979</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.094606</td>\n",
       "      <td>0.245789</td>\n",
       "      <td>0.211704</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.238172</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.225806</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066176</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>129000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.164050</td>\n",
       "      <td>0.038000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.165647</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.197774</td>\n",
       "      <td>0.309420</td>\n",
       "      <td>0.308090</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.339922</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047532</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>160000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.198630</td>\n",
       "      <td>0.047250</td>\n",
       "      <td>0.127907</td>\n",
       "      <td>0.320367</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.334760</td>\n",
       "      <td>0.407361</td>\n",
       "      <td>0.434596</td>\n",
       "      <td>0.386139</td>\n",
       "      <td>0.430006</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.387097</td>\n",
       "      <td>0.251497</td>\n",
       "      <td>0.127971</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.404412</td>\n",
       "      <td>0.164062</td>\n",
       "      <td>214000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>615000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Lot Frontage     Lot Area  Mas Vnr Area  BsmtFin SF 1  BsmtFin SF 2  \\\n",
       "count   1465.000000  1465.000000   1465.000000   1465.000000   1465.000000   \n",
       "mean       0.164050     0.040069      0.079589      0.195996      0.033653   \n",
       "std        0.070614     0.033377      0.135144      0.194242      0.110672   \n",
       "min        0.000000     0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.133562     0.028979      0.000000      0.000000      0.000000   \n",
       "50%        0.164050     0.038000      0.000000      0.165647      0.000000   \n",
       "75%        0.198630     0.047250      0.127907      0.320367      0.000000   \n",
       "max        1.000000     1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "       Bsmt Unf SF  Total Bsmt SF   1st Flr SF   2nd Flr SF  Gr Liv Area  \\\n",
       "count  1465.000000    1465.000000  1465.000000  1465.000000  1465.000000   \n",
       "mean      0.234964       0.327096     0.335612     0.184996     0.351834   \n",
       "std       0.184177       0.133468     0.160598     0.232006     0.152059   \n",
       "min       0.000000       0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.094606       0.245789     0.211704     0.000000     0.238172   \n",
       "50%       0.197774       0.309420     0.308090     0.000000     0.339922   \n",
       "75%       0.334760       0.407361     0.434596     0.386139     0.430006   \n",
       "max       1.000000       1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       Bsmt Full Bath  Bsmt Half Bath    Full Bath    Half Bath  \\\n",
       "count     1465.000000     1465.000000  1465.000000  1465.000000   \n",
       "mean         0.149590        0.056011     0.391809     0.193174   \n",
       "std          0.176864        0.229943     0.136373     0.254511   \n",
       "min          0.000000        0.000000     0.000000     0.000000   \n",
       "25%          0.000000        0.000000     0.250000     0.000000   \n",
       "50%          0.000000        0.000000     0.500000     0.000000   \n",
       "75%          0.333333        0.000000     0.500000     0.500000   \n",
       "max          1.000000        1.000000     1.000000     1.000000   \n",
       "\n",
       "       Bedroom AbvGr  Kitchen AbvGr  TotRms AbvGrd   Fireplaces  Garage Cars  \\\n",
       "count    1465.000000    1465.000000    1465.000000  1465.000000  1465.000000   \n",
       "mean        0.474061       0.024915       0.341570     0.198635     0.354130   \n",
       "std         0.138331       0.111927       0.155453     0.215655     0.150313   \n",
       "min         0.000000       0.000000       0.000000     0.000000     0.000000   \n",
       "25%         0.333333       0.000000       0.200000     0.000000     0.200000   \n",
       "50%         0.500000       0.000000       0.300000     0.333333     0.400000   \n",
       "75%         0.500000       0.000000       0.400000     0.333333     0.400000   \n",
       "max         1.000000       1.000000       1.000000     1.000000     1.000000   \n",
       "\n",
       "       Garage Area  Wood Deck SF  Open Porch SF  Enclosed Porch   3Ssn Porch  \\\n",
       "count  1465.000000   1465.000000    1465.000000     1465.000000  1465.000000   \n",
       "mean      0.318574      0.138093       0.085031        0.051139     0.006502   \n",
       "std       0.142651      0.181691       0.117929        0.137547     0.058724   \n",
       "min       0.000000      0.000000       0.000000        0.000000     0.000000   \n",
       "25%       0.225806      0.000000       0.000000        0.000000     0.000000   \n",
       "50%       0.322581      0.000000       0.047532        0.000000     0.000000   \n",
       "75%       0.387097      0.251497       0.127971        0.000000     0.000000   \n",
       "max       1.000000      1.000000       1.000000        1.000000     1.000000   \n",
       "\n",
       "       Screen Porch    Pool Area     age_sold  age_rebuilt      SalePrice  \n",
       "count   1465.000000  1465.000000  1465.000000  1465.000000    1465.000000  \n",
       "mean       0.028164     0.000683     0.277996     0.113983  179878.498976  \n",
       "std        0.099966     0.026126     0.223423     0.195275   78400.026435  \n",
       "min        0.000000     0.000000     0.000000     0.000000   12789.000000  \n",
       "25%        0.000000     0.000000     0.066176     0.007812  129000.000000  \n",
       "50%        0.000000     0.000000     0.264706     0.007812  160000.000000  \n",
       "75%        0.000000     0.000000     0.404412     0.164062  214000.000000  \n",
       "max        1.000000     1.000000     1.000000     1.000000  615000.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check Results of feature transformation of training dataset\n",
    "transform_features(housing_dataset)[0].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Mas Vnr Area</th>\n",
       "      <th>BsmtFin SF 1</th>\n",
       "      <th>BsmtFin SF 2</th>\n",
       "      <th>Bsmt Unf SF</th>\n",
       "      <th>Total Bsmt SF</th>\n",
       "      <th>1st Flr SF</th>\n",
       "      <th>2nd Flr SF</th>\n",
       "      <th>Gr Liv Area</th>\n",
       "      <th>Bsmt Full Bath</th>\n",
       "      <th>Bsmt Half Bath</th>\n",
       "      <th>Full Bath</th>\n",
       "      <th>Half Bath</th>\n",
       "      <th>Bedroom AbvGr</th>\n",
       "      <th>Kitchen AbvGr</th>\n",
       "      <th>TotRms AbvGrd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>Garage Cars</th>\n",
       "      <th>Garage Area</th>\n",
       "      <th>Wood Deck SF</th>\n",
       "      <th>Open Porch SF</th>\n",
       "      <th>Enclosed Porch</th>\n",
       "      <th>3Ssn Porch</th>\n",
       "      <th>Screen Porch</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>age_sold</th>\n",
       "      <th>age_rebuilt</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.166242</td>\n",
       "      <td>0.054865</td>\n",
       "      <td>0.063202</td>\n",
       "      <td>0.077396</td>\n",
       "      <td>0.032627</td>\n",
       "      <td>0.264580</td>\n",
       "      <td>0.172595</td>\n",
       "      <td>0.174995</td>\n",
       "      <td>0.162029</td>\n",
       "      <td>0.220916</td>\n",
       "      <td>0.206967</td>\n",
       "      <td>0.033128</td>\n",
       "      <td>0.521957</td>\n",
       "      <td>0.186348</td>\n",
       "      <td>0.358020</td>\n",
       "      <td>0.519454</td>\n",
       "      <td>0.343870</td>\n",
       "      <td>0.150683</td>\n",
       "      <td>0.440745</td>\n",
       "      <td>0.332582</td>\n",
       "      <td>0.066894</td>\n",
       "      <td>0.065438</td>\n",
       "      <td>0.023647</td>\n",
       "      <td>0.005826</td>\n",
       "      <td>0.035867</td>\n",
       "      <td>0.005485</td>\n",
       "      <td>0.279541</td>\n",
       "      <td>0.105437</td>\n",
       "      <td>181713.621160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.075358</td>\n",
       "      <td>0.052388</td>\n",
       "      <td>0.114029</td>\n",
       "      <td>0.082639</td>\n",
       "      <td>0.114949</td>\n",
       "      <td>0.208230</td>\n",
       "      <td>0.074134</td>\n",
       "      <td>0.086051</td>\n",
       "      <td>0.210677</td>\n",
       "      <td>0.101621</td>\n",
       "      <td>0.259109</td>\n",
       "      <td>0.129741</td>\n",
       "      <td>0.186826</td>\n",
       "      <td>0.248117</td>\n",
       "      <td>0.103204</td>\n",
       "      <td>0.101880</td>\n",
       "      <td>0.122404</td>\n",
       "      <td>0.162270</td>\n",
       "      <td>0.192361</td>\n",
       "      <td>0.153586</td>\n",
       "      <td>0.092126</td>\n",
       "      <td>0.094800</td>\n",
       "      <td>0.067729</td>\n",
       "      <td>0.059847</td>\n",
       "      <td>0.124028</td>\n",
       "      <td>0.062648</td>\n",
       "      <td>0.233680</td>\n",
       "      <td>0.198431</td>\n",
       "      <td>81362.628458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.133562</td>\n",
       "      <td>0.036338</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100325</td>\n",
       "      <td>0.130115</td>\n",
       "      <td>0.117412</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.144876</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.222144</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.046512</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>129850.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.166242</td>\n",
       "      <td>0.048900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.063785</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.217371</td>\n",
       "      <td>0.161702</td>\n",
       "      <td>0.157110</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.206858</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.336389</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.037736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.263566</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>160500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.195205</td>\n",
       "      <td>0.062516</td>\n",
       "      <td>0.098125</td>\n",
       "      <td>0.130936</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.379006</td>\n",
       "      <td>0.212111</td>\n",
       "      <td>0.221592</td>\n",
       "      <td>0.342373</td>\n",
       "      <td>0.267898</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.406206</td>\n",
       "      <td>0.117978</td>\n",
       "      <td>0.094340</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.418605</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>213133.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Lot Frontage     Lot Area  Mas Vnr Area  BsmtFin SF 1  BsmtFin SF 2  \\\n",
       "count   1465.000000  1465.000000   1465.000000   1465.000000   1465.000000   \n",
       "mean       0.166242     0.054865      0.063202      0.077396      0.032627   \n",
       "std        0.075358     0.052388      0.114029      0.082639      0.114949   \n",
       "min        0.000000     0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.133562     0.036338      0.000000      0.000000      0.000000   \n",
       "50%        0.166242     0.048900      0.000000      0.063785      0.000000   \n",
       "75%        0.195205     0.062516      0.098125      0.130936      0.000000   \n",
       "max        1.000000     1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "       Bsmt Unf SF  Total Bsmt SF   1st Flr SF   2nd Flr SF  Gr Liv Area  \\\n",
       "count  1465.000000    1465.000000  1465.000000  1465.000000  1465.000000   \n",
       "mean      0.264580       0.172595     0.174995     0.162029     0.220916   \n",
       "std       0.208230       0.074134     0.086051     0.210677     0.101621   \n",
       "min       0.000000       0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.100325       0.130115     0.117412     0.000000     0.144876   \n",
       "50%       0.217371       0.161702     0.157110     0.000000     0.206858   \n",
       "75%       0.379006       0.212111     0.221592     0.342373     0.267898   \n",
       "max       1.000000       1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       Bsmt Full Bath  Bsmt Half Bath    Full Bath    Half Bath  \\\n",
       "count     1465.000000     1465.000000  1465.000000  1465.000000   \n",
       "mean         0.206967        0.033128     0.521957     0.186348   \n",
       "std          0.259109        0.129741     0.186826     0.248117   \n",
       "min          0.000000        0.000000     0.000000     0.000000   \n",
       "25%          0.000000        0.000000     0.333333     0.000000   \n",
       "50%          0.000000        0.000000     0.666667     0.000000   \n",
       "75%          0.500000        0.000000     0.666667     0.500000   \n",
       "max          1.000000        1.000000     1.000000     1.000000   \n",
       "\n",
       "       Bedroom AbvGr  Kitchen AbvGr  TotRms AbvGrd   Fireplaces  Garage Cars  \\\n",
       "count    1465.000000    1465.000000    1465.000000  1465.000000  1465.000000   \n",
       "mean        0.358020       0.519454       0.343870     0.150683     0.440745   \n",
       "std         0.103204       0.101880       0.122404     0.162270     0.192361   \n",
       "min         0.000000       0.000000       0.000000     0.000000     0.000000   \n",
       "25%         0.250000       0.500000       0.230769     0.000000     0.250000   \n",
       "50%         0.375000       0.500000       0.307692     0.250000     0.500000   \n",
       "75%         0.375000       0.500000       0.384615     0.250000     0.500000   \n",
       "max         1.000000       1.000000       1.000000     1.000000     1.000000   \n",
       "\n",
       "       Garage Area  Wood Deck SF  Open Porch SF  Enclosed Porch   3Ssn Porch  \\\n",
       "count  1465.000000   1465.000000    1465.000000     1465.000000  1465.000000   \n",
       "mean      0.332582      0.066894       0.065438        0.023647     0.005826   \n",
       "std       0.153586      0.092126       0.094800        0.067729     0.059847   \n",
       "min       0.000000      0.000000       0.000000        0.000000     0.000000   \n",
       "25%       0.222144      0.000000       0.000000        0.000000     0.000000   \n",
       "50%       0.336389      0.000000       0.037736        0.000000     0.000000   \n",
       "75%       0.406206      0.117978       0.094340        0.000000     0.000000   \n",
       "max       1.000000      1.000000       1.000000        1.000000     1.000000   \n",
       "\n",
       "       Screen Porch    Pool Area     age_sold  age_rebuilt      SalePrice  \n",
       "count   1465.000000  1465.000000  1465.000000  1465.000000    1465.000000  \n",
       "mean       0.035867     0.005485     0.279541     0.105437  181713.621160  \n",
       "std        0.124028     0.062648     0.233680     0.198431   81362.628458  \n",
       "min        0.000000     0.000000     0.000000     0.000000   13100.000000  \n",
       "25%        0.000000     0.000000     0.046512     0.000000  129850.000000  \n",
       "50%        0.000000     0.000000     0.263566     0.000000  160500.000000  \n",
       "75%        0.000000     0.000000     0.418605     0.137931  213133.000000  \n",
       "max        1.000000     1.000000     1.000000     1.000000  755000.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transform_features(housing_dataset)[1].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1465 entries, 0 to 1464\n",
      "Data columns (total 29 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Lot Frontage    1465 non-null   float64\n",
      " 1   Lot Area        1465 non-null   float64\n",
      " 2   Mas Vnr Area    1465 non-null   float64\n",
      " 3   BsmtFin SF 1    1465 non-null   float64\n",
      " 4   BsmtFin SF 2    1465 non-null   float64\n",
      " 5   Bsmt Unf SF     1465 non-null   float64\n",
      " 6   Total Bsmt SF   1465 non-null   float64\n",
      " 7   1st Flr SF      1465 non-null   float64\n",
      " 8   2nd Flr SF      1465 non-null   float64\n",
      " 9   Gr Liv Area     1465 non-null   float64\n",
      " 10  Bsmt Full Bath  1465 non-null   float64\n",
      " 11  Bsmt Half Bath  1465 non-null   float64\n",
      " 12  Full Bath       1465 non-null   float64\n",
      " 13  Half Bath       1465 non-null   float64\n",
      " 14  Bedroom AbvGr   1465 non-null   float64\n",
      " 15  Kitchen AbvGr   1465 non-null   float64\n",
      " 16  TotRms AbvGrd   1465 non-null   float64\n",
      " 17  Fireplaces      1465 non-null   float64\n",
      " 18  Garage Cars     1465 non-null   float64\n",
      " 19  Garage Area     1465 non-null   float64\n",
      " 20  Wood Deck SF    1465 non-null   float64\n",
      " 21  Open Porch SF   1465 non-null   float64\n",
      " 22  Enclosed Porch  1465 non-null   float64\n",
      " 23  3Ssn Porch      1465 non-null   float64\n",
      " 24  Screen Porch    1465 non-null   float64\n",
      " 25  Pool Area       1465 non-null   float64\n",
      " 26  age_sold        1465 non-null   float64\n",
      " 27  age_rebuilt     1465 non-null   float64\n",
      " 28  SalePrice       1465 non-null   int64  \n",
      "dtypes: float64(28), int64(1)\n",
      "memory usage: 332.0 KB\n"
     ]
    }
   ],
   "source": [
    "transform_features(housing_dataset)[0].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. **Feature Selection - II (Scatter Plot)**\n",
    "    - Write a function to plot linearity of all (numerical) features versus `SalePrice` column (scatter plot) in training dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scatter_plot(df):\n",
    "    ''' Plots scatter plots of various (numerical) features in the training dataset versus target column \n",
    "    Args\n",
    "    df: dataframe, matrix like\n",
    "    Returns (matplotlib figure object): Scatter plots of features versus target on the same figure object \n",
    "    '''\n",
    "    \n",
    "    # Extract features and target from training dataset using the function 'transform_features'\n",
    "    # transform_features() automatically also cleans the data,imputes missing values and splits dataset into training and test sets\n",
    "    features = list(transform_features(df)[0].columns)[:-1]\n",
    "    target = transform_features(df)[0].columns[-1]\n",
    "    \n",
    "    fig = plt.figure(figsize=(14, 54))\n",
    "    plt.style.use('fivethirtyeight')\n",
    "    \n",
    "    for i, feature in enumerate(features, 1): # use enumerate to set the counter for use in subplots\n",
    "        ax = fig.add_subplot(len(features),2,i)\n",
    "        ax.scatter(x = transform_features(df)[0][feature], y= transform_features(df)[0][target])\n",
    "        ax.set_xlabel(feature, fontsize = 16)\n",
    "    plt.tight_layout() # tight_layout() prevents spill over of data and labels among subplots\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x3888 with 28 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot various features against SalePrice column\n",
    "scatter_plot(housing_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. **Feature Selection - III (Correlation and Heatmap)**\n",
    "From above scatter plots, a number of features seem to have a linear relationship with the target column. We will further explore this by determining correlation. For this we will, \n",
    "    - Write a function to select features automatically based on a correlation threshhold\n",
    "    - Plot heatmap of all (numerical) features versus `SalePrice` column in training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_features(df, ct=0.3, k=2):\n",
    "    ''' Selects features for training and prediction based on correlation threshhold provided \n",
    "    Args\n",
    "    df: dataframe, matrix like\n",
    "    ct(float): correlation threshhold, default = 0.3\n",
    "    Returns (matplotlib figure object): Scatter plots of features versus target on the same figure object \n",
    "    '''\n",
    "    # Use the transform_features function to extract features and target from the dataframe\n",
    "    cols = list(transform_features(df)[0].columns)\n",
    "    features = list(transform_features(df)[0].columns)[:-1]\n",
    "    target = transform_features(df)[0].columns[-1]\n",
    "    # Find correlation among features and reduce to values valid for target column\n",
    "    # find the absolutes values and sort them\n",
    "    corrs = transform_features(df)[0][cols].corr().abs()\n",
    "    corrs_target = corrs[target].sort_values(ascending=False)\n",
    "    # Select features based on threshhold\n",
    "    selected_feature = list(corrs_target[corrs_target >= ct].index)\n",
    "    # Remove the target from the list\n",
    "    selected_feature.remove(target)\n",
    "    return pd.Series(selected_feature) # return a series object instead of a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Gr Liv Area\n",
       "1      Total Bsmt SF\n",
       "2        Garage Cars\n",
       "3        Garage Area\n",
       "4         1st Flr SF\n",
       "5           age_sold\n",
       "6          Full Bath\n",
       "7       Mas Vnr Area\n",
       "8       BsmtFin SF 1\n",
       "9         Fireplaces\n",
       "10     TotRms AbvGrd\n",
       "11      Lot Frontage\n",
       "12     Open Porch SF\n",
       "13      Wood Deck SF\n",
       "14    Bsmt Full Bath\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_features(housing_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a heatmap of selected features\n",
    "fig = plt.figure(figsize=(8, 6))\n",
    "plt.style.use('fivethirtyeight')\n",
    "mat = transform_features(housing_dataset)[0][select_features(housing_dataset)]\n",
    "# Add target column\n",
    "mat['SalePrice'] = transform_features(housing_dataset)[0].iloc[:,-1]\n",
    "corrmat = mat.corr().abs()\n",
    "sns.heatmap(corrmat, cmap=\"YlGnBu\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gr Liv Area       0.699537\n",
      "Total Bsmt SF     0.682929\n",
      "Garage Cars       0.664468\n",
      "Garage Area       0.663488\n",
      "1st Flr SF        0.656942\n",
      "age_sold          0.593622\n",
      "Full Bath         0.519375\n",
      "Mas Vnr Area      0.515546\n",
      "BsmtFin SF 1      0.512804\n",
      "Fireplaces        0.485892\n",
      "TotRms AbvGrd     0.484053\n",
      "Lot Frontage      0.347671\n",
      "Open Porch SF     0.343910\n",
      "Wood Deck SF      0.318280\n",
      "Bsmt Full Bath    0.314595\n",
      "SalePrice         1.000000\n",
      "Name: SalePrice, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Reconfirm correlations of selected features with target column\n",
    "print(corrmat['SalePrice'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Correcting Overfitting**\n",
    "The heat map indicates that some features are very closely correlated with each other. This `overfitting` might impact the final results, so we need to modify the `select_features` function in order to take care of overfitting. For now, we will do it manually, and later amend the function. \n",
    "```\n",
    "features = select_features(df, ct=0.3, k=i).drop(['Total Bsmt SF', 'Garage Cars', 'otRms AbvGrd'])\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training, Testing and Cross Validation\n",
    "We will now train, test and cross-validate univariate and multivariate models for selected features and select the optimum model using cross-validation\n",
    "- First, we will write a function to train and test models with any number of folds and any correlation threshhold.\n",
    "- univariate models for each selected feature\n",
    "- Then we will group selected features according to their correlation ranking in groups of 2, 3, 4,....n, where n is the total number of selected features, and train, test and cross-validate these multi-variate models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Model Testing, Training and Tuning - I (Univariate Cases)**\n",
    "- First, we will write a function to train and test models with any number of k-folds and any correlation threshhold between features and target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lr_train_test_univariate(df, k=2, ct=0.3, target = 'SalePrice'):\n",
    "    \"\"\" Cleans the dataset, imputes missing values, splits datasets into training and test sets according to number of folds,\n",
    "    selects features based on correlation threshhold among features and target, \n",
    "    trains the model and predicts root mean squared error(RMSE) values for target features \n",
    "    Args:\n",
    "    df (matrix, dataframe): dataframe like or matrix like\n",
    "    k(int): k-folds for splitting dataset\n",
    "    ct(float): Pearson's correlation coefficient threshhold between features and target\n",
    "    target(array like): target df feature\n",
    "    Returns:\n",
    "    Array: RMSE values for the range of k=folds specified\n",
    "    \"\"\"\n",
    "    features = select_features(df = df, k = k, ct = ct).drop(labels = [1, 2, 10]).reset_index(drop=True) # Remove features to avoid overfitting\n",
    "    \n",
    "    train = transform_features(df = df, k=k)[0][features]\n",
    "    # Add target Column\n",
    "    train[target] = transform_features(df = df, k=k)[0][target]\n",
    "    # Add target Column\n",
    "    test = transform_features(df = df, k = k)[1][features]\n",
    "    test[target] = transform_features(df = df, k = k)[1][target]\n",
    "    \n",
    "    lr = LinearRegression()\n",
    "    train_rmse_values = []\n",
    "    test_rmse_values = []\n",
    "    for feature in features:        \n",
    "        lr.fit(train[[feature]], train[target])\n",
    "        train_predictions = lr.predict(train[[feature]]) # We need to predict for the training set as well\n",
    "        test_predictions = lr.predict(test[[feature]]) \n",
    "        train_mse = mean_squared_error(train_predictions, train[target])\n",
    "        test_mse = mean_squared_error(test_predictions, test[target])\n",
    "        train_rmse = np.sqrt(train_mse)\n",
    "        test_rmse = np.sqrt(test_mse)\n",
    "#         train_rmse_values.append(feature) # if needed to print feature names with rmse values\n",
    "        train_rmse_values.append(train_rmse)\n",
    "#         test_rmse_values.append(feature) # if needed to print feature names with rmse values\n",
    "        test_rmse_values.append(test_rmse)\n",
    "        \n",
    "    \n",
    "    return [train_rmse_values, test_rmse_values]      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[56005.217429556425,\n",
       " 58637.88376829443,\n",
       " 59088.85634103881,\n",
       " 63070.40421551882,\n",
       " 66973.58859378891,\n",
       " 67155.08360688074,\n",
       " 67283.9136954212,\n",
       " 68499.69582762515,\n",
       " 73484.06393333082,\n",
       " 73592.70841565132,\n",
       " 74297.60283686062,\n",
       " 74393.95145605135]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training dataset rmse values for each feature, k = 2\n",
    "lr_train_test_univariate(housing_dataset)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[58127.254498892216,\n",
       " 62314.53745487544,\n",
       " 63090.702102867304,\n",
       " 66496.1310144202,\n",
       " 68124.05147595512,\n",
       " 69419.30271578858,\n",
       " 72801.36106103059,\n",
       " 71006.67265745853,\n",
       " 76123.38470639838,\n",
       " 76974.14729626851,\n",
       " 76915.50426023571,\n",
       " 77758.04056890406]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training dataset rmse values for each feature, k = 5\n",
    "lr_train_test_univariate(housing_dataset, k=5)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[78140.05153669385,\n",
       " 64215.93109184163,\n",
       " 87004.07868196556,\n",
       " 69476.91173733852,\n",
       " 76957.72992985389,\n",
       " 71182.95790683379,\n",
       " 81259.94737566516,\n",
       " 73355.0268932875,\n",
       " 76680.02836402833,\n",
       " 78219.23438537894,\n",
       " 78873.05867563661,\n",
       " 80955.41809047079]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test dataset rmse values for each feature, k = 2\n",
    "lr_train_test_univariate(housing_dataset)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[83927.09431255271,\n",
       " 70083.51001696513,\n",
       " 174126.4234990232,\n",
       " 65498.88450520311,\n",
       " 76480.05866477478,\n",
       " 67579.1892850194,\n",
       " 120998.95308979055,\n",
       " 67572.4010575006,\n",
       " 95514.08933111538,\n",
       " 75486.45649918634,\n",
       " 72596.15542134087,\n",
       " 74512.98144653268]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test dataset rmse values for each feature, k = 2\n",
    "lr_train_test_univariate(housing_dataset, k=5)[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plotting rmse Values**\n",
    "We will now plot rmse values for each feature as a bar plot for both training and test sets while splitting the data into k-folds between 2 and 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for training dataset\n",
    "# Remove features to avoid overfitting\n",
    "features = select_features(housing_dataset).drop(labels = [1, 2, 10]).reset_index(drop=True)\n",
    "fig = plt.figure(figsize=(16, 8))\n",
    "plt.style.use('fivethirtyeight')\n",
    "for i in range(2, 7): \n",
    "    plt.plot(lr_train_test_univariate(housing_dataset, k = i)[0], linewidth = 5, label = i)\n",
    "plt.xlabel(\"Feature\")\n",
    "plt.ylabel('RMSE')\n",
    "plt.xlim(1, len(features))\n",
    "plt.xticks(ticks = [i for i in range(1, len(features)+1)], labels = features)\n",
    "plt.legend()\n",
    "plt.title('Root Mean Squared Error for Univariate Cases- (k = 2 - 6), Training')\n",
    "plt.tight_layout() # tight_layout() prevents spill over of data and labels among subplots\n",
    "plt.rcParams['font.size'] = '10'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for test dataset\n",
    " # Remove features to avoid overfitting\n",
    "features = select_features(housing_dataset).drop(labels = [1, 2, 10]).reset_index(drop=True)\n",
    "fig = plt.figure(figsize=(16, 8))\n",
    "plt.style.use('fivethirtyeight')\n",
    "for i in range(2, 7): \n",
    "    plt.plot(lr_train_test_univariate(housing_dataset, k = i)[1], linewidth = 5, label = i)\n",
    "plt.xlabel(\"Feature\")\n",
    "plt.ylabel('RMSE')\n",
    "plt.xlim(1, len(features)+1)\n",
    "plt.xticks(ticks = [i for i in range(1, len(features)+1)], labels = features)\n",
    "plt.legend()\n",
    "plt.title('Root Mean Squared Error for Univariate Cases- (k = 2 - 6), Test')\n",
    "plt.tight_layout() # tight_layout() prevents spill over of data and labels among subplots\n",
    "plt.rcParams['font.size'] = '10'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Model Testing, Training and Tuning - II (Multivariate Cases)**\n",
    "\n",
    "- From consideration of univariate cases following conclusions can be drawn:\n",
    " ` Best performing models are split at k-fold value of 2 for training set and k-fold value of 3 for test set\n",
    " - Some features such as `Fireplaces`, `Lot Frontage`, `Open Porch SF`, `Wood Deck SF`, `Bsmt Full Bath`  give constant performance at all fold values, so we can drop some of these features to avoid overfitting\n",
    "\n",
    "- First, we will write a function for the multi-variate case\n",
    "- Then we will group selected features according to their rmse values in groups of 2, 3, 4,....n, where n is the total number of selected features, and train, test and cross-validate these multi-variate models at various k-fold values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lr_train_test_multivariate(df, k=2, ct=0.3, target = 'SalePrice'):\n",
    "    \"\"\" Cleans the dataset, imputes missing values, splits datasets into training and test sets according to number of folds,\n",
    "    selects features based on correlation threshhold among features and target, \n",
    "    creates feature_groups based on number of features \n",
    "    trains the model and predicts root mean squared error(RMSE) values for target features \n",
    "    Args:\n",
    "    df (matrix, dataframe): dataframe like or matrix like\n",
    "    k(int): k-folds for splitting dataset\n",
    "    ct(float): Pearson's correlation coefficient threshhold between features and target\n",
    "    target(array like): target df feature\n",
    "    Returns:\n",
    "    Array: RMSE values for the range of k=folds specified\n",
    "    \"\"\"\n",
    "    # Drop more features\n",
    "    features = select_features(df = df, k = k, ct = ct).drop(labels = [1, 2, 7, 10, 11, 14]).reset_index(drop=True) # Remove features to avoid overfitting\n",
    "    # Make a dictionary of feature_groups from groups of 2, 3, 4....n\n",
    "    feature_groups = {}\n",
    "    \n",
    "    for i in range(1, len(features)+1):\n",
    "        feature_groups[str(i+1)+'_features'] = list(features[0:i+1])\n",
    "   \n",
    "    # Use function transform_features to create training and test datasets\n",
    "    train = transform_features(df = df, k=k)[0][features]\n",
    "    # Add target Column\n",
    "    train[target] = transform_features(df = df, k=k)[0][target]\n",
    "    # Add target Column\n",
    "    test = transform_features(df = df, k = k)[1][features]\n",
    "    test[target] = transform_features(df = df, k = k)[1][target]\n",
    "    \n",
    "    lr = LinearRegression()\n",
    "    train_rmse_values = []\n",
    "    test_rmse_values = []\n",
    "    for group in feature_groups:   \n",
    "        lr.fit(train[feature_groups[group]], train[target])\n",
    "        train_predictions = lr.predict(train[feature_groups[group]]) # We need to predict for the training set as well\n",
    "        test_predictions = lr.predict(test[feature_groups[group]]) \n",
    "        train_mse = mean_squared_error(train_predictions, train[target])\n",
    "        test_mse = mean_squared_error(test_predictions, test[target])\n",
    "        train_rmse = np.sqrt(train_mse)\n",
    "        test_rmse = np.sqrt(test_mse)\n",
    "#         train_rmse_values.append(feature) # if needed to print feature names with rmse values\n",
    "        train_rmse_values.append(train_rmse)\n",
    "#         test_rmse_values.append(feature) # if needed to print feature names with rmse values\n",
    "        test_rmse_values.append(test_rmse)\n",
    "        \n",
    "    \n",
    "    return [train_rmse_values, test_rmse_values]      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[47388.73445708952,\n",
       " 43513.413617098675,\n",
       " 38527.77804417378,\n",
       " 38290.540009559954,\n",
       " 35602.888163140036,\n",
       " 35411.671814562134,\n",
       " 34975.419497995106,\n",
       " 34857.482293161935,\n",
       " 34857.482293161935]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training dataset rmse values for each feature, k = 2\n",
    "lr_train_test_multivariate(housing_dataset)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[62535.04010612586,\n",
       " 71832.90415262246,\n",
       " 67804.50436990509,\n",
       " 74715.39131489521,\n",
       " 73883.8463580121,\n",
       " 72007.14245834004,\n",
       " 72156.33312654687,\n",
       " 72514.74321376043,\n",
       " 72514.74321376043]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training dataset rmse values for each feature, k = 2\n",
    "lr_train_test_multivariate(housing_dataset)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Gr Liv Area\n",
       "1      Garage Area\n",
       "2       1st Flr SF\n",
       "3         age_sold\n",
       "4        Full Bath\n",
       "5     BsmtFin SF 1\n",
       "6       Fireplaces\n",
       "7    Open Porch SF\n",
       "8     Wood Deck SF\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drom more features and group features as 2, 3, 4 .... n features to test various multi-variate combinations\n",
    "features = select_features(housing_dataset).drop(labels = [1, 2, 7, 10, 11, 14]).reset_index(drop=True)\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "{'2_features': ['Gr Liv Area', 'Garage Area'], '3_features': ['Gr Liv Area', 'Garage Area', '1st Flr SF'], '4_features': ['Gr Liv Area', 'Garage Area', '1st Flr SF', 'age_sold'], '5_features': ['Gr Liv Area', 'Garage Area', '1st Flr SF', 'age_sold', 'Full Bath'], '6_features': ['Gr Liv Area', 'Garage Area', '1st Flr SF', 'age_sold', 'Full Bath', 'BsmtFin SF 1'], '7_features': ['Gr Liv Area', 'Garage Area', '1st Flr SF', 'age_sold', 'Full Bath', 'BsmtFin SF 1', 'Fireplaces'], '8_features': ['Gr Liv Area', 'Garage Area', '1st Flr SF', 'age_sold', 'Full Bath', 'BsmtFin SF 1', 'Fireplaces', 'Open Porch SF'], '9_features': ['Gr Liv Area', 'Garage Area', '1st Flr SF', 'age_sold', 'Full Bath', 'BsmtFin SF 1', 'Fireplaces', 'Open Porch SF', 'Wood Deck SF']}\n",
      "['2_features', '3_features', '4_features', '5_features', '6_features', '7_features', '8_features', '9_features']\n"
     ]
    }
   ],
   "source": [
    "# Make a dictionary of feature groups starting from \n",
    "feature_groups = {}\n",
    "for i in range(1, len(features)):\n",
    "    feature_groups[str(i+1)+'_features'] = list(features[0:i+1])\n",
    "print(len(feature_groups))\n",
    "print(feature_groups)  \n",
    "print(list(feature_groups.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for training dataset\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.style.use('fivethirtyeight')\n",
    "for i in range(2, 7): \n",
    "    plt.plot(lr_train_test_multivariate(housing_dataset, k = i)[0], linewidth = 5, label = i)\n",
    "plt.xlabel(\"Feature Groups\")\n",
    "plt.ylabel('RMSE')\n",
    "plt.xlim(1, len(feature_groups))\n",
    "# plt.xticks(ticks = [i for i in range(1, len(feature_groups)+1)], labels = list(feature_groups.keys()))\n",
    "plt.legend()\n",
    "plt.title('Root Mean Squared Error for Multivariate Cases- (k = 2 - 6), Training')\n",
    "plt.tight_layout() # tight_layout() prevents spill over of data and labels among subplots\n",
    "plt.rcParams['font.size'] = '10'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for training dataset\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.style.use('fivethirtyeight')\n",
    "for i in range(2, 7): \n",
    "    plt.plot(lr_train_test_multivariate(housing_dataset, k = i)[1], linewidth = 5, label = i)\n",
    "plt.xlabel(\"Feature Groups\")\n",
    "plt.ylabel('RMSE')\n",
    "plt.xlim(1, len(feature_groups))\n",
    "# plt.xticks(ticks = [i for i in range(1, len(feature_groups)+1)], labels = list(feature_groups.keys()))\n",
    "plt.legend()\n",
    "plt.title('Root Mean Squared Error for Multivariate Cases- (k = 2 - 6), Test')\n",
    "plt.tight_layout() # tight_layout() prevents spill over of data and labels among subplots\n",
    "plt.rcParams['font.size'] = '10'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k = 3,   9_features,   rmse:  [52801.24756555713, 50506.81098400353, 45336.246923056715, 45807.22457172923, 46002.057845993135, 44756.637487683394, 45125.1735033098, 44888.89377328727, 44888.89377328727]\n",
      "mean for k = 3:  46679.24293643417\n"
     ]
    }
   ],
   "source": [
    "# Print rmse values for best models as well as their mean value at various groupings of features\n",
    "print('k = 3, ', ' 9_features, ', ' rmse: ', lr_train_test_multivariate(housing_dataset, k = 3)[1])\n",
    "print('mean for k = 3: ', np.mean(lr_train_test_multivariate(housing_dataset, k = 3)[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "We can draw following conclusions from above:\n",
    "- The best performing models are the multivariate models which have near consistent performance in groups of 4 to 9 features\n",
    "- The best model is the one with 9-features split at 3 folds i.e. 66% training data and 34% test with an rmse of 44888. The mean SalePrice of the entire data is approximately 181,000. So, this model is considerably accurate \n",
    "- Performance of models deprecates appreciably if training data is increased beyond 3 folds\n",
    "- We can further fine-tune by manipulating correlation threshhold values"
   ]
  }
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