{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b7dcc692",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# @hidden_cell\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pprint import pprint\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "housing_df = pd.read_csv(\"../../data/Housing.csv\")\n",
    "housing_df['total_size'] = housing_df['floor_size']+housing_df['garage_size']\n",
    "# housing_df['price_per_unit_area'] = (housing_df['sold_price']/housing_df['total_size']).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fd99f35",
   "metadata": {},
   "source": [
    "# Feature Engineering and Selection in practice"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5aafb8b",
   "metadata": {},
   "source": [
    "We have discussed a lot of feature engineering and feature selection techniques in the previous two sections of this chapter. Now it is time to apply some of them in practice for an actual regression problem.\n",
    "\n",
    "We go back to the housing dataset that contains information on various property listings in Athens, Ohio. As a reminder, each listing is described through a number of features including floor area, garage area, bed room count etc. In addition, we also have information regarding a listing's selling price per unit area. We treat thus price as the outcome variable that we want to predict by fitting a linear regression model.  \n",
    "Let us first take a look at the features available to us. \n",
    "<!-- We also do not use `sold_price` as a feature, when training the model, since that is the information which has been used to calculate the response variable `price_per_unit_area` by dividing the former by the listing's total area. -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4da9e52e",
   "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>floor_size</th>\n",
       "      <th>bed_room_count</th>\n",
       "      <th>built_year</th>\n",
       "      <th>sold_date</th>\n",
       "      <th>sold_price</th>\n",
       "      <th>room_count</th>\n",
       "      <th>garage_size</th>\n",
       "      <th>parking_lot</th>\n",
       "      <th>total_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2068</td>\n",
       "      <td>3</td>\n",
       "      <td>2003</td>\n",
       "      <td>Aug2015</td>\n",
       "      <td>195500</td>\n",
       "      <td>6</td>\n",
       "      <td>768</td>\n",
       "      <td>3</td>\n",
       "      <td>2836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3372</td>\n",
       "      <td>3</td>\n",
       "      <td>1999</td>\n",
       "      <td>Dec2015</td>\n",
       "      <td>385000</td>\n",
       "      <td>6</td>\n",
       "      <td>480</td>\n",
       "      <td>2</td>\n",
       "      <td>3852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3130</td>\n",
       "      <td>3</td>\n",
       "      <td>1999</td>\n",
       "      <td>Jan2017</td>\n",
       "      <td>188000</td>\n",
       "      <td>7</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>3530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3991</td>\n",
       "      <td>3</td>\n",
       "      <td>1999</td>\n",
       "      <td>Nov2014</td>\n",
       "      <td>375000</td>\n",
       "      <td>8</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>4391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1450</td>\n",
       "      <td>2</td>\n",
       "      <td>1999</td>\n",
       "      <td>Jan2015</td>\n",
       "      <td>136000</td>\n",
       "      <td>7</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>1650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   floor_size  bed_room_count  built_year sold_date  sold_price  room_count  \\\n",
       "0        2068               3        2003   Aug2015      195500           6   \n",
       "1        3372               3        1999   Dec2015      385000           6   \n",
       "2        3130               3        1999   Jan2017      188000           7   \n",
       "3        3991               3        1999   Nov2014      375000           8   \n",
       "4        1450               2        1999   Jan2015      136000           7   \n",
       "\n",
       "   garage_size  parking_lot  total_size  \n",
       "0          768            3        2836  \n",
       "1          480            2        3852  \n",
       "2          400            2        3530  \n",
       "3          400            2        4391  \n",
       "4          200            1        1650  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0759b791",
   "metadata": {},
   "source": [
    "We also take a look at the different column types in the dataset and their corresponding non-null entry counts prior to proceeding with any feature engineering steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd698636",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 106 entries, 0 to 105\n",
      "Data columns (total 9 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   floor_size      106 non-null    int64 \n",
      " 1   bed_room_count  106 non-null    int64 \n",
      " 2   built_year      106 non-null    int64 \n",
      " 3   sold_date       106 non-null    object\n",
      " 4   sold_price      106 non-null    int64 \n",
      " 5   room_count      106 non-null    int64 \n",
      " 6   garage_size     106 non-null    int64 \n",
      " 7   parking_lot     106 non-null    int64 \n",
      " 8   total_size      106 non-null    int64 \n",
      "dtypes: int64(8), object(1)\n",
      "memory usage: 7.6+ KB\n"
     ]
    }
   ],
   "source": [
    "housing_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee13f9ca",
   "metadata": {},
   "source": [
    "We should also check for missing values and potential duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f40eeda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in each column:\n",
      "floor_size        0\n",
      "bed_room_count    0\n",
      "built_year        0\n",
      "sold_date         0\n",
      "sold_price        0\n",
      "room_count        0\n",
      "garage_size       0\n",
      "parking_lot       0\n",
      "total_size        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Missing values in each column:\")\n",
    "print(housing_df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7e9357a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of duplicate records: 1\n",
      "Number of records in deduplicated dataset: 105\n"
     ]
    }
   ],
   "source": [
    "duplicate_count = housing_df.duplicated().sum()\n",
    "print(f\"\\nNumber of duplicate records: {duplicate_count}\")\n",
    "housing_df = housing_df.drop_duplicates()\n",
    "print(\"Number of records in deduplicated dataset:\",len(housing_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7129e96",
   "metadata": {},
   "source": [
    "Prior to looking into possible feature engineering strategies, let us look at our target variable more closely. Here we plot the distribution of the `sold_price` column using a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b1d554e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sold Price Statistics:\n",
      "Min: 87000\n",
      "Max: 550000\n",
      "Mean: 250857.62\n",
      "Median: 233000.00\n",
      "Lower Quartile: 156000.00\n",
      "Upper Quartile: 315000.00\n",
      "Standard deviation: 101458.82\n",
      "Skewness: 0.69\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(housing_df['sold_price'], bins=40, kde=True, color='royalblue')\n",
    "plt.title('Distribution of House Sale Prices')\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n",
    "\n",
    "print(f\"Sold Price Statistics:\")\n",
    "print(f\"Min: {housing_df['sold_price'].min()}\")\n",
    "print(f\"Max: {housing_df['sold_price'].max()}\")\n",
    "print(f\"Mean: {housing_df['sold_price'].mean():.2f}\")\n",
    "print(f\"Median: {housing_df['sold_price'].median():.2f}\")\n",
    "print(f\"Lower Quartile: {housing_df['sold_price'].quantile(0.25):.2f}\")\n",
    "print(f\"Upper Quartile: {housing_df['sold_price'].quantile(0.75):.2f}\")\n",
    "print(f\"Standard deviation: {housing_df['sold_price'].std():.2f}\")\n",
    "print(f\"Skewness: {housing_df['sold_price'].skew():.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b45ccae2",
   "metadata": {},
   "source": [
    "The histogram plot of listing sale prices reveal the following things:\n",
    "\n",
    "1. The price distribution is right-skewed with a large cluster of prices in the lower ranges of prices with the tail of the distribution extending towards the higher prices. We can also see that the median is closer to the lower quartile.\n",
    "2. There are a few property prices with very high prices in the 500K range but we should probably not assume them to be outliers.\n",
    "\n",
    "The right-skewed nature of the price distribution suggests we should apply logarithmic transformation on the target variable, `sold_price`, and check if that lends normality to the price distribution. Log transform often gives us more symmetric distributions that are more stable to work with when fitting a model. So let's go ahead and do that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "367aa9fe",
   "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>floor_size</th>\n",
       "      <th>bed_room_count</th>\n",
       "      <th>built_year</th>\n",
       "      <th>sold_date</th>\n",
       "      <th>sold_price</th>\n",
       "      <th>room_count</th>\n",
       "      <th>garage_size</th>\n",
       "      <th>parking_lot</th>\n",
       "      <th>total_size</th>\n",
       "      <th>sold_price_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2068</td>\n",
       "      <td>3</td>\n",
       "      <td>2003</td>\n",
       "      <td>Aug2015</td>\n",
       "      <td>195500</td>\n",
       "      <td>6</td>\n",
       "      <td>768</td>\n",
       "      <td>3</td>\n",
       "      <td>2836</td>\n",
       "      <td>12.183316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3372</td>\n",
       "      <td>3</td>\n",
       "      <td>1999</td>\n",
       "      <td>Dec2015</td>\n",
       "      <td>385000</td>\n",
       "      <td>6</td>\n",
       "      <td>480</td>\n",
       "      <td>2</td>\n",
       "      <td>3852</td>\n",
       "      <td>12.860999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3130</td>\n",
       "      <td>3</td>\n",
       "      <td>1999</td>\n",
       "      <td>Jan2017</td>\n",
       "      <td>188000</td>\n",
       "      <td>7</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>3530</td>\n",
       "      <td>12.144197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3991</td>\n",
       "      <td>3</td>\n",
       "      <td>1999</td>\n",
       "      <td>Nov2014</td>\n",
       "      <td>375000</td>\n",
       "      <td>8</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>4391</td>\n",
       "      <td>12.834681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1450</td>\n",
       "      <td>2</td>\n",
       "      <td>1999</td>\n",
       "      <td>Jan2015</td>\n",
       "      <td>136000</td>\n",
       "      <td>7</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>1650</td>\n",
       "      <td>11.820410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   floor_size  bed_room_count  built_year sold_date  sold_price  room_count  \\\n",
       "0        2068               3        2003   Aug2015      195500           6   \n",
       "1        3372               3        1999   Dec2015      385000           6   \n",
       "2        3130               3        1999   Jan2017      188000           7   \n",
       "3        3991               3        1999   Nov2014      375000           8   \n",
       "4        1450               2        1999   Jan2015      136000           7   \n",
       "\n",
       "   garage_size  parking_lot  total_size  sold_price_log  \n",
       "0          768            3        2836       12.183316  \n",
       "1          480            2        3852       12.860999  \n",
       "2          400            2        3530       12.144197  \n",
       "3          400            2        4391       12.834681  \n",
       "4          200            1        1650       11.820410  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df['sold_price_log'] = np.log(housing_df[\"sold_price\"])\n",
    "housing_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "15eec391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "# housing_df['sold_price'].plot(kind='hist', bins=10, title='Distribution of House Sale Prices')\n",
    "sns.histplot(housing_df['sold_price_log'], bins=40, kde=True, color='red')\n",
    "plt.title('Distribution of House Sale Prices (Log Sale)')\n",
    "plt.xlabel('Log Price')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf16f574",
   "metadata": {},
   "source": [
    "Most of the features are straightforward and ready to be used. This minimizes our job when it comes to transforming existing features into custom ones. However, it makes sense to manipulate the `sold_date` feature to a new feature: `property_age`. So let us do that first.\n",
    "We first separate the sale year from the `sold_date` feature and then subtract the `built_year` feature from it to calculate the age of the property."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8106215a",
   "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>floor_size</th>\n",
       "      <th>bed_room_count</th>\n",
       "      <th>built_year</th>\n",
       "      <th>sold_date</th>\n",
       "      <th>sold_price</th>\n",
       "      <th>room_count</th>\n",
       "      <th>garage_size</th>\n",
       "      <th>parking_lot</th>\n",
       "      <th>total_size</th>\n",
       "      <th>sold_price_log</th>\n",
       "      <th>sale_year</th>\n",
       "      <th>property_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2068</td>\n",
       "      <td>3</td>\n",
       "      <td>2003</td>\n",
       "      <td>Aug2015</td>\n",
       "      <td>195500</td>\n",
       "      <td>6</td>\n",
       "      <td>768</td>\n",
       "      <td>3</td>\n",
       "      <td>2836</td>\n",
       "      <td>12.183316</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3372</td>\n",
       "      <td>3</td>\n",
       "      <td>1999</td>\n",
       "      <td>Dec2015</td>\n",
       "      <td>385000</td>\n",
       "      <td>6</td>\n",
       "      <td>480</td>\n",
       "      <td>2</td>\n",
       "      <td>3852</td>\n",
       "      <td>12.860999</td>\n",
       "      <td>2015</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3130</td>\n",
       "      <td>3</td>\n",
       "      <td>1999</td>\n",
       "      <td>Jan2017</td>\n",
       "      <td>188000</td>\n",
       "      <td>7</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>3530</td>\n",
       "      <td>12.144197</td>\n",
       "      <td>2017</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3991</td>\n",
       "      <td>3</td>\n",
       "      <td>1999</td>\n",
       "      <td>Nov2014</td>\n",
       "      <td>375000</td>\n",
       "      <td>8</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>4391</td>\n",
       "      <td>12.834681</td>\n",
       "      <td>2014</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1450</td>\n",
       "      <td>2</td>\n",
       "      <td>1999</td>\n",
       "      <td>Jan2015</td>\n",
       "      <td>136000</td>\n",
       "      <td>7</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>1650</td>\n",
       "      <td>11.820410</td>\n",
       "      <td>2015</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   floor_size  bed_room_count  built_year sold_date  sold_price  room_count  \\\n",
       "0        2068               3        2003   Aug2015      195500           6   \n",
       "1        3372               3        1999   Dec2015      385000           6   \n",
       "2        3130               3        1999   Jan2017      188000           7   \n",
       "3        3991               3        1999   Nov2014      375000           8   \n",
       "4        1450               2        1999   Jan2015      136000           7   \n",
       "\n",
       "   garage_size  parking_lot  total_size  sold_price_log  sale_year  \\\n",
       "0          768            3        2836       12.183316       2015   \n",
       "1          480            2        3852       12.860999       2015   \n",
       "2          400            2        3530       12.144197       2017   \n",
       "3          400            2        4391       12.834681       2014   \n",
       "4          200            1        1650       11.820410       2015   \n",
       "\n",
       "   property_age  \n",
       "0            12  \n",
       "1            16  \n",
       "2            18  \n",
       "3            15  \n",
       "4            16  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_sale_year(sell_date):\n",
    "    '''Takes the sell date and extracts the year'''\n",
    "    return int(sell_date[3:])\n",
    "\n",
    "def get_property_age(row):\n",
    "    '''Returns the age of the listing'''\n",
    "    built_year = row['built_year']\n",
    "    sale_year = row['sale_year']\n",
    "    return sale_year-built_year\n",
    "\n",
    "housing_df['sold_date'] = housing_df['sold_date'].str.strip()\n",
    "housing_df['sale_year'] = housing_df['sold_date'].apply(get_sale_year)\n",
    "housing_df['property_age'] = housing_df[['built_year', 'sale_year']].apply(get_property_age, axis=1)\n",
    "housing_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a0991df",
   "metadata": {},
   "source": [
    "In addition, we also plot the data distributions of some of the numeric features that could be essential to our model going forward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "14e9144d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = ['floor_size','bed_room_count','built_year','room_count','garage_size','parking_lot','total_size','property_age']\n",
    "plt.figure(figsize=(20, 10), frameon=False)\n",
    "# Examine distribution of numerical features\n",
    "for i, col in enumerate(features):\n",
    "    plt.subplot(2, 4, i+1)\n",
    "    sns.histplot(housing_df[col], kde=True)\n",
    "    plt.xlabel(col)\n",
    "    plt.ylabel('Frequency')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf8aa3e1",
   "metadata": {},
   "source": [
    "We also notice that the distributions for `floor_size` and `garage_size` exhibit right skewedness. Hence we apply similar log transformations to each of these two features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2fad481a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing_df['floor_size_log'] = np.log(housing_df['floor_size'])\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.histplot(housing_df['floor_size_log'].dropna(), kde=True, color='darkgreen')\n",
    "plt.title(\"Distribution of Floor Size (Log Scale)\")\n",
    "plt.xlabel(\"Log of Floor Size\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fb34c32b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing_df['garage_size_log'] = np.log(housing_df['garage_size'])\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.histplot(housing_df['garage_size_log'].dropna(), kde=True, color='purple')\n",
    "plt.title(\"Distribution of Garage Size (Log Scale)\")\n",
    "plt.xlabel(\"Log of Garage Size\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b180e732",
   "metadata": {},
   "source": [
    "We also do not have any categorical feature in the data that would require additional transformation. Finally, we notice that the feature `total_size` is computed by adding the feature values from `floor_size` and `garage_size`. Hence it is important to **drop** the `total_size` feature. Otherwise, we introduce the issue of **multicollinearity** -- a phenomenon where two or more features have a linear relationship. In this case, the linear relationship goes as follows: \n",
    ">  $total\\_size = floor\\_size + garage\\_size$\n",
    "\n",
    "Multicollinearity shows the presence of highly correlated predictors such that change in values of one feature can impact the other. Multicollinearity can lead to unstable regression coefficients and makes it difficult to determine the impact of each predictor on the response variable.  \n",
    "With the feature transformations concluded above, our feature engineering steps will now consist of standardizing the following numeric features:\n",
    "- `floor_size_log`\n",
    "- `bed_room_count`\n",
    "- `built_year`\n",
    "- `room_count`\n",
    "- `garage_size_log`\n",
    "- `parking_lot`\n",
    "- `property_age`\n",
    "\n",
    "We also include the column for the original target variable `sold_price`, but this is for record keeping purposes so that we can evaluate the performance of the predicted values on the original scale instead of the log scale. We will be dropping this column prior to sending the other features for model fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "34529c41",
   "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>floor_size_log</th>\n",
       "      <th>bed_room_count</th>\n",
       "      <th>built_year</th>\n",
       "      <th>room_count</th>\n",
       "      <th>garage_size_log</th>\n",
       "      <th>parking_lot</th>\n",
       "      <th>property_age</th>\n",
       "      <th>sold_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.634337</td>\n",
       "      <td>3</td>\n",
       "      <td>2003</td>\n",
       "      <td>6</td>\n",
       "      <td>6.643790</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>195500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.123261</td>\n",
       "      <td>3</td>\n",
       "      <td>1999</td>\n",
       "      <td>6</td>\n",
       "      <td>6.173786</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>385000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.048788</td>\n",
       "      <td>3</td>\n",
       "      <td>1999</td>\n",
       "      <td>7</td>\n",
       "      <td>5.991465</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>188000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.291797</td>\n",
       "      <td>3</td>\n",
       "      <td>1999</td>\n",
       "      <td>8</td>\n",
       "      <td>5.991465</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.279319</td>\n",
       "      <td>2</td>\n",
       "      <td>1999</td>\n",
       "      <td>7</td>\n",
       "      <td>5.298317</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>136000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   floor_size_log  bed_room_count  built_year  room_count  garage_size_log  \\\n",
       "0        7.634337               3        2003           6         6.643790   \n",
       "1        8.123261               3        1999           6         6.173786   \n",
       "2        8.048788               3        1999           7         5.991465   \n",
       "3        8.291797               3        1999           8         5.991465   \n",
       "4        7.279319               2        1999           7         5.298317   \n",
       "\n",
       "   parking_lot  property_age  sold_price  \n",
       "0            3            12      195500  \n",
       "1            2            16      385000  \n",
       "2            2            18      188000  \n",
       "3            2            15      375000  \n",
       "4            1            16      136000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_list = ['floor_size_log','bed_room_count','built_year','room_count','garage_size_log','parking_lot','property_age','sold_price']\n",
    "housing_data_df = housing_df[column_list]\n",
    "housing_data_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "653dac69",
   "metadata": {},
   "source": [
    "The response variable for this problem are the prices of the listings in our dataset but in the log scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "62927a23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    12.183316\n",
       "1    12.860999\n",
       "2    12.144197\n",
       "3    12.834681\n",
       "4    11.820410\n",
       "Name: sold_price_log, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_data_prices_log = housing_df['sold_price_log']\n",
    "housing_data_prices_log.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20ca8e8e",
   "metadata": {},
   "source": [
    "Prior to applying standardization to numeric features, we split the dataset into training and test data. We do that by using the `train_test_split` function and also specifying the fraction of the whole data we want to use for testing. This is done to prevent any information leakage between training and test partitions that could inadvertently lead to over-optimistic performance projections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "28134403",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_data_df_train, housing_data_df_test, housing_data_prices_log_train, housing_data_prices_log_test = train_test_split(housing_data_df, housing_data_prices_log, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2dcf142",
   "metadata": {},
   "source": [
    "Now we calculate the mean and standard deviation of each numeric feature from the training split only and use the same to standardize each feature distribution in both the training and test data splits.  \n",
    "We use `StandardScaler` from the `scikit-learn` module. The `fit_transform` function calculates the means and standard deviations on the training data and then applies them to scale the feature values. \n",
    "At the end of this step, each feature distribution is transformed into one with 0 mean and unit variance.  \n",
    "Prior to that, we drop the column containing the original sale prices since we do not want them to be scaled and included with the rest of the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "37b6c218",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the column with original sale prices from both training and test\n",
    "column_to_remove = 'sold_price'\n",
    "X_train = housing_data_df_train.drop(column_to_remove, axis=1)\n",
    "X_test = housing_data_df_test.drop(column_to_remove, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7d075e28",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        -1.55561249,  0.11973039],\n",
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       "         0.09845649, -2.63793092],\n",
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       "         0.09845649, -0.36691572],\n",
       "       [ 0.70207187,  0.3723334 , -0.90532223,  0.91457265,  1.78047765,\n",
       "         1.75252546,  0.93080725],\n",
       "       [ 0.40921641,  0.3723334 ,  1.08717735,  0.36188127, -0.21847199,\n",
       "         0.09845649, -0.85356183],\n",
       "       [ 0.94157287,  1.41486693,  0.25696919,  0.36188127, -0.21847199,\n",
       "         0.09845649, -0.36691572],\n",
       "       [ 1.12226188, -0.67020012, -0.07511407, -0.74350151,  0.22372951,\n",
       "         0.09845649, -0.04248498],\n",
       "       [ 1.74009163,  1.41486693, -0.90532223,  2.57264681,  1.23394208,\n",
       "         1.75252546,  0.93080725],\n",
       "       [ 0.21245591, -0.67020012, -0.90532223,  2.01995543, -0.21847199,\n",
       "         0.09845649,  0.93080725],\n",
       "       [ 0.91801663, -0.67020012, -0.07511407, -0.19081012, -0.21847199,\n",
       "         0.09845649,  0.28194576],\n",
       "       [ 0.21811647,  0.3723334 ,  1.08717735,  0.36188127,  0.24385737,\n",
       "         0.09845649, -1.17799258],\n",
       "       [-0.68281895,  0.3723334 ,  0.58905245, -0.74350151, -0.85238435,\n",
       "        -1.55561249, -0.36691572],\n",
       "       [ 1.41657998,  2.45740046,  1.08717735,  0.91457265,  0.97024381,\n",
       "         1.75252546, -1.17799258],\n",
       "       [ 0.65522239,  0.3723334 , -1.56948875,  0.36188127,  0.35167038,\n",
       "         0.09845649,  1.41745336],\n",
       "       [ 0.55105036, -0.67020012, -0.07511407, -0.19081012, -0.21847199,\n",
       "         1.75252546,  0.11973039],\n",
       "       [-0.36573471, -0.67020012,  0.25696919, -1.2961929 ,  0.665931  ,\n",
       "         0.09845649, -0.20470035],\n",
       "       [ 0.83077498,  0.3723334 , -1.23740549,  0.36188127,  0.665931  ,\n",
       "         1.75252546,  1.09302262],\n",
       "       [-2.46394141, -1.71273365,  1.25321898, -1.84888428,  0.26878455,\n",
       "         0.09845649, -1.17799258],\n",
       "       [ 0.03454968,  0.3723334 , -0.90532223,  0.36188127, -0.21847199,\n",
       "         0.09845649,  1.09302262],\n",
       "       [ 0.27519648,  0.3723334 ,  1.08717735,  0.91457265,  0.01269269,\n",
       "         0.09845649, -1.34020795],\n",
       "       [-0.14019346, -0.67020012, -0.2411557 , -0.74350151, -0.64134753,\n",
       "        -1.55561249, -0.04248498],\n",
       "       [ 0.28183433, -0.67020012, -0.40719733, -0.19081012,  0.41786456,\n",
       "         0.09845649,  0.60637651],\n",
       "       [ 0.952843  ,  0.3723334 ,  0.42301082,  2.01995543,  0.665931  ,\n",
       "         0.09845649, -0.36691572]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "778871fd",
   "metadata": {},
   "source": [
    "For the test split we use the `transform` function on the existing scaler. This standardizes feature values in the test data by applying the same transformations derived from the feature distributions in the training set. Note that it is important to **not** refit the scaler again, separately, on the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "569b7c4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.88096664,  0.3723334 ,  0.25696919,  0.36188127,  1.35734882,\n",
       "         1.75252546, -0.36691572],\n",
       "       [ 0.18969611, -0.67020012,  1.08717735, -1.2961929 ,  0.6148682 ,\n",
       "         0.09845649, -1.17799258],\n",
       "       [ 0.21472153,  0.3723334 ,  1.41926061, -0.19081012, -0.21847199,\n",
       "         0.09845649, -1.17799258],\n",
       "       [ 0.40499387,  0.3723334 ,  1.08717735,  0.36188127, -0.21847199,\n",
       "         0.09845649, -1.17799258],\n",
       "       [-0.97596622, -1.71273365,  0.42301082, -1.2961929 , -1.89962655,\n",
       "        -1.55561249, -0.36691572],\n",
       "       [ 0.55405369,  1.41486693, -1.40344712,  0.36188127, -0.21847199,\n",
       "         0.09845649,  1.41745336],\n",
       "       [-0.86463962, -1.71273365,  0.25696919, -0.74350151, -0.21847199,\n",
       "         0.09845649, -0.52913109],\n",
       "       [ 1.50004251,  1.41486693,  0.09092756, -0.19081012, -0.21847199,\n",
       "         0.09845649, -0.20470035],\n",
       "       [ 0.6114808 ,  1.41486693,  0.25696919, -0.19081012, -0.21847199,\n",
       "         0.09845649, -0.36691572],\n",
       "       [-0.21863178, -0.67020012,  0.58905245, -0.74350151,  1.36367318,\n",
       "         1.75252546, -0.69134646],\n",
       "       [ 0.57299877,  0.3723334 , -0.07511407,  0.36188127, -0.21847199,\n",
       "         0.09845649,  0.11973039],\n",
       "       [-0.80158996, -0.67020012, -0.2411557 , -1.2961929 ,  0.11522737,\n",
       "         0.09845649,  0.28194576],\n",
       "       [-0.35456345, -0.67020012,  0.58905245, -0.19081012, -1.22626038,\n",
       "        -1.55561249, -0.69134646],\n",
       "       [ 0.97867559,  1.41486693, -0.90532223, -0.19081012, -0.21847199,\n",
       "         0.09845649,  1.09302262],\n",
       "       [ 0.25964538,  0.3723334 ,  1.41926061,  1.46726404, -1.89962655,\n",
       "        -1.55561249, -1.17799258],\n",
       "       [-1.19228381, -1.71273365, -0.07511407, -0.19081012, -1.89962655,\n",
       "        -1.55561249, -0.04248498],\n",
       "       [ 0.3060366 ,  0.3723334 ,  0.92113571, -0.19081012,  0.01269269,\n",
       "         0.09845649, -0.85356183],\n",
       "       [-1.49387765, -1.71273365,  0.09092756, -1.2961929 , -1.89962655,\n",
       "        -1.55561249, -0.36691572],\n",
       "       [-0.59928337, -0.67020012,  0.42301082, -0.19081012,  1.84347843,\n",
       "         3.40659443, -0.69134646],\n",
       "       [ 1.11248455, -0.67020012, -1.23740549,  1.46726404, -0.21847199,\n",
       "         0.09845649,  1.09302262],\n",
       "       [-1.10296662, -0.67020012,  0.09092756, -0.74350151, -1.89962655,\n",
       "        -1.55561249, -0.04248498]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_scaled = scaler.transform(X_test)\n",
    "X_test_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7b669c4",
   "metadata": {},
   "source": [
    "Now let us fit a linear regression model to predict the housing prices from the available features as well as identify the most important features in the data that enables us to do so. To do this, we will use `SequentialFeatureSelector` from the `mlxtend` module. The `SequentialFeatureSelector` is capable of performing either forward selection or backward elimination of features depending on their importance in predicting the response variable. Both techniques are variations of a greedy feature selection process that has been explained in the [previous section](../2/feature_selection.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a8d37881",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
    "from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82400619",
   "metadata": {},
   "source": [
    "We start by initializing the `SequentialFeatureSelector` with a model instance, as is appropriate for the prediction problem at hand. Here our model of choice is Linear Regression. In addition, we set the values of a few more important parameters.\n",
    "- $k\\_features$ : We can either limit the number of features to be selected from the feature set or set this parameter to $best$ in which case the feature selector will choose the subset that yields the best cross-validation performance.\n",
    "- $forward$ : When set to $True$ the feature selector runs the forward selection algorithm. Otherwise, it can be set to $False$ when backward elimination is executed.\n",
    "- $scoring$ : A number of accuracy/error metrics are available to evaluate the model performance with each feature set that is iteratively selected during sequential feature selection. For this regression problem, we choose the $R^2$ metric. Alternatives include mean squared error, mean absolute error, etc.\n",
    "- $cv$: When set to an integer $k$, the selector will use $k$-fold cross validation to split data into training and validation splits. Intermediate versions of the model are iteratively trained with different feature sets on the training splits and evaluated on the validation split. We will discuss cross-validation in greater detail in the {ref}`next chapter <sec-cross-validation-and-regularization>`.\n",
    "\n",
    "Once the feature selector has been appropriately initialized, we call the `fit` function to perform sequential feature selection alongside the model of choice and determine the optimal feature subset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cc5f2b3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()\n",
    "\n",
    "sfs = SFS(lr, \n",
    "          k_features='best', \n",
    "          forward=True, \n",
    "          floating=False, \n",
    "          scoring='r2',\n",
    "          cv=3)\n",
    "\n",
    "sfs = sfs.fit(X_train_scaled, housing_data_prices_log_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "436eb07c",
   "metadata": {},
   "source": [
    "To get a breakdown of the feature selector's execution at the end of each iteration, we can call the `get_metric_dict` function. Here we print the execution report in the form of a dataframe. We see the feature selected in each iteration and how the model performance, as given by the average cross-validation score, changes accordingly. As seen below, the optimal feature set for the housing prices prediction problem seems to be selected in the fourth iteration as the corresponding average cross-validation score, using the $R^2$ metric is highest for that iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "360bf310",
   "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>feature_idx</th>\n",
       "      <th>cv_scores</th>\n",
       "      <th>avg_score</th>\n",
       "      <th>feature_names</th>\n",
       "      <th>ci_bound</th>\n",
       "      <th>std_dev</th>\n",
       "      <th>std_err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(0,)</td>\n",
       "      <td>[0.8011541407669223, 0.5979697556955595, 0.518...</td>\n",
       "      <td>0.639148</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>0.267973</td>\n",
       "      <td>0.119082</td>\n",
       "      <td>0.084203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(0, 5)</td>\n",
       "      <td>[0.7373225274223243, 0.6709184297106074, 0.603...</td>\n",
       "      <td>0.670642</td>\n",
       "      <td>(0, 5)</td>\n",
       "      <td>0.122773</td>\n",
       "      <td>0.054558</td>\n",
       "      <td>0.038578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(0, 1, 5)</td>\n",
       "      <td>[0.7261841998683258, 0.6921919520698298, 0.615...</td>\n",
       "      <td>0.678081</td>\n",
       "      <td>(0, 1, 5)</td>\n",
       "      <td>0.103804</td>\n",
       "      <td>0.046129</td>\n",
       "      <td>0.032618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(0, 1, 2, 5)</td>\n",
       "      <td>[0.7160295872355962, 0.7049961004106151, 0.627...</td>\n",
       "      <td>0.682688</td>\n",
       "      <td>(0, 1, 2, 5)</td>\n",
       "      <td>0.089129</td>\n",
       "      <td>0.039607</td>\n",
       "      <td>0.028006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(0, 1, 2, 5, 6)</td>\n",
       "      <td>[0.706748962481389, 0.7051984593000533, 0.6222...</td>\n",
       "      <td>0.678081</td>\n",
       "      <td>(0, 1, 2, 5, 6)</td>\n",
       "      <td>0.088778</td>\n",
       "      <td>0.039451</td>\n",
       "      <td>0.027896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(0, 1, 2, 3, 5, 6)</td>\n",
       "      <td>[0.707059205186533, 0.6965157705757061, 0.6107...</td>\n",
       "      <td>0.671426</td>\n",
       "      <td>(0, 1, 2, 3, 5, 6)</td>\n",
       "      <td>0.097109</td>\n",
       "      <td>0.043153</td>\n",
       "      <td>0.030514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6)</td>\n",
       "      <td>[0.6816988966656055, 0.6713140577899961, 0.581...</td>\n",
       "      <td>0.644916</td>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6)</td>\n",
       "      <td>0.100986</td>\n",
       "      <td>0.044876</td>\n",
       "      <td>0.031732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             feature_idx                                          cv_scores  \\\n",
       "1                   (0,)  [0.8011541407669223, 0.5979697556955595, 0.518...   \n",
       "2                 (0, 5)  [0.7373225274223243, 0.6709184297106074, 0.603...   \n",
       "3              (0, 1, 5)  [0.7261841998683258, 0.6921919520698298, 0.615...   \n",
       "4           (0, 1, 2, 5)  [0.7160295872355962, 0.7049961004106151, 0.627...   \n",
       "5        (0, 1, 2, 5, 6)  [0.706748962481389, 0.7051984593000533, 0.6222...   \n",
       "6     (0, 1, 2, 3, 5, 6)  [0.707059205186533, 0.6965157705757061, 0.6107...   \n",
       "7  (0, 1, 2, 3, 4, 5, 6)  [0.6816988966656055, 0.6713140577899961, 0.581...   \n",
       "\n",
       "  avg_score          feature_names  ci_bound   std_dev   std_err  \n",
       "1  0.639148                   (0,)  0.267973  0.119082  0.084203  \n",
       "2  0.670642                 (0, 5)  0.122773  0.054558  0.038578  \n",
       "3  0.678081              (0, 1, 5)  0.103804  0.046129  0.032618  \n",
       "4  0.682688           (0, 1, 2, 5)  0.089129  0.039607  0.028006  \n",
       "5  0.678081        (0, 1, 2, 5, 6)  0.088778  0.039451  0.027896  \n",
       "6  0.671426     (0, 1, 2, 3, 5, 6)  0.097109  0.043153  0.030514  \n",
       "7  0.644916  (0, 1, 2, 3, 4, 5, 6)  0.100986  0.044876  0.031732  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfs_metric_df = pd.DataFrame.from_dict(sfs.get_metric_dict()).T\n",
    "sfs_metric_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28f76f3b",
   "metadata": {},
   "source": [
    "To get a sense of the actual features being selected, we can also print their names by following the indices of the selected features in the list of features that were initially passed to the feature selector. For the current problem, the optimal feature set include 4 features as determined by the sequential feature selector:\n",
    "- `floor_size_log`\n",
    "- `bed_room_count`\n",
    "- `built_year`\n",
    "- `parking_lot`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dd13cd46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k: 4\n",
      "['floor_size_log', 'bed_room_count', 'built_year', 'parking_lot']\n"
     ]
    }
   ],
   "source": [
    "k = len(sfs.k_feature_names_)\n",
    "print(f'k: {k}')\n",
    "pprint([column_list[int(feature_index)] for feature_index in list(sfs.k_feature_names_)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89959e6a",
   "metadata": {},
   "source": [
    "We can also visualize the iterative feature selection process by using the `plot_sfs` function available within the `mlxtend` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "792c45cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6391475579206499 0.682688058191487\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_r2_score = sfs_metric_df['avg_score'].min()\n",
    "max_r2_score = sfs_metric_df['avg_score'].max()\n",
    "margin = (max_r2_score - min_r2_score)*0.1\n",
    "print(min_r2_score, max_r2_score)\n",
    "\n",
    "fig = plot_sfs(sfs.get_metric_dict(), ylabel='R^2')\n",
    "plt.ylim([min_r2_score-margin, max_r2_score+margin])\n",
    "plt.title('Sequential Forward Selection')\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17dad5e4",
   "metadata": {},
   "source": [
    "Once the optimal feature set has been selected, the linear regression model is trained on the whole training data. First we separate the features suggested by the selector from the whole training data. The `sfs.transform()` function accomplishes this. Next we call the `fit` function on the linear regression model to train it on the selected features and corresponding target variable values, as obtained from the training split. Here, we print the model coefficients associated with each feature at the end of model training as well as the intercept term.  \n",
    "We also compute the $R^2$ score on the training fit to compare it with the test performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "25a28d56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [0.24111826 0.04644596 0.03113367 0.09764008]\n",
      "Intercept: 12.388289869468666\n",
      "\n",
      "TRAIN R2: 0.682688058191487\n",
      "TRAIN ADJUSTED R2: 0.6666216307581445\n"
     ]
    }
   ],
   "source": [
    "X_train_scaled_sfs = sfs.transform(X_train_scaled)\n",
    "lr.fit(X_train_scaled_sfs, housing_data_prices_log_train)\n",
    "\n",
    "k = len(sfs.k_feature_names_)\n",
    "n = X_train_scaled.shape[0]\n",
    "r2 = sfs.k_score_\n",
    "adj_r2 = 1 - (1 - sfs.k_score_) * ((n - 1) / (n - k - 1))\n",
    "\n",
    "# Print the coefficients and intercept\n",
    "print(f\"Coefficients: {lr.coef_}\")\n",
    "print(f\"Intercept: {lr.intercept_}\")\n",
    "print(\"\")\n",
    "print(f'TRAIN R2: {r2}')\n",
    "print(f'TRAIN ADJUSTED R2: {adj_r2}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f5ba5e7",
   "metadata": {},
   "source": [
    "Upon fitting the model, we can now use it to make prediction on the test data. Again we use the `sfs.transform()` function to select the subset of features that was deemed to be optimal by the selector from the test data. Next we call the `predict` function on the fitted model and pass the feature subset from the test data to get our predictions. Note that for this problem, we deemed it appropriate to transform the response variable, `sold_price` from the absolute scale to the log scale. This was done to obtain a more symmetric distribution of the response variable. Hence the predicted values obtained from our model on the test data are also not the actual sale prices but those on the log scale.  \n",
    "We print the $R2$ score on the test predictions to compare the change in performance from training to test data. In this case, the $R^2$ score dropped from 0.67 on the training data to 0.31 on the test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f6caafe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TEST R2: 0.3070768940772879\n",
      "TEST ADJUSTED R2: 0.2719921798533531\n"
     ]
    }
   ],
   "source": [
    "X_test_scaled_sfs = sfs.transform(X_test_scaled)\n",
    "y_pred_log = lr.predict(X_test_scaled_sfs)\n",
    "test_r2 = r2_score(housing_data_prices_log_test, y_pred_log)\n",
    "adj_test_r2 = 1 - (1 - test_r2) * ((n - 1) / (n - k - 1))\n",
    "print(f'TEST R2: {test_r2}')\n",
    "print(f'TEST ADJUSTED R2: {adj_test_r2}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83e4aeef",
   "metadata": {},
   "source": [
    "Due to the initial log-transformation conducted on the target variable, the fitted model predicts selling prices on the log-scale. Thus for us to generate the predicted price of a listing in the original scale, we need to perform an inverse exponential transformation on the predicted values. Once that is done, we compare the predicted values with the actual values of the `sold_price` column in the test data.  \n",
    "We use root mean squared error (**RMSE**) as the metric of choice to determine the deviation between the predicted selling prices and the actual prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "472e49b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 65,749\n",
      "\n",
      "Sold price predictions:\n",
      "Actual_price Predicted_price\n",
      "     300,000         361,175\n",
      "      87,000         254,323\n",
      "     384,000         271,348\n",
      "     222,000         281,165\n",
      "     140,000         152,466\n",
      "     270,000         283,089\n",
      "     158,000         183,116\n",
      "     375,000         372,554\n",
      "     315,000         302,264\n",
      "     195,500         266,706\n",
      "     200,000         282,383\n",
      "     117,300         192,142\n",
      "     152,000         186,861\n",
      "     290,000         318,510\n",
      "     267,500         233,394\n",
      "     136,000         142,490\n",
      "     210,200         273,120\n",
      "     135,000         133,183\n",
      "     155,000         284,490\n",
      "     310,700         295,520\n",
      "     145,000         153,608\n"
     ]
    }
   ],
   "source": [
    "y_pred = np.exp(y_pred_log)\n",
    "rmse = np.sqrt(mean_squared_error(housing_data_df_test['sold_price'].values, y_pred))\n",
    "\n",
    "output_df = pd.DataFrame({\n",
    "    \"Actual_price\": housing_data_df_test['sold_price'].values,\n",
    "    \"Predicted_price\": y_pred,\n",
    "})\n",
    "\n",
    "print(f\"Test RMSE: {rmse:,.0f}\")\n",
    "print(\"\\nSold price predictions:\")\n",
    "print(output_df.to_string(index=False, formatters={\n",
    "    \"Actual_price\": \"{:,.0f}\".format,\n",
    "    \"Predicted_price\": \"{:,.0f}\".format,\n",
    "}))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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