Real Estates.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Business Problem\n",
    "## 1.1 Problem Context\n",
    "\n",
    "Our client is a large Real Estate Investment Trust (REIT).\n",
    "* They invest in houses, apartments, and condos(complex of buildings) within a small county in New York state.\n",
    "* As part of their business, they try to predict the fair transaction price of a property before it's sold.\n",
    "* They do so to calibrate their internal pricing models and keep a pulse on the market.\n",
    "\n",
    "## 1.2 Problem Statement\n",
    "The REIT has hired us to find a data-driven approach to valuing properties.\n",
    "* They currently have an untapped dataset of transaction prices for previous properties on the market.\n",
    "* The data was collected in 2016.\n",
    "* Our task is to build a real-estate pricing model using that dataset.\n",
    "* If we can build a model to predict transaction prices with an average error of under US Dollars 70,000, then our client will be very satisfied with the our resultant model.\n",
    "\n",
    "## 1.3 Business Objectives and Constraints\n",
    "* Deliverable: Trained model file\n",
    "* Win condition: Avg. prediction error < \\$70,000\n",
    "* Model Interpretability will be useful\n",
    "* No latency requirement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Machine Learning Problem\n",
    "## 2.1 Data Overview\n",
    "\n",
    "For this project:\n",
    "1. The dataset has 1883 observations in the county where the REIT operates.\n",
    "2. Each observation is for the transaction of one property only.\n",
    "3. Each transaction was between \\$200,000 and \\$800,000.\n",
    "\n",
    "#### Target Variable\n",
    "* 'tx_price' - Transaction price in USD\n",
    "\n",
    "#### Features of the data:\n",
    "\n",
    "Public records:\n",
    "* 'tx_year' - Year the transaction took place\n",
    "* 'property_tax' - Monthly property tax\n",
    "* 'insurance' - Cost of monthly homeowner's insurance\n",
    "\n",
    "Property characteristics:\n",
    "* 'beds' - Number of bedrooms\n",
    "* 'baths' - Number of bathrooms\n",
    "* 'sqft' - Total floor area in squared feet\n",
    "* 'lot_size' - Total outside area in squared feet\n",
    "* 'year_built' - Year property was built\n",
    "* 'active_life' - Number of gyms, yoga studios, and sports venues within 1 mile\n",
    "* 'basement' - Does the property have a basement?\n",
    "* 'exterior_walls' - The material used for constructing walls of the house\n",
    "* 'roof' - The material used for constructing the roof\n",
    "\n",
    "Location convenience scores:\n",
    "* 'restaurants' - Number of restaurants within 1 mile\n",
    "* 'groceries' - Number of grocery stores within 1 mile\n",
    "* 'nightlife' - Number of nightlife venues within 1 mile\n",
    "* 'cafes' - Number of cafes within 1 mile\n",
    "* 'shopping' - Number of stores within 1 mile\n",
    "* 'arts_entertainment' - Number of arts and entertainment venues within 1 mile\n",
    "* 'beauty_spas' - Number of beauty and spa locations within 1 mile\n",
    "* 'active_life' - Number of gyms, yoga studios, and sports venues within 1 mile\n",
    "\n",
    "Neighborhood demographics:\n",
    "* 'median_age' - Median age of the neighborhood\n",
    "* 'married' - Percent of neighborhood who are married\n",
    "* 'college_grad' - Percent of neighborhood who graduated college\n",
    "\n",
    "Schools:\n",
    "* 'num_schools' - Number of public schools within district\n",
    "* 'median_school' - Median score of the public schools within district, on the range 1 - 10\n",
    "\n",
    "## 2.2 Mapping business problem to ML problem\n",
    "### 2.2.1 Type of Machine Learning Problem\n",
    "It is a regression problem, where given the above set of features, we need to predict the transaction price of the house.\n",
    "\n",
    "### 2.2.2 Performance Metric (KPI)\n",
    "**Since it is a regression problem, we will use the following regression metrics:**\n",
    "#### 2.2.2.1 Root Mean Squared Error (RMSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](Images/rmse.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2.2.2 R-squared\n",
    "![title](Images/r_squared.png)\n",
    "\n",
    "Just remember the value of R suared must be at least greater than 0 and 1 in the best case"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Train-Test Splits\n",
    "**80-20**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\abc\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# NumPy for numerical computing\n",
    "import numpy as np\n",
    "\n",
    "# Pandas for DataFrames\n",
    "import pandas as pd\n",
    "\n",
    "# Matplotlib for visualization\n",
    "from matplotlib import pyplot as plt\n",
    "# display plots in the notebook\n",
    "%matplotlib inline\n",
    "# import color maps\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "# Seaborn for easier visualization\n",
    "import seaborn as sns\n",
    "\n",
    "# Ignore Warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from math import sqrt\n",
    "\n",
    "# Function for splitting training and test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Function to perform data standardization \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Libraries to perform hyperparameter tuning\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Import classes for ML Models\n",
    "from sklearn.linear_model import Ridge  ## Linear Regression + L2 regularization\n",
    "from sklearn.linear_model import Lasso ##Linear Regression + L1 regularizatio\n",
    "from sklearn.svm import SVR ## Support Vector Regressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor \n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "# Evaluation Metrics\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_absolute_error as mae\n",
    "\n",
    "## import xgboost\n",
    "import os\n",
    "mingw_path = 'C:\\\\Program Files\\\\mingw-w64\\\\x86_64-5.3.0-posix-seh-rt_v4-rev0\\\\mingw64\\\\bin'\n",
    "os.environ['PATH'] = mingw_path + ';' + os.environ['PATH']\n",
    "from xgboost import XGBRegressor\n",
    "from xgboost import plot_importance  ## to plot feature importance\n",
    "\n",
    "# To save the final model on disk\n",
    "from sklearn.externals import joblib  ## Reference http://scikit-learn.org/stable/modules/model_persistence.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.set_printoptions(precision=2, suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Load real estate data from CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('Files/real_estate_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Display the dimensions of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1883, 26)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Given features of  dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tx_price', 'beds', 'baths', 'sqft', 'year_built', 'lot_size',\n",
       "       'property_type', 'exterior_walls', 'roof', 'basement', 'restaurants',\n",
       "       'groceries', 'nightlife', 'cafes', 'shopping', 'arts_entertainment',\n",
       "       'beauty_spas', 'active_life', 'median_age', 'married', 'college_grad',\n",
       "       'property_tax', 'insurance', 'median_school', 'num_schools', 'tx_year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Display the first 5 rows for observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>tx_price</th>\n",
       "      <th>beds</th>\n",
       "      <th>baths</th>\n",
       "      <th>sqft</th>\n",
       "      <th>year_built</th>\n",
       "      <th>lot_size</th>\n",
       "      <th>property_type</th>\n",
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       "      <th>nightlife</th>\n",
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       "      <th>beauty_spas</th>\n",
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       "      <th>median_age</th>\n",
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       "      <th>insurance</th>\n",
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       "      <td>Apartment / Condo / Townhouse</td>\n",
       "      <td>Wood Siding</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>81.0</td>\n",
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       "      <td>Apartment / Condo / Townhouse</td>\n",
       "      <td>Brick</td>\n",
       "      <td>Composition Shingle</td>\n",
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       "      <td>105</td>\n",
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       "      <td>2</td>\n",
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       "      <td>14</td>\n",
       "      <td>39.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>279900</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>615</td>\n",
       "      <td>1963</td>\n",
       "      <td>0</td>\n",
       "      <td>Apartment / Condo / Townhouse</td>\n",
       "      <td>Wood Siding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>183</td>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "      <td>101</td>\n",
       "      <td>10</td>\n",
       "      <td>74</td>\n",
       "      <td>62</td>\n",
       "      <td>28.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2012</td>\n",
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       "      <td>1</td>\n",
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       "      <td>Apartment / Condo / Townhouse</td>\n",
       "      <td>Wood Siding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>9</td>\n",
       "      <td>38</td>\n",
       "      <td>25</td>\n",
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       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "      <td>83</td>\n",
       "      <td>36.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2005</td>\n",
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       "      <td>340000</td>\n",
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       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>Apartment / Condo / Townhouse</td>\n",
       "      <td>Brick</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>20.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2002</td>\n",
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      ],
      "text/plain": [
       "   tx_price  beds  baths  sqft  year_built  lot_size  \\\n",
       "0    295850     1      1   584        2013         0   \n",
       "1    216500     1      1   612        1965         0   \n",
       "2    279900     1      1   615        1963         0   \n",
       "3    379900     1      1   618        2000     33541   \n",
       "4    340000     1      1   634        1992         0   \n",
       "\n",
       "                   property_type exterior_walls                 roof  \\\n",
       "0  Apartment / Condo / Townhouse    Wood Siding                  NaN   \n",
       "1  Apartment / Condo / Townhouse          Brick  Composition Shingle   \n",
       "2  Apartment / Condo / Townhouse    Wood Siding                  NaN   \n",
       "3  Apartment / Condo / Townhouse    Wood Siding                  NaN   \n",
       "4  Apartment / Condo / Townhouse          Brick                  NaN   \n",
       "\n",
       "   basement  restaurants  groceries  nightlife  cafes  shopping  \\\n",
       "0       NaN          107          9         30     19        89   \n",
       "1       1.0          105         15          6     13        87   \n",
       "2       NaN          183         13         31     30       101   \n",
       "3       NaN          198          9         38     25       127   \n",
       "4       NaN          149          7         22     20        83   \n",
       "\n",
       "   arts_entertainment  beauty_spas  active_life  median_age  married  \\\n",
       "0                   6           47           58        33.0     65.0   \n",
       "1                   2           26           14        39.0     73.0   \n",
       "2                  10           74           62        28.0     15.0   \n",
       "3                  11           72           83        36.0     25.0   \n",
       "4                  10           50           73        37.0     20.0   \n",
       "\n",
       "   college_grad  property_tax  insurance  median_school  num_schools  tx_year  \n",
       "0          84.0         234.0       81.0            9.0          3.0     2013  \n",
       "1          69.0         169.0       51.0            3.0          3.0     2006  \n",
       "2          86.0         216.0       74.0            8.0          3.0     2012  \n",
       "3          91.0         265.0       92.0            9.0          3.0     2005  \n",
       "4          75.0          88.0       30.0            9.0          3.0     2002  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', 100) ## display max 100 columns\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some feaures are numeric and some are categorical\n",
    "\n",
    "###### Filtering the categorical features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_type     object\n",
       "exterior_walls    object\n",
       "roof              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes[df.dtypes=='object']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Distribution of Numeric Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the most enlightening data exploration tasks is plotting the distributions of your features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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yNhVoJKcDs7o7Fbg2IvbOfdWsDnyPPtS2aU7YZity4dWsBnks4ytIw+EsBe4g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\n2epcDxxFmsf7KOC6QvrxkqaRuk8ucSuUdRpJGwJXA1+MiBekSrMA9q2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N58/t8t81SGN5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OIi/pUaWeO7My6ITXBzMza75KJ681ERGSRrG6ANWepGRLEfFFpbVBx5IKDu0t6bMR8dOCwgV4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\nmVnpOXk1MzMzMzOz0vv/+/jS1ujhOrEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc2c4c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histogram grid\n",
    "df.hist(figsize=(16,16), xrot=-45) ## Display the labels rotated by 45 degress\n",
    "\n",
    "# Clear the text \"residue\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "We can make out quite a few observations:\n",
    "\n",
    "For example, consider the histogram for beds:\n",
    "* Over 600 houses have 4 bedrooms, and similar number of houses have 3 bedrooms.\n",
    "* Less than 100 houses have one bedroom and so on.\n",
    "\n",
    "For example, consider the histogram for sqft:\n",
    "* Over 600 house having sqft less then 2500.\n",
    "* Over 200 having the sqft less 5000 & few no of house having sqft greater then 7500.\n",
    "\n",
    "\n",
    "Also, look at the plot for the 'year_built' feature:\n",
    "* The last bin in the histogram represents the range 2010-2020.\n",
    "* Since this dataset was pulled in 2016, we should not have properties built in 2019.\n",
    "* A property built after 2016 would be a measurement error.\n",
    "* However, because of the 10-year bins, it's hard to tell if there's a measurement error just from the plot above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##### Display summary statistics for numeric features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tx_price</th>\n",
       "      <th>beds</th>\n",
       "      <th>baths</th>\n",
       "      <th>sqft</th>\n",
       "      <th>year_built</th>\n",
       "      <th>lot_size</th>\n",
       "      <th>basement</th>\n",
       "      <th>restaurants</th>\n",
       "      <th>groceries</th>\n",
       "      <th>nightlife</th>\n",
       "      <th>cafes</th>\n",
       "      <th>shopping</th>\n",
       "      <th>arts_entertainment</th>\n",
       "      <th>beauty_spas</th>\n",
       "      <th>active_life</th>\n",
       "      <th>median_age</th>\n",
       "      <th>married</th>\n",
       "      <th>college_grad</th>\n",
       "      <th>property_tax</th>\n",
       "      <th>insurance</th>\n",
       "      <th>median_school</th>\n",
       "      <th>num_schools</th>\n",
       "      <th>tx_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1.883000e+03</td>\n",
       "      <td>1657.0</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>422839.807754</td>\n",
       "      <td>3.420605</td>\n",
       "      <td>2.579926</td>\n",
       "      <td>2329.398832</td>\n",
       "      <td>1982.963887</td>\n",
       "      <td>1.339262e+04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.210303</td>\n",
       "      <td>4.505045</td>\n",
       "      <td>5.074881</td>\n",
       "      <td>5.325013</td>\n",
       "      <td>40.802443</td>\n",
       "      <td>3.394052</td>\n",
       "      <td>23.416888</td>\n",
       "      <td>15.835369</td>\n",
       "      <td>38.601168</td>\n",
       "      <td>69.091875</td>\n",
       "      <td>65.085502</td>\n",
       "      <td>466.777483</td>\n",
       "      <td>140.454063</td>\n",
       "      <td>6.502921</td>\n",
       "      <td>2.793415</td>\n",
       "      <td>2007.111524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>151462.593276</td>\n",
       "      <td>1.068554</td>\n",
       "      <td>0.945576</td>\n",
       "      <td>1336.991858</td>\n",
       "      <td>20.295945</td>\n",
       "      <td>4.494930e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.867012</td>\n",
       "      <td>4.491029</td>\n",
       "      <td>8.464668</td>\n",
       "      <td>7.539309</td>\n",
       "      <td>53.539296</td>\n",
       "      <td>4.699787</td>\n",
       "      <td>25.776916</td>\n",
       "      <td>17.667717</td>\n",
       "      <td>6.634110</td>\n",
       "      <td>19.659767</td>\n",
       "      <td>16.953165</td>\n",
       "      <td>231.656645</td>\n",
       "      <td>72.929765</td>\n",
       "      <td>1.996109</td>\n",
       "      <td>0.505358</td>\n",
       "      <td>5.196898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>200000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1880.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</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>22.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>300000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1345.000000</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>1.542000e+03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>392000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1907.000000</td>\n",
       "      <td>1986.000000</td>\n",
       "      <td>6.098000e+03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>426.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>525000.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3005.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1.176100e+04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>800000.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8450.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.220551e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4508.000000</td>\n",
       "      <td>1374.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            tx_price         beds        baths         sqft   year_built  \\\n",
       "count    1883.000000  1883.000000  1883.000000  1883.000000  1883.000000   \n",
       "mean   422839.807754     3.420605     2.579926  2329.398832  1982.963887   \n",
       "std    151462.593276     1.068554     0.945576  1336.991858    20.295945   \n",
       "min    200000.000000     1.000000     1.000000   500.000000  1880.000000   \n",
       "25%    300000.000000     3.000000     2.000000  1345.000000  1970.000000   \n",
       "50%    392000.000000     3.000000     3.000000  1907.000000  1986.000000   \n",
       "75%    525000.000000     4.000000     3.000000  3005.000000  2000.000000   \n",
       "max    800000.000000     5.000000     6.000000  8450.000000  2015.000000   \n",
       "\n",
       "           lot_size  basement  restaurants    groceries    nightlife  \\\n",
       "count  1.883000e+03    1657.0  1883.000000  1883.000000  1883.000000   \n",
       "mean   1.339262e+04       1.0    40.210303     4.505045     5.074881   \n",
       "std    4.494930e+04       0.0    46.867012     4.491029     8.464668   \n",
       "min    0.000000e+00       1.0     0.000000     0.000000     0.000000   \n",
       "25%    1.542000e+03       1.0     7.000000     1.000000     0.000000   \n",
       "50%    6.098000e+03       1.0    23.000000     3.000000     2.000000   \n",
       "75%    1.176100e+04       1.0    58.000000     7.000000     6.000000   \n",
       "max    1.220551e+06       1.0   266.000000    24.000000    54.000000   \n",
       "\n",
       "             cafes     shopping  arts_entertainment  beauty_spas  active_life  \\\n",
       "count  1883.000000  1883.000000         1883.000000  1883.000000  1883.000000   \n",
       "mean      5.325013    40.802443            3.394052    23.416888    15.835369   \n",
       "std       7.539309    53.539296            4.699787    25.776916    17.667717   \n",
       "min       0.000000     0.000000            0.000000     0.000000     0.000000   \n",
       "25%       0.000000     6.000000            0.000000     4.000000     4.000000   \n",
       "50%       3.000000    22.000000            2.000000    15.000000    10.000000   \n",
       "75%       6.000000    51.000000            5.000000    35.000000    21.000000   \n",
       "max      47.000000   340.000000           35.000000   177.000000    94.000000   \n",
       "\n",
       "        median_age      married  college_grad  property_tax    insurance  \\\n",
       "count  1883.000000  1883.000000   1883.000000   1883.000000  1883.000000   \n",
       "mean     38.601168    69.091875     65.085502    466.777483   140.454063   \n",
       "std       6.634110    19.659767     16.953165    231.656645    72.929765   \n",
       "min      22.000000    11.000000      5.000000     88.000000    30.000000   \n",
       "25%      33.000000    58.000000     54.000000    320.000000    94.000000   \n",
       "50%      38.000000    73.000000     66.000000    426.000000   125.000000   \n",
       "75%      43.000000    84.000000     78.000000    569.000000   169.000000   \n",
       "max      69.000000   100.000000    100.000000   4508.000000  1374.000000   \n",
       "\n",
       "       median_school  num_schools      tx_year  \n",
       "count    1883.000000  1883.000000  1883.000000  \n",
       "mean        6.502921     2.793415  2007.111524  \n",
       "std         1.996109     0.505358     5.196898  \n",
       "min         1.000000     1.000000  1993.000000  \n",
       "25%         5.000000     3.000000  2004.000000  \n",
       "50%         7.000000     3.000000  2007.000000  \n",
       "75%         8.000000     3.000000  2011.000000  \n",
       "max        10.000000     4.000000  2016.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Obeservation:**\n",
    "* Look at the 'year_built' column, we can see that its max value is 2015.\n",
    "* The 'basement' feature has some missing values, also its standard deviation is 0.0, while its min and max are both 1.0. Maybe this is a feature that should be binary?\n",
    "* Just note the mean values of all the parameters and get a sense whether the values look reasonable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Distributions of categorical features\n",
    "Display summary statistics for categorical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_type</th>\n",
       "      <th>exterior_walls</th>\n",
       "      <th>roof</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1883</td>\n",
       "      <td>1660</td>\n",
       "      <td>1529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Single-Family</td>\n",
       "      <td>Brick</td>\n",
       "      <td>Composition Shingle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1080</td>\n",
       "      <td>687</td>\n",
       "      <td>1179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        property_type exterior_walls                 roof\n",
       "count            1883           1660                 1529\n",
       "unique              2             16                   16\n",
       "top     Single-Family          Brick  Composition Shingle\n",
       "freq             1080            687                 1179"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=['object'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Obeservation:**\n",
    "* exterior_walls and roof having some of the missing values.\n",
    "* There are 16 unique classes for 'exterior_walls', and the most frequent one is 'Brick'.\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Plot bar plot for the 'exterior_walls' feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e82748>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/DeG8mcCgoVaSJGmyGs4rvS5LssuoVTJ5/Bh4cbP9WuC0ngNJ1kvytSRXJLkq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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcc08dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.countplot(y='exterior_walls',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "Take a look at the frequencies of the classes.\n",
    "\n",
    "Several classes are quite prevalent in the dataset. They have long bars. Those include:\n",
    "* 'Brick'\n",
    "* 'Siding (Alum/Vinyl)'\n",
    "* 'Metal'\n",
    "* 'Combination'\n",
    "\n",
    "On the flipside, some classes have really short bars. For example:\n",
    "* 'Concrete Block'\n",
    "* 'Concrete'\n",
    "* 'Block'\n",
    "* 'Wood Shingle'\n",
    "* etc...\n",
    "* These don't have many observations, and they are called sparse classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 Sparse Classes\n",
    "Sparse classes are classes in categorical features that have a very small number of observations.\n",
    "\n",
    "They tend to be problematic when we get to building models.\n",
    "* In the best case, they don't influence the model much.\n",
    "* In the worst case, they can cause the model to be overfit.\n",
    "\n",
    "Let's make a mental note to combine or reassign some of these classes later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bar plot for each categorical feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd6ae128>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MKhssaUJ6VvheZG/LKrkz/ZwOzIyIuakF/wLQp5l9kGLeX9KywNHA6GqVJB1bGrH/xrtL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcc081d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,2))\n",
    "sns.countplot(y='property_type',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd6b1860>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PmAp8sNtH9bq/2h4P/Bk4g1pQbw8cVddnL+CKsnwetVkqkrYH/mb73ibjjgX+y/amwNPA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\n1ciF1czMrEb/AR/Q+7eeFRRLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd6b1240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,8))\n",
    "sns.countplot(y='exterior_walls', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xce7a588>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S8vjHZR6Ae4Dxkj5exuyVUtMQ23eWpouAPStdri4/JzfV9me2x9nutN259qBVejt1RET0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\nMzOrACdkMzOzCvg/GJKTgEUpBUYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc27ba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,8))\n",
    "sns.countplot(y='roof', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:** In both 'exterior_walls' and 'roof' as well we have many sparse classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.5 Segmentations\n",
    "Segmentations are powerful ways to cut the data to observe the relationship between categorical features and numeric features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Segmenting the target variable by key categorical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xade5bf0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m8TDgCuA5NB96M7ctPxq4oD2+ADi6PZ7b1gvwduDtPX1d0Lbb3rYtf3t7yURj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xae76590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='property_type',x='tx_price',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:** In general, it looks like single family homes are more expensive then the apartment/condo/Townhouse."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the two property types across other features as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tx_price</th>\n",
       "      <th>beds</th>\n",
       "      <th>baths</th>\n",
       "      <th>sqft</th>\n",
       "      <th>year_built</th>\n",
       "      <th>lot_size</th>\n",
       "      <th>basement</th>\n",
       "      <th>restaurants</th>\n",
       "      <th>groceries</th>\n",
       "      <th>nightlife</th>\n",
       "      <th>cafes</th>\n",
       "      <th>shopping</th>\n",
       "      <th>arts_entertainment</th>\n",
       "      <th>beauty_spas</th>\n",
       "      <th>active_life</th>\n",
       "      <th>median_age</th>\n",
       "      <th>married</th>\n",
       "      <th>college_grad</th>\n",
       "      <th>property_tax</th>\n",
       "      <th>insurance</th>\n",
       "      <th>median_school</th>\n",
       "      <th>num_schools</th>\n",
       "      <th>tx_year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>property_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apartment / Condo / Townhouse</th>\n",
       "      <td>366614.034869</td>\n",
       "      <td>2.601494</td>\n",
       "      <td>2.200498</td>\n",
       "      <td>1513.727273</td>\n",
       "      <td>1988.936488</td>\n",
       "      <td>3944.239103</td>\n",
       "      <td>1.0</td>\n",
       "      <td>58.418431</td>\n",
       "      <td>5.919054</td>\n",
       "      <td>7.855542</td>\n",
       "      <td>8.037360</td>\n",
       "      <td>57.631382</td>\n",
       "      <td>4.840598</td>\n",
       "      <td>32.087173</td>\n",
       "      <td>22.410959</td>\n",
       "      <td>37.199253</td>\n",
       "      <td>57.534247</td>\n",
       "      <td>66.372354</td>\n",
       "      <td>346.261519</td>\n",
       "      <td>105.652553</td>\n",
       "      <td>6.382316</td>\n",
       "      <td>2.831880</td>\n",
       "      <td>2007.941469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single-Family</th>\n",
       "      <td>464644.711111</td>\n",
       "      <td>4.029630</td>\n",
       "      <td>2.862037</td>\n",
       "      <td>2935.865741</td>\n",
       "      <td>1978.523148</td>\n",
       "      <td>20417.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.672222</td>\n",
       "      <td>3.453704</td>\n",
       "      <td>3.007407</td>\n",
       "      <td>3.308333</td>\n",
       "      <td>28.289815</td>\n",
       "      <td>2.318519</td>\n",
       "      <td>16.970370</td>\n",
       "      <td>10.946296</td>\n",
       "      <td>39.643519</td>\n",
       "      <td>77.685185</td>\n",
       "      <td>64.128704</td>\n",
       "      <td>556.383333</td>\n",
       "      <td>166.329630</td>\n",
       "      <td>6.592593</td>\n",
       "      <td>2.764815</td>\n",
       "      <td>2006.494444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    tx_price      beds     baths         sqft  \\\n",
       "property_type                                                                   \n",
       "Apartment / Condo / Townhouse  366614.034869  2.601494  2.200498  1513.727273   \n",
       "Single-Family                  464644.711111  4.029630  2.862037  2935.865741   \n",
       "\n",
       "                                year_built      lot_size  basement  \\\n",
       "property_type                                                        \n",
       "Apartment / Condo / Townhouse  1988.936488   3944.239103       1.0   \n",
       "Single-Family                  1978.523148  20417.666667       1.0   \n",
       "\n",
       "                               restaurants  groceries  nightlife     cafes  \\\n",
       "property_type                                                                \n",
       "Apartment / Condo / Townhouse    58.418431   5.919054   7.855542  8.037360   \n",
       "Single-Family                    26.672222   3.453704   3.007407  3.308333   \n",
       "\n",
       "                                shopping  arts_entertainment  beauty_spas  \\\n",
       "property_type                                                               \n",
       "Apartment / Condo / Townhouse  57.631382            4.840598    32.087173   \n",
       "Single-Family                  28.289815            2.318519    16.970370   \n",
       "\n",
       "                               active_life  median_age    married  \\\n",
       "property_type                                                       \n",
       "Apartment / Condo / Townhouse    22.410959   37.199253  57.534247   \n",
       "Single-Family                    10.946296   39.643519  77.685185   \n",
       "\n",
       "                               college_grad  property_tax   insurance  \\\n",
       "property_type                                                           \n",
       "Apartment / Condo / Townhouse     66.372354    346.261519  105.652553   \n",
       "Single-Family                     64.128704    556.383333  166.329630   \n",
       "\n",
       "                               median_school  num_schools      tx_year  \n",
       "property_type                                                           \n",
       "Apartment / Condo / Townhouse       6.382316     2.831880  2007.941469  \n",
       "Single-Family                       6.592593     2.764815  2006.494444  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('property_type').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some questions to consider include:\n",
    "* On average, which type of property is larger?\n",
    "* Which type of property is has larger lots?\n",
    "* Which type of property is in areas with more nightlife options/more restaurants/more grocery stores?\n",
    "* Do these relationships make intuitive sense, or are any surprising to you?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Segment 'sqft' by 'property_type' and plot the boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb517290>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nV4G3l+3nAaeVy6cD55XLbwcuLJdfANwI7AYcDPwX0LOdtb0P+DLwrfJ622sq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\nklSZ/w9f1tNtKp11vwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xad80d70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='property_type',x='sqft',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After producing the plot, consider these questions:\n",
    "* Which type of property is larger, on average?\n",
    "* Which type of property sees greater variance in sizes?\n",
    "* Does the difference in distributions between classes make intuitive sense?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Segment by property_type and display the means and standard deviations within each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">tx_price</th>\n",
       "      <th colspan=\"2\" halign=\"left\">beds</th>\n",
       "      <th colspan=\"2\" halign=\"left\">baths</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sqft</th>\n",
       "      <th colspan=\"2\" halign=\"left\">year_built</th>\n",
       "      <th colspan=\"2\" halign=\"left\">lot_size</th>\n",
       "      <th colspan=\"2\" halign=\"left\">basement</th>\n",
       "      <th colspan=\"2\" halign=\"left\">restaurants</th>\n",
       "      <th colspan=\"2\" halign=\"left\">groceries</th>\n",
       "      <th colspan=\"2\" halign=\"left\">nightlife</th>\n",
       "      <th colspan=\"2\" halign=\"left\">cafes</th>\n",
       "      <th colspan=\"2\" halign=\"left\">shopping</th>\n",
       "      <th colspan=\"2\" halign=\"left\">arts_entertainment</th>\n",
       "      <th colspan=\"2\" halign=\"left\">beauty_spas</th>\n",
       "      <th colspan=\"2\" halign=\"left\">active_life</th>\n",
       "      <th colspan=\"2\" halign=\"left\">median_age</th>\n",
       "      <th colspan=\"2\" halign=\"left\">married</th>\n",
       "      <th colspan=\"2\" halign=\"left\">college_grad</th>\n",
       "      <th colspan=\"2\" halign=\"left\">property_tax</th>\n",
       "      <th colspan=\"2\" halign=\"left\">insurance</th>\n",
       "      <th colspan=\"2\" halign=\"left\">median_school</th>\n",
       "      <th colspan=\"2\" halign=\"left\">num_schools</th>\n",
       "      <th colspan=\"2\" halign=\"left\">tx_year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>property_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apartment / Condo / Townhouse</th>\n",
       "      <td>366614.034869</td>\n",
       "      <td>121784.490486</td>\n",
       "      <td>2.601494</td>\n",
       "      <td>0.810220</td>\n",
       "      <td>2.200498</td>\n",
       "      <td>0.815009</td>\n",
       "      <td>1513.727273</td>\n",
       "      <td>556.286650</td>\n",
       "      <td>1988.936488</td>\n",
       "      <td>15.513640</td>\n",
       "      <td>3944.239103</td>\n",
       "      <td>44284.168767</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.418431</td>\n",
       "      <td>54.343594</td>\n",
       "      <td>5.919054</td>\n",
       "      <td>4.645774</td>\n",
       "      <td>7.855542</td>\n",
       "      <td>10.643816</td>\n",
       "      <td>8.037360</td>\n",
       "      <td>9.077038</td>\n",
       "      <td>57.631382</td>\n",
       "      <td>61.852299</td>\n",
       "      <td>4.840598</td>\n",
       "      <td>5.234834</td>\n",
       "      <td>32.087173</td>\n",
       "      <td>26.910443</td>\n",
       "      <td>22.410959</td>\n",
       "      <td>21.058178</td>\n",
       "      <td>37.199253</td>\n",
       "      <td>6.906584</td>\n",
       "      <td>57.534247</td>\n",
       "      <td>20.372706</td>\n",
       "      <td>66.372354</td>\n",
       "      <td>17.095874</td>\n",
       "      <td>346.261519</td>\n",
       "      <td>142.292282</td>\n",
       "      <td>105.652553</td>\n",
       "      <td>47.118015</td>\n",
       "      <td>6.382316</td>\n",
       "      <td>1.941998</td>\n",
       "      <td>2.831880</td>\n",
       "      <td>0.455370</td>\n",
       "      <td>2007.941469</td>\n",
       "      <td>4.099487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single-Family</th>\n",
       "      <td>464644.711111</td>\n",
       "      <td>157758.739013</td>\n",
       "      <td>4.029630</td>\n",
       "      <td>0.795639</td>\n",
       "      <td>2.862037</td>\n",
       "      <td>0.937551</td>\n",
       "      <td>2935.865741</td>\n",
       "      <td>1422.871169</td>\n",
       "      <td>1978.523148</td>\n",
       "      <td>22.210582</td>\n",
       "      <td>20417.666667</td>\n",
       "      <td>44165.529302</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.672222</td>\n",
       "      <td>34.726416</td>\n",
       "      <td>3.453704</td>\n",
       "      <td>4.067285</td>\n",
       "      <td>3.007407</td>\n",
       "      <td>5.543822</td>\n",
       "      <td>3.308333</td>\n",
       "      <td>5.325053</td>\n",
       "      <td>28.289815</td>\n",
       "      <td>42.292313</td>\n",
       "      <td>2.318519</td>\n",
       "      <td>3.929691</td>\n",
       "      <td>16.970370</td>\n",
       "      <td>22.872112</td>\n",
       "      <td>10.946296</td>\n",
       "      <td>12.599296</td>\n",
       "      <td>39.643519</td>\n",
       "      <td>6.225732</td>\n",
       "      <td>77.685185</td>\n",
       "      <td>13.868205</td>\n",
       "      <td>64.128704</td>\n",
       "      <td>16.790347</td>\n",
       "      <td>556.383333</td>\n",
       "      <td>244.351559</td>\n",
       "      <td>166.329630</td>\n",
       "      <td>77.816022</td>\n",
       "      <td>6.592593</td>\n",
       "      <td>2.031663</td>\n",
       "      <td>2.764815</td>\n",
       "      <td>0.537959</td>\n",
       "      <td>2006.494444</td>\n",
       "      <td>5.807059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    tx_price                     beds  \\\n",
       "                                        mean            std      mean   \n",
       "property_type                                                           \n",
       "Apartment / Condo / Townhouse  366614.034869  121784.490486  2.601494   \n",
       "Single-Family                  464644.711111  157758.739013  4.029630   \n",
       "\n",
       "                                            baths                   sqft  \\\n",
       "                                    std      mean       std         mean   \n",
       "property_type                                                              \n",
       "Apartment / Condo / Townhouse  0.810220  2.200498  0.815009  1513.727273   \n",
       "Single-Family                  0.795639  2.862037  0.937551  2935.865741   \n",
       "\n",
       "                                             year_built             \\\n",
       "                                       std         mean        std   \n",
       "property_type                                                        \n",
       "Apartment / Condo / Townhouse   556.286650  1988.936488  15.513640   \n",
       "Single-Family                  1422.871169  1978.523148  22.210582   \n",
       "\n",
       "                                   lot_size               basement       \\\n",
       "                                       mean           std     mean  std   \n",
       "property_type                                                             \n",
       "Apartment / Condo / Townhouse   3944.239103  44284.168767      1.0  0.0   \n",
       "Single-Family                  20417.666667  44165.529302      1.0  0.0   \n",
       "\n",
       "                              restaurants            groceries            \\\n",
       "                                     mean        std      mean       std   \n",
       "property_type                                                              \n",
       "Apartment / Condo / Townhouse   58.418431  54.343594  5.919054  4.645774   \n",
       "Single-Family                   26.672222  34.726416  3.453704  4.067285   \n",
       "\n",
       "                              nightlife                cafes            \\\n",
       "                                   mean        std      mean       std   \n",
       "property_type                                                            \n",
       "Apartment / Condo / Townhouse  7.855542  10.643816  8.037360  9.077038   \n",
       "Single-Family                  3.007407   5.543822  3.308333  5.325053   \n",
       "\n",
       "                                shopping            arts_entertainment  \\\n",
       "                                    mean        std               mean   \n",
       "property_type                                                            \n",
       "Apartment / Condo / Townhouse  57.631382  61.852299           4.840598   \n",
       "Single-Family                  28.289815  42.292313           2.318519   \n",
       "\n",
       "                                        beauty_spas            active_life  \\\n",
       "                                    std        mean        std        mean   \n",
       "property_type                                                                \n",
       "Apartment / Condo / Townhouse  5.234834   32.087173  26.910443   22.410959   \n",
       "Single-Family                  3.929691   16.970370  22.872112   10.946296   \n",
       "\n",
       "                                         median_age              married  \\\n",
       "                                     std       mean       std       mean   \n",
       "property_type                                                              \n",
       "Apartment / Condo / Townhouse  21.058178  37.199253  6.906584  57.534247   \n",
       "Single-Family                  12.599296  39.643519  6.225732  77.685185   \n",
       "\n",
       "                                         college_grad            property_tax  \\\n",
       "                                     std         mean        std         mean   \n",
       "property_type                                                                   \n",
       "Apartment / Condo / Townhouse  20.372706    66.372354  17.095874   346.261519   \n",
       "Single-Family                  13.868205    64.128704  16.790347   556.383333   \n",
       "\n",
       "                                            insurance             \\\n",
       "                                      std        mean        std   \n",
       "property_type                                                      \n",
       "Apartment / Condo / Townhouse  142.292282  105.652553  47.118015   \n",
       "Single-Family                  244.351559  166.329630  77.816022   \n",
       "\n",
       "                              median_school           num_schools            \\\n",
       "                                       mean       std        mean       std   \n",
       "property_type                                                                 \n",
       "Apartment / Condo / Townhouse      6.382316  1.941998    2.831880  0.455370   \n",
       "Single-Family                      6.592593  2.031663    2.764815  0.537959   \n",
       "\n",
       "                                   tx_year            \n",
       "                                      mean       std  \n",
       "property_type                                         \n",
       "Apartment / Condo / Townhouse  2007.941469  4.099487  \n",
       "Single-Family                  2006.494444  5.807059  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('property_type').agg([np.mean,np.std])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.6 Correlations\n",
    "* Finally, let's take a look at the relationships between numeric features and other numeric features.\n",
    "* ***Correlation*** is a value between -1 and 1 that represents how closely values for two separate features move in unison.\n",
    "* Positive correlation means that as one feature increases, the other increases; eg. a child's age and her height.\n",
    "* Negative correlation means that as one feature increases, the other decreases; eg. hours spent studying and number of parties attended.\n",
    "* Correlations near -1 or 1 indicate a strong relationship.\n",
    "* Those closer to 0 indicate a weak relationship.\n",
    "* 0 indicates no relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tx_price</th>\n",
       "      <th>beds</th>\n",
       "      <th>baths</th>\n",
       "      <th>sqft</th>\n",
       "      <th>year_built</th>\n",
       "      <th>lot_size</th>\n",
       "      <th>basement</th>\n",
       "      <th>restaurants</th>\n",
       "      <th>groceries</th>\n",
       "      <th>nightlife</th>\n",
       "      <th>cafes</th>\n",
       "      <th>shopping</th>\n",
       "      <th>arts_entertainment</th>\n",
       "      <th>beauty_spas</th>\n",
       "      <th>active_life</th>\n",
       "      <th>median_age</th>\n",
       "      <th>married</th>\n",
       "      <th>college_grad</th>\n",
       "      <th>property_tax</th>\n",
       "      <th>insurance</th>\n",
       "      <th>median_school</th>\n",
       "      <th>num_schools</th>\n",
       "      <th>tx_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tx_price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.384046</td>\n",
       "      <td>0.389375</td>\n",
       "      <td>0.469573</td>\n",
       "      <td>0.033138</td>\n",
       "      <td>0.130558</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.038027</td>\n",
       "      <td>-0.094314</td>\n",
       "      <td>0.009361</td>\n",
       "      <td>-0.001398</td>\n",
       "      <td>-0.038246</td>\n",
       "      <td>-0.021076</td>\n",
       "      <td>-0.054349</td>\n",
       "      <td>-0.001165</td>\n",
       "      <td>0.126335</td>\n",
       "      <td>0.200494</td>\n",
       "      <td>0.268577</td>\n",
       "      <td>0.535148</td>\n",
       "      <td>0.532947</td>\n",
       "      <td>0.175762</td>\n",
       "      <td>-0.014380</td>\n",
       "      <td>0.108782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beds</th>\n",
       "      <td>0.384046</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.644572</td>\n",
       "      <td>0.691039</td>\n",
       "      <td>-0.011844</td>\n",
       "      <td>0.164399</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.495834</td>\n",
       "      <td>-0.421412</td>\n",
       "      <td>-0.440844</td>\n",
       "      <td>-0.464289</td>\n",
       "      <td>-0.388670</td>\n",
       "      <td>-0.442168</td>\n",
       "      <td>-0.419832</td>\n",
       "      <td>-0.486957</td>\n",
       "      <td>0.133711</td>\n",
       "      <td>0.643240</td>\n",
       "      <td>-0.082354</td>\n",
       "      <td>0.547643</td>\n",
       "      <td>0.485776</td>\n",
       "      <td>0.137309</td>\n",
       "      <td>-0.124359</td>\n",
       "      <td>-0.174081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baths</th>\n",
       "      <td>0.389375</td>\n",
       "      <td>0.644572</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.682060</td>\n",
       "      <td>0.206141</td>\n",
       "      <td>0.132976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.350210</td>\n",
       "      <td>-0.340024</td>\n",
       "      <td>-0.306686</td>\n",
       "      <td>-0.316836</td>\n",
       "      <td>-0.259603</td>\n",
       "      <td>-0.305885</td>\n",
       "      <td>-0.282008</td>\n",
       "      <td>-0.329736</td>\n",
       "      <td>0.095844</td>\n",
       "      <td>0.442225</td>\n",
       "      <td>0.016097</td>\n",
       "      <td>0.525776</td>\n",
       "      <td>0.475430</td>\n",
       "      <td>0.163365</td>\n",
       "      <td>-0.088299</td>\n",
       "      <td>-0.132110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft</th>\n",
       "      <td>0.469573</td>\n",
       "      <td>0.691039</td>\n",
       "      <td>0.682060</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.254589</td>\n",
       "      <td>0.246016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.353759</td>\n",
       "      <td>-0.371167</td>\n",
       "      <td>-0.281540</td>\n",
       "      <td>-0.300969</td>\n",
       "      <td>-0.275586</td>\n",
       "      <td>-0.293402</td>\n",
       "      <td>-0.310465</td>\n",
       "      <td>-0.332663</td>\n",
       "      <td>0.109811</td>\n",
       "      <td>0.480167</td>\n",
       "      <td>0.065343</td>\n",
       "      <td>0.660264</td>\n",
       "      <td>0.594049</td>\n",
       "      <td>0.220669</td>\n",
       "      <td>-0.102032</td>\n",
       "      <td>-0.236190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_built</th>\n",
       "      <td>0.033138</td>\n",
       "      <td>-0.011844</td>\n",
       "      <td>0.206141</td>\n",
       "      <td>0.254589</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.106948</td>\n",
       "      <td>-0.222443</td>\n",
       "      <td>-0.080006</td>\n",
       "      <td>-0.102209</td>\n",
       "      <td>-0.121684</td>\n",
       "      <td>-0.195141</td>\n",
       "      <td>-0.163670</td>\n",
       "      <td>-0.134919</td>\n",
       "      <td>-0.237152</td>\n",
       "      <td>0.038208</td>\n",
       "      <td>-0.014204</td>\n",
       "      <td>-0.046504</td>\n",
       "      <td>-0.109521</td>\n",
       "      <td>0.155835</td>\n",
       "      <td>-0.188106</td>\n",
       "      <td>-0.043301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lot_size</th>\n",
       "      <td>0.130558</td>\n",
       "      <td>0.164399</td>\n",
       "      <td>0.132976</td>\n",
       "      <td>0.246016</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.113887</td>\n",
       "      <td>-0.118574</td>\n",
       "      <td>-0.072224</td>\n",
       "      <td>-0.098182</td>\n",
       "      <td>-0.116560</td>\n",
       "      <td>-0.067189</td>\n",
       "      <td>-0.121075</td>\n",
       "      <td>-0.092668</td>\n",
       "      <td>0.099140</td>\n",
       "      <td>0.122028</td>\n",
       "      <td>-0.030725</td>\n",
       "      <td>0.165800</td>\n",
       "      <td>0.146579</td>\n",
       "      <td>0.096551</td>\n",
       "      <td>-0.031535</td>\n",
       "      <td>-0.048207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basement</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restaurants</th>\n",
       "      <td>-0.038027</td>\n",
       "      <td>-0.495834</td>\n",
       "      <td>-0.350210</td>\n",
       "      <td>-0.353759</td>\n",
       "      <td>-0.106948</td>\n",
       "      <td>-0.113887</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.701462</td>\n",
       "      <td>0.926675</td>\n",
       "      <td>0.937616</td>\n",
       "      <td>0.818815</td>\n",
       "      <td>0.726907</td>\n",
       "      <td>0.867829</td>\n",
       "      <td>0.881799</td>\n",
       "      <td>-0.063816</td>\n",
       "      <td>-0.615584</td>\n",
       "      <td>0.214260</td>\n",
       "      <td>-0.100821</td>\n",
       "      <td>-0.019925</td>\n",
       "      <td>-0.068425</td>\n",
       "      <td>0.251171</td>\n",
       "      <td>0.044739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>groceries</th>\n",
       "      <td>-0.094314</td>\n",
       "      <td>-0.421412</td>\n",
       "      <td>-0.340024</td>\n",
       "      <td>-0.371167</td>\n",
       "      <td>-0.222443</td>\n",
       "      <td>-0.118574</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.701462</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.565003</td>\n",
       "      <td>0.602857</td>\n",
       "      <td>0.526356</td>\n",
       "      <td>0.502837</td>\n",
       "      <td>0.626850</td>\n",
       "      <td>0.574532</td>\n",
       "      <td>-0.022377</td>\n",
       "      <td>-0.508793</td>\n",
       "      <td>-0.006025</td>\n",
       "      <td>-0.159506</td>\n",
       "      <td>-0.093974</td>\n",
       "      <td>-0.354553</td>\n",
       "      <td>0.272856</td>\n",
       "      <td>0.115674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nightlife</th>\n",
       "      <td>0.009361</td>\n",
       "      <td>-0.440844</td>\n",
       "      <td>-0.306686</td>\n",
       "      <td>-0.281540</td>\n",
       "      <td>-0.080006</td>\n",
       "      <td>-0.072224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.926675</td>\n",
       "      <td>0.565003</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.923901</td>\n",
       "      <td>0.743321</td>\n",
       "      <td>0.703038</td>\n",
       "      <td>0.777461</td>\n",
       "      <td>0.858836</td>\n",
       "      <td>-0.028611</td>\n",
       "      <td>-0.568811</td>\n",
       "      <td>0.253717</td>\n",
       "      <td>-0.054386</td>\n",
       "      <td>0.024551</td>\n",
       "      <td>-0.036948</td>\n",
       "      <td>0.196399</td>\n",
       "      <td>0.027495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cafes</th>\n",
       "      <td>-0.001398</td>\n",
       "      <td>-0.464289</td>\n",
       "      <td>-0.316836</td>\n",
       "      <td>-0.300969</td>\n",
       "      <td>-0.102209</td>\n",
       "      <td>-0.098182</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.937616</td>\n",
       "      <td>0.602857</td>\n",
       "      <td>0.923901</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.837058</td>\n",
       "      <td>0.694575</td>\n",
       "      <td>0.822541</td>\n",
       "      <td>0.826648</td>\n",
       "      <td>0.026400</td>\n",
       "      <td>-0.581850</td>\n",
       "      <td>0.266652</td>\n",
       "      <td>-0.044410</td>\n",
       "      <td>0.036502</td>\n",
       "      <td>-0.027868</td>\n",
       "      <td>0.224312</td>\n",
       "      <td>0.013558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shopping</th>\n",
       "      <td>-0.038246</td>\n",
       "      <td>-0.388670</td>\n",
       "      <td>-0.259603</td>\n",
       "      <td>-0.275586</td>\n",
       "      <td>-0.121684</td>\n",
       "      <td>-0.116560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.818815</td>\n",
       "      <td>0.526356</td>\n",
       "      <td>0.743321</td>\n",
       "      <td>0.837058</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.653803</td>\n",
       "      <td>0.860987</td>\n",
       "      <td>0.678515</td>\n",
       "      <td>0.032339</td>\n",
       "      <td>-0.454072</td>\n",
       "      <td>0.177383</td>\n",
       "      <td>-0.040556</td>\n",
       "      <td>0.018846</td>\n",
       "      <td>-0.048058</td>\n",
       "      <td>0.228988</td>\n",
       "      <td>-0.000324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arts_entertainment</th>\n",
       "      <td>-0.021076</td>\n",
       "      <td>-0.442168</td>\n",
       "      <td>-0.305885</td>\n",
       "      <td>-0.293402</td>\n",
       "      <td>-0.195141</td>\n",
       "      <td>-0.067189</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.726907</td>\n",
       "      <td>0.502837</td>\n",
       "      <td>0.703038</td>\n",
       "      <td>0.694575</td>\n",
       "      <td>0.653803</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.757860</td>\n",
       "      <td>0.800994</td>\n",
       "      <td>-0.014640</td>\n",
       "      <td>-0.554533</td>\n",
       "      <td>0.239129</td>\n",
       "      <td>-0.066346</td>\n",
       "      <td>0.015380</td>\n",
       "      <td>-0.138266</td>\n",
       "      <td>0.214163</td>\n",
       "      <td>0.034356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beauty_spas</th>\n",
       "      <td>-0.054349</td>\n",
       "      <td>-0.419832</td>\n",
       "      <td>-0.282008</td>\n",
       "      <td>-0.310465</td>\n",
       "      <td>-0.163670</td>\n",
       "      <td>-0.121075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.867829</td>\n",
       "      <td>0.626850</td>\n",
       "      <td>0.777461</td>\n",
       "      <td>0.822541</td>\n",
       "      <td>0.860987</td>\n",
       "      <td>0.757860</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.785920</td>\n",
       "      <td>-0.002961</td>\n",
       "      <td>-0.510331</td>\n",
       "      <td>0.177066</td>\n",
       "      <td>-0.057293</td>\n",
       "      <td>0.010980</td>\n",
       "      <td>-0.098727</td>\n",
       "      <td>0.262651</td>\n",
       "      <td>0.008066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>active_life</th>\n",
       "      <td>-0.001165</td>\n",
       "      <td>-0.486957</td>\n",
       "      <td>-0.329736</td>\n",
       "      <td>-0.332663</td>\n",
       "      <td>-0.134919</td>\n",
       "      <td>-0.092668</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.881799</td>\n",
       "      <td>0.574532</td>\n",
       "      <td>0.858836</td>\n",
       "      <td>0.826648</td>\n",
       "      <td>0.678515</td>\n",
       "      <td>0.800994</td>\n",
       "      <td>0.785920</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.081159</td>\n",
       "      <td>-0.632052</td>\n",
       "      <td>0.323985</td>\n",
       "      <td>-0.057646</td>\n",
       "      <td>0.030911</td>\n",
       "      <td>0.007675</td>\n",
       "      <td>0.235246</td>\n",
       "      <td>0.028759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_age</th>\n",
       "      <td>0.126335</td>\n",
       "      <td>0.133711</td>\n",
       "      <td>0.095844</td>\n",
       "      <td>0.109811</td>\n",
       "      <td>-0.237152</td>\n",
       "      <td>0.099140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.063816</td>\n",
       "      <td>-0.022377</td>\n",
       "      <td>-0.028611</td>\n",
       "      <td>0.026400</td>\n",
       "      <td>0.032339</td>\n",
       "      <td>-0.014640</td>\n",
       "      <td>-0.002961</td>\n",
       "      <td>-0.081159</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.045555</td>\n",
       "      <td>0.169749</td>\n",
       "      <td>0.215435</td>\n",
       "      <td>0.232503</td>\n",
       "      <td>-0.002861</td>\n",
       "      <td>0.071455</td>\n",
       "      <td>-0.118490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>0.200494</td>\n",
       "      <td>0.643240</td>\n",
       "      <td>0.442225</td>\n",
       "      <td>0.480167</td>\n",
       "      <td>0.038208</td>\n",
       "      <td>0.122028</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.615584</td>\n",
       "      <td>-0.508793</td>\n",
       "      <td>-0.568811</td>\n",
       "      <td>-0.581850</td>\n",
       "      <td>-0.454072</td>\n",
       "      <td>-0.554533</td>\n",
       "      <td>-0.510331</td>\n",
       "      <td>-0.632052</td>\n",
       "      <td>0.045555</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.143461</td>\n",
       "      <td>0.305330</td>\n",
       "      <td>0.233701</td>\n",
       "      <td>0.169439</td>\n",
       "      <td>-0.212870</td>\n",
       "      <td>-0.134787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>college_grad</th>\n",
       "      <td>0.268577</td>\n",
       "      <td>-0.082354</td>\n",
       "      <td>0.016097</td>\n",
       "      <td>0.065343</td>\n",
       "      <td>-0.014204</td>\n",
       "      <td>-0.030725</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.214260</td>\n",
       "      <td>-0.006025</td>\n",
       "      <td>0.253717</td>\n",
       "      <td>0.266652</td>\n",
       "      <td>0.177383</td>\n",
       "      <td>0.239129</td>\n",
       "      <td>0.177066</td>\n",
       "      <td>0.323985</td>\n",
       "      <td>0.169749</td>\n",
       "      <td>-0.143461</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.287933</td>\n",
       "      <td>0.326057</td>\n",
       "      <td>0.400306</td>\n",
       "      <td>0.049756</td>\n",
       "      <td>-0.189932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>property_tax</th>\n",
       "      <td>0.535148</td>\n",
       "      <td>0.547643</td>\n",
       "      <td>0.525776</td>\n",
       "      <td>0.660264</td>\n",
       "      <td>-0.046504</td>\n",
       "      <td>0.165800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.100821</td>\n",
       "      <td>-0.159506</td>\n",
       "      <td>-0.054386</td>\n",
       "      <td>-0.044410</td>\n",
       "      <td>-0.040556</td>\n",
       "      <td>-0.066346</td>\n",
       "      <td>-0.057293</td>\n",
       "      <td>-0.057646</td>\n",
       "      <td>0.215435</td>\n",
       "      <td>0.305330</td>\n",
       "      <td>0.287933</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.984724</td>\n",
       "      <td>0.237455</td>\n",
       "      <td>0.037342</td>\n",
       "      <td>-0.257703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insurance</th>\n",
       "      <td>0.532947</td>\n",
       "      <td>0.485776</td>\n",
       "      <td>0.475430</td>\n",
       "      <td>0.594049</td>\n",
       "      <td>-0.109521</td>\n",
       "      <td>0.146579</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.019925</td>\n",
       "      <td>-0.093974</td>\n",
       "      <td>0.024551</td>\n",
       "      <td>0.036502</td>\n",
       "      <td>0.018846</td>\n",
       "      <td>0.015380</td>\n",
       "      <td>0.010980</td>\n",
       "      <td>0.030911</td>\n",
       "      <td>0.232503</td>\n",
       "      <td>0.233701</td>\n",
       "      <td>0.326057</td>\n",
       "      <td>0.984724</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.234889</td>\n",
       "      <td>0.056985</td>\n",
       "      <td>-0.254706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_school</th>\n",
       "      <td>0.175762</td>\n",
       "      <td>0.137309</td>\n",
       "      <td>0.163365</td>\n",
       "      <td>0.220669</td>\n",
       "      <td>0.155835</td>\n",
       "      <td>0.096551</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.068425</td>\n",
       "      <td>-0.354553</td>\n",
       "      <td>-0.036948</td>\n",
       "      <td>-0.027868</td>\n",
       "      <td>-0.048058</td>\n",
       "      <td>-0.138266</td>\n",
       "      <td>-0.098727</td>\n",
       "      <td>0.007675</td>\n",
       "      <td>-0.002861</td>\n",
       "      <td>0.169439</td>\n",
       "      <td>0.400306</td>\n",
       "      <td>0.237455</td>\n",
       "      <td>0.234889</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.145572</td>\n",
       "      <td>-0.167141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_schools</th>\n",
       "      <td>-0.014380</td>\n",
       "      <td>-0.124359</td>\n",
       "      <td>-0.088299</td>\n",
       "      <td>-0.102032</td>\n",
       "      <td>-0.188106</td>\n",
       "      <td>-0.031535</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.251171</td>\n",
       "      <td>0.272856</td>\n",
       "      <td>0.196399</td>\n",
       "      <td>0.224312</td>\n",
       "      <td>0.228988</td>\n",
       "      <td>0.214163</td>\n",
       "      <td>0.262651</td>\n",
       "      <td>0.235246</td>\n",
       "      <td>0.071455</td>\n",
       "      <td>-0.212870</td>\n",
       "      <td>0.049756</td>\n",
       "      <td>0.037342</td>\n",
       "      <td>0.056985</td>\n",
       "      <td>-0.145572</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.007361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tx_year</th>\n",
       "      <td>0.108782</td>\n",
       "      <td>-0.174081</td>\n",
       "      <td>-0.132110</td>\n",
       "      <td>-0.236190</td>\n",
       "      <td>-0.043301</td>\n",
       "      <td>-0.048207</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.044739</td>\n",
       "      <td>0.115674</td>\n",
       "      <td>0.027495</td>\n",
       "      <td>0.013558</td>\n",
       "      <td>-0.000324</td>\n",
       "      <td>0.034356</td>\n",
       "      <td>0.008066</td>\n",
       "      <td>0.028759</td>\n",
       "      <td>-0.118490</td>\n",
       "      <td>-0.134787</td>\n",
       "      <td>-0.189932</td>\n",
       "      <td>-0.257703</td>\n",
       "      <td>-0.254706</td>\n",
       "      <td>-0.167141</td>\n",
       "      <td>0.007361</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    tx_price      beds     baths      sqft  year_built  \\\n",
       "tx_price            1.000000  0.384046  0.389375  0.469573    0.033138   \n",
       "beds                0.384046  1.000000  0.644572  0.691039   -0.011844   \n",
       "baths               0.389375  0.644572  1.000000  0.682060    0.206141   \n",
       "sqft                0.469573  0.691039  0.682060  1.000000    0.254589   \n",
       "year_built          0.033138 -0.011844  0.206141  0.254589    1.000000   \n",
       "lot_size            0.130558  0.164399  0.132976  0.246016    0.000068   \n",
       "basement                 NaN       NaN       NaN       NaN         NaN   \n",
       "restaurants        -0.038027 -0.495834 -0.350210 -0.353759   -0.106948   \n",
       "groceries          -0.094314 -0.421412 -0.340024 -0.371167   -0.222443   \n",
       "nightlife           0.009361 -0.440844 -0.306686 -0.281540   -0.080006   \n",
       "cafes              -0.001398 -0.464289 -0.316836 -0.300969   -0.102209   \n",
       "shopping           -0.038246 -0.388670 -0.259603 -0.275586   -0.121684   \n",
       "arts_entertainment -0.021076 -0.442168 -0.305885 -0.293402   -0.195141   \n",
       "beauty_spas        -0.054349 -0.419832 -0.282008 -0.310465   -0.163670   \n",
       "active_life        -0.001165 -0.486957 -0.329736 -0.332663   -0.134919   \n",
       "median_age          0.126335  0.133711  0.095844  0.109811   -0.237152   \n",
       "married             0.200494  0.643240  0.442225  0.480167    0.038208   \n",
       "college_grad        0.268577 -0.082354  0.016097  0.065343   -0.014204   \n",
       "property_tax        0.535148  0.547643  0.525776  0.660264   -0.046504   \n",
       "insurance           0.532947  0.485776  0.475430  0.594049   -0.109521   \n",
       "median_school       0.175762  0.137309  0.163365  0.220669    0.155835   \n",
       "num_schools        -0.014380 -0.124359 -0.088299 -0.102032   -0.188106   \n",
       "tx_year             0.108782 -0.174081 -0.132110 -0.236190   -0.043301   \n",
       "\n",
       "                    lot_size  basement  restaurants  groceries  nightlife  \\\n",
       "tx_price            0.130558       NaN    -0.038027  -0.094314   0.009361   \n",
       "beds                0.164399       NaN    -0.495834  -0.421412  -0.440844   \n",
       "baths               0.132976       NaN    -0.350210  -0.340024  -0.306686   \n",
       "sqft                0.246016       NaN    -0.353759  -0.371167  -0.281540   \n",
       "year_built          0.000068       NaN    -0.106948  -0.222443  -0.080006   \n",
       "lot_size            1.000000       NaN    -0.113887  -0.118574  -0.072224   \n",
       "basement                 NaN       NaN          NaN        NaN        NaN   \n",
       "restaurants        -0.113887       NaN     1.000000   0.701462   0.926675   \n",
       "groceries          -0.118574       NaN     0.701462   1.000000   0.565003   \n",
       "nightlife          -0.072224       NaN     0.926675   0.565003   1.000000   \n",
       "cafes              -0.098182       NaN     0.937616   0.602857   0.923901   \n",
       "shopping           -0.116560       NaN     0.818815   0.526356   0.743321   \n",
       "arts_entertainment -0.067189       NaN     0.726907   0.502837   0.703038   \n",
       "beauty_spas        -0.121075       NaN     0.867829   0.626850   0.777461   \n",
       "active_life        -0.092668       NaN     0.881799   0.574532   0.858836   \n",
       "median_age          0.099140       NaN    -0.063816  -0.022377  -0.028611   \n",
       "married             0.122028       NaN    -0.615584  -0.508793  -0.568811   \n",
       "college_grad       -0.030725       NaN     0.214260  -0.006025   0.253717   \n",
       "property_tax        0.165800       NaN    -0.100821  -0.159506  -0.054386   \n",
       "insurance           0.146579       NaN    -0.019925  -0.093974   0.024551   \n",
       "median_school       0.096551       NaN    -0.068425  -0.354553  -0.036948   \n",
       "num_schools        -0.031535       NaN     0.251171   0.272856   0.196399   \n",
       "tx_year            -0.048207       NaN     0.044739   0.115674   0.027495   \n",
       "\n",
       "                       cafes  shopping  arts_entertainment  beauty_spas  \\\n",
       "tx_price           -0.001398 -0.038246           -0.021076    -0.054349   \n",
       "beds               -0.464289 -0.388670           -0.442168    -0.419832   \n",
       "baths              -0.316836 -0.259603           -0.305885    -0.282008   \n",
       "sqft               -0.300969 -0.275586           -0.293402    -0.310465   \n",
       "year_built         -0.102209 -0.121684           -0.195141    -0.163670   \n",
       "lot_size           -0.098182 -0.116560           -0.067189    -0.121075   \n",
       "basement                 NaN       NaN                 NaN          NaN   \n",
       "restaurants         0.937616  0.818815            0.726907     0.867829   \n",
       "groceries           0.602857  0.526356            0.502837     0.626850   \n",
       "nightlife           0.923901  0.743321            0.703038     0.777461   \n",
       "cafes               1.000000  0.837058            0.694575     0.822541   \n",
       "shopping            0.837058  1.000000            0.653803     0.860987   \n",
       "arts_entertainment  0.694575  0.653803            1.000000     0.757860   \n",
       "beauty_spas         0.822541  0.860987            0.757860     1.000000   \n",
       "active_life         0.826648  0.678515            0.800994     0.785920   \n",
       "median_age          0.026400  0.032339           -0.014640    -0.002961   \n",
       "married            -0.581850 -0.454072           -0.554533    -0.510331   \n",
       "college_grad        0.266652  0.177383            0.239129     0.177066   \n",
       "property_tax       -0.044410 -0.040556           -0.066346    -0.057293   \n",
       "insurance           0.036502  0.018846            0.015380     0.010980   \n",
       "median_school      -0.027868 -0.048058           -0.138266    -0.098727   \n",
       "num_schools         0.224312  0.228988            0.214163     0.262651   \n",
       "tx_year             0.013558 -0.000324            0.034356     0.008066   \n",
       "\n",
       "                    active_life  median_age   married  college_grad  \\\n",
       "tx_price              -0.001165    0.126335  0.200494      0.268577   \n",
       "beds                  -0.486957    0.133711  0.643240     -0.082354   \n",
       "baths                 -0.329736    0.095844  0.442225      0.016097   \n",
       "sqft                  -0.332663    0.109811  0.480167      0.065343   \n",
       "year_built            -0.134919   -0.237152  0.038208     -0.014204   \n",
       "lot_size              -0.092668    0.099140  0.122028     -0.030725   \n",
       "basement                    NaN         NaN       NaN           NaN   \n",
       "restaurants            0.881799   -0.063816 -0.615584      0.214260   \n",
       "groceries              0.574532   -0.022377 -0.508793     -0.006025   \n",
       "nightlife              0.858836   -0.028611 -0.568811      0.253717   \n",
       "cafes                  0.826648    0.026400 -0.581850      0.266652   \n",
       "shopping               0.678515    0.032339 -0.454072      0.177383   \n",
       "arts_entertainment     0.800994   -0.014640 -0.554533      0.239129   \n",
       "beauty_spas            0.785920   -0.002961 -0.510331      0.177066   \n",
       "active_life            1.000000   -0.081159 -0.632052      0.323985   \n",
       "median_age            -0.081159    1.000000  0.045555      0.169749   \n",
       "married               -0.632052    0.045555  1.000000     -0.143461   \n",
       "college_grad           0.323985    0.169749 -0.143461      1.000000   \n",
       "property_tax          -0.057646    0.215435  0.305330      0.287933   \n",
       "insurance              0.030911    0.232503  0.233701      0.326057   \n",
       "median_school          0.007675   -0.002861  0.169439      0.400306   \n",
       "num_schools            0.235246    0.071455 -0.212870      0.049756   \n",
       "tx_year                0.028759   -0.118490 -0.134787     -0.189932   \n",
       "\n",
       "                    property_tax  insurance  median_school  num_schools  \\\n",
       "tx_price                0.535148   0.532947       0.175762    -0.014380   \n",
       "beds                    0.547643   0.485776       0.137309    -0.124359   \n",
       "baths                   0.525776   0.475430       0.163365    -0.088299   \n",
       "sqft                    0.660264   0.594049       0.220669    -0.102032   \n",
       "year_built             -0.046504  -0.109521       0.155835    -0.188106   \n",
       "lot_size                0.165800   0.146579       0.096551    -0.031535   \n",
       "basement                     NaN        NaN            NaN          NaN   \n",
       "restaurants            -0.100821  -0.019925      -0.068425     0.251171   \n",
       "groceries              -0.159506  -0.093974      -0.354553     0.272856   \n",
       "nightlife              -0.054386   0.024551      -0.036948     0.196399   \n",
       "cafes                  -0.044410   0.036502      -0.027868     0.224312   \n",
       "shopping               -0.040556   0.018846      -0.048058     0.228988   \n",
       "arts_entertainment     -0.066346   0.015380      -0.138266     0.214163   \n",
       "beauty_spas            -0.057293   0.010980      -0.098727     0.262651   \n",
       "active_life            -0.057646   0.030911       0.007675     0.235246   \n",
       "median_age              0.215435   0.232503      -0.002861     0.071455   \n",
       "married                 0.305330   0.233701       0.169439    -0.212870   \n",
       "college_grad            0.287933   0.326057       0.400306     0.049756   \n",
       "property_tax            1.000000   0.984724       0.237455     0.037342   \n",
       "insurance               0.984724   1.000000       0.234889     0.056985   \n",
       "median_school           0.237455   0.234889       1.000000    -0.145572   \n",
       "num_schools             0.037342   0.056985      -0.145572     1.000000   \n",
       "tx_year                -0.257703  -0.254706      -0.167141     0.007361   \n",
       "\n",
       "                     tx_year  \n",
       "tx_price            0.108782  \n",
       "beds               -0.174081  \n",
       "baths              -0.132110  \n",
       "sqft               -0.236190  \n",
       "year_built         -0.043301  \n",
       "lot_size           -0.048207  \n",
       "basement                 NaN  \n",
       "restaurants         0.044739  \n",
       "groceries           0.115674  \n",
       "nightlife           0.027495  \n",
       "cafes               0.013558  \n",
       "shopping           -0.000324  \n",
       "arts_entertainment  0.034356  \n",
       "beauty_spas         0.008066  \n",
       "active_life         0.028759  \n",
       "median_age         -0.118490  \n",
       "married            -0.134787  \n",
       "college_grad       -0.189932  \n",
       "property_tax       -0.257703  \n",
       "insurance          -0.254706  \n",
       "median_school      -0.167141  \n",
       "num_schools         0.007361  \n",
       "tx_year             1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A lot of numbers make things difficult to read. So let's visualize this.\n",
    "But first, it's important to notice that the correlations for 'basement' all show as NaN. This is expected because right now that feature doesn't vary at all (its standard deviation is 0), as we saw all the way back in step 2. We'll fix this later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xad15170>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B31W7CEkzgBkAnxo5iYlD392OSzczM7NOqeF7/nIrSkLY2rPUXm1lX2t9uwKu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\nJSGMiIiIGLgkhBERERED93+/8KsYjrin5wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xadd1b30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What to look for?**\n",
    "* The colorbar on the right explains the meaning of the heatmap - Dark colors indicate **strong negative correlations** and light colors indicate **strong positive correlations**.\n",
    "* Perhaps the most helpful way to interpret this correlation heatmap is to first find features that are correlated with our target variable by scanning the first column.\n",
    "* In this case, it doesn't look like many features are strongly correlated with the target variable.\n",
    "* There is a weak positive correlation between 'tx_price' and 'property_tax'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Just making the heatmap visualization more fancy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CVrj7N+4eJuhdi40ePYLurRUr4Omng6GQTzwRbDvgAOjWDV5+OaolV60KXp98EixPmxYE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Qtm1r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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xae0e2f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask=np.zeros_like(df.corr())\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "plt.figure(figsize=(15,10))\n",
    "with sns.axes_style(\"white\"):\n",
    "    ax = sns.heatmap(df.corr()*100, mask=mask, fmt='.0f', annot=True, lw=1, cmap=ListedColormap(['green', 'yellow', 'red','blue']))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Dropping the duplicates (De-duplication)\n",
    "Duplicate observations most frequently arise during data collection, such as when you:\n",
    "* Combine datasets from multiple places\n",
    "* Scrape data\n",
    "* Receive data from clients/other departments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1883, 26)\n"
     ]
    }
   ],
   "source": [
    "df=df.drop_duplicates()\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like we didn't have any duplicates in our original dataset. Even so, it's a good idea to check this as an easy first step for cleaning your dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Fix structural errors\n",
    "* Recall, the basement features had some nan values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ nan,   1.])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.basement.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Even though NaN represents \"missing\" values, those are actually meant to indicate properties without basements.\n",
    "* Fill missing 'basement' values with the value 0 to turn 'basement' into a true indicator variable.\n",
    "\n",
    "**Note** - Nan in basement doesn't means that value is missing, their Nan represent that praticular property donot having basement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.basement.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.basement.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 Typos and capitalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd6bdc88>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cAYwHWnwC3MzMassj5No6lJRNqeh64FLgfUlzgdnAN/O+94HNJU0A3iWlRIQU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc416a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Class distributions for 'roof'\n",
    "sns.countplot(y='roof', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this plot, we can easily catch typos and inconsistent capitalization. For example:\n",
    "* 'composition' should be 'Composition'\n",
    "* 'asphalt' should be 'Asphalt'\n",
    "* 'shake-shingle' should be 'Shake Shingle'\n",
    "* 'asphalt,shake-shingle' could probably just be 'Shake Shingle'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.roof.replace('composition', 'Composition', inplace=True)\n",
    "df.roof.replace('asphalt', 'Asphalt', inplace=True)\n",
    "df.roof.replace(['shake-shingle', 'asphalt,shake-shingle'], 'Shake Shingle', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc41fb00>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UuNsAOO+wjeocjZlZ1yBpRkQ0tVbPQ9ZmZmYF4IRsZmZWAL6GbFXbqG9PD1Wb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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcd11fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Class distribution for 'roof' after the modifications\n",
    "sns.countplot(y='roof', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4 Mislabeled classes\n",
    "Finally, we'll check for classes that are labeled as separate classes when they should really be the same.\n",
    "* e.g. If 'N/A' and 'Not Applicable' appear as two separate classes, we should combine them.\n",
    "* For example, let's plot the class distributions for 'exterior_walls':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd5637b8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AD9YY2z6snFivBYay/DJ0ra4grfDHV9n+AeC+iFjayuObmVkbKTVI6ZryZd3VIuINSYOB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\nVjMzszpyYjUzM6uj/w+gNxPv3JsH7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xceeaf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='exterior_walls', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using that chart, we can easily catch mislabeled classes. For example\n",
    "* 'Rock, Stone' should be 'Masonry'\n",
    "* 'Concrete' and 'Block' should both just be 'Concrete Block'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcee7160>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K0iWS/resfQ9J1xQ+j5T0f/n97pLG51HztZL65fZZkn6S26dL2ii3ryjpQkn3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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xccf72b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.exterior_walls.replace(['Rock, Stone'], 'Masonry', inplace=True)\n",
    "df.exterior_walls.replace(['Concrete', 'Block'], 'Concrete Block', inplace=True)\n",
    "sns.countplot(y='exterior_walls', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.5 Removing Outliers\n",
    "* Outliers can cause problems with certain types of models.\n",
    "* Boxplots are a nice way to detect outliers\n",
    "* Let's start with a box plot of your target variable, since that's what you're actually trying to predict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcf1ea20>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAAELCAYAAADuufyvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwiBLUhIGWZKSMMiSlMT/AeNmcCsWUkm7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xce12240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df.tx_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation**\n",
    "\n",
    "* The two vertical bars on the ends are the min and max values. All properties sold for between \\$200,000 and \\$800,000.\n",
    "* The box in the middle is the interquartile range (25th percentile to 75th percentile).\n",
    "* Half of all observations fall in that box.\n",
    "* Finally, the vertical bar in the middle of the box is the median."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xce125f8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAAEKCAYAAAAl5S8KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xce7fcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Checking outliers in number of bedrooms\n",
    "sns.boxplot(df.beds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcdc5518>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xcc20c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Checking outliers in size of the house\n",
    "sns.boxplot(df.sqft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcc5b3c8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8A82P5Qvi9ULc3lt2+y1xnAcVz4CnrVetPuYw/l+QNBrX41tKHtAdtxaS7lDyFqsDkv46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\ni5Roj5DQsQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcc19080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Checking outliers in lot size\n",
    "sns.boxplot(df.lot_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the dot on the extreme right. This might indicate some erroneous point.\n",
    "\n",
    "Let's look at the largest 5 lot sizes just to confirm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "102     1220551\n",
       "1111     436471\n",
       "1876     436035\n",
       "1832     436035\n",
       "1115     435600\n",
       "Name: lot_size, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.lot_size.sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The largest property has a lot_size of 1,220,551 sqft. The next largest has a lot_size of only 436,471 sqft.\n",
    "\n",
    "Because it's unlikely the REIT will ever invest in properties with lots that large, and because it's the only one in the dataset, let's remove it so it doesn't interfere with our ability to model normal size properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1882, 26)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Remove observations with lot_size greater than 500,000 sqft.\n",
    "df = df[df.lot_size <= 500000]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcbd2e48>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vpPw7AtYpdw41X742vqvgFkln47/VL0i6w8yuju8KuEO5c1mnJf3QzG6J7yJYW9RWWh8N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\nAAAnCGQAcIJABgAnCGQAcIJABgAn/h//lOmaK1XrIQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd5ecda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plotting the boxplot of lot size after the change\n",
    "sns.boxplot(df.lot_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Even though visually the plot looks the same but the x-axis dimensions have changed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6 Label missing categorical data\n",
    "You cannot simply ignore missing values in your dataset. You must handle them in some way for the very practical reason that Scikit-Learn algorithms do not accept missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_type       0\n",
       "exterior_walls    223\n",
       "roof              353\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display number of missing values by categorical feature\n",
    "df.select_dtypes(include=['object']).isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The best way to handle missing data for categorical features is to simply label them as 'Missing'!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_type     0\n",
       "exterior_walls    0\n",
       "roof              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['exterior_walls'] = df['exterior_walls'].fillna('Missing')\n",
    "df['roof'] = df['roof'].fillna('Missing')\n",
    "df.select_dtypes(include=['object']).isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.7 Flag and fill missing numeric data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tx_price              0\n",
       "beds                  0\n",
       "baths                 0\n",
       "sqft                  0\n",
       "year_built            0\n",
       "lot_size              0\n",
       "basement              0\n",
       "restaurants           0\n",
       "groceries             0\n",
       "nightlife             0\n",
       "cafes                 0\n",
       "shopping              0\n",
       "arts_entertainment    0\n",
       "beauty_spas           0\n",
       "active_life           0\n",
       "median_age            0\n",
       "married               0\n",
       "college_grad          0\n",
       "property_tax          0\n",
       "insurance             0\n",
       "median_school         0\n",
       "num_schools           0\n",
       "tx_year               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display number of missing values by numeric feature\n",
    "df.select_dtypes(exclude=['object']).isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, it looks like we don't have any numerical features with missing values in this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Before we move on to the next module, let's save the new dataframe we worked hard to clean.\n",
    "This makes sure we don't have to re-do all the cleaning after clossing the session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save cleaned dataframe to new file\n",
    "df.to_csv('Files/cleaned_df.csv', index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# 5. Feature Engineering\n",
    "* Feature engineering is the practice of creating new features from existing ones\n",
    "* The engineered features are often more specific or isolate key information.\n",
    "* Often, feature engineering is one of the most valuable tasks a data scientist can do to improve model effectiveness.\n",
    "\n",
    "**Note:** There are limitless possibilities for Feature Engineering, and it's a skill that will naturally improve as you gain more experience and domain expertise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 Indicator variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* For example, let's say you knew that homes with 2 bedrooms and 2 bathrooms are especially popular for investors.\n",
    "* Maybe you suspect these types of properties command premium prices. (You don't need to know for sure.)\n",
    "* Create an indicator variable to flag properties with 2 beds and 2 baths and name it 'two_and_two'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['two_and_two'] = ((df.beds == 2) & (df.baths == 2)).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09458023379383634"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display percent of rows where two_and_two == 1\n",
    "df[df['two_and_two']==1].shape[0]/df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interpretation is that almost 9.5% of the observations in our dataset were for properties with 2 beds and 2 baths."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There are some people which are inserted in 3 or 4 bed rooms Apartment, so lets try to figure it out "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['3-4 bed'] = (df.beds >2).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8113708820403825"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display percent of rows where 3-4 bed == 1\n",
    "df[df['3-4 bed']==1].shape[0]/df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interpretation is that almost 81.1% of the observations in our dataset were for properties with 3 &4 beds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There are many people who are inserted in new_property as compared to older property, so let us try to figure out the older property as one of the feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['older_property'] = (df.year_built < 1970).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24920297555791712"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display percent of rows where older_property == 1\n",
    "df[df['older_property']==1].shape[0]/df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interpretation is that almost 24.9% of the observations in our dataset were for properties which are older."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* \n",
    "Create a new feature containing tax and insurance which a propery holder will have to pay monthly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['tax_and_insurance'] = df.property_tax + df.insurance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: housing market recession\n",
    "According to data from Zillow, the lowest housing prices were from 2010 to end of 2013 (country-wide).\n",
    "![title](Images/zillow-housing-prices.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new feature called 'during_recession' to indicate if a transaction falls between 2010 and 2013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['during_recession'] = ((df.tx_year >= 2010) & (df.tx_year <= 2013)).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2635494155154091"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print percent of transactions where during_recession == 1\n",
    "df[df['during_recession']==1].shape[0]/df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 Interaction features\n",
    "* Interaction features are operations between two or more other features.\n",
    "* In some contexts, \"interaction terms\" must be products of two variables.\n",
    "* In our context, interaction features can be products, sums, or differences between two features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* For example, in our dataset, we know the transaction year and the year the property was built in.\n",
    "* However, the more useful piece of information that combining these two features provides is the age of the property at the time of the transaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a property age feature\n",
    "df['property_age'] = df.tx_year - df.year_built"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** 'property_age' denotes the age of the property when it was sold and not how old it is today, since we want to predict the price at the time when the property is sold.\n",
    "\n",
    "**Sanity Check:** It's always nice to do a quick sanity check after creating a feature, which could save you headaches down the road. For example, 'property_age' should never be less than 0, right?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-8\n"
     ]
    }
   ],
   "source": [
    "print(df.property_age.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice find. Something is definitely wrong. Age of property can never be negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n"
     ]
    }
   ],
   "source": [
    "# Number of observations with 'property_age' < 0\n",
    "print(sum(df.property_age < 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* On second thought, it's possible that some home owners bought houses before the construction company built them.\n",
    "* This is not uncommon, especially with single-family homes.\n",
    "* However, for this problem, **we are only interested in houses that already exist** because the REIT only buys existing ones!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1863, 32)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove rows where property_age is less than 0\n",
    "df = df[df.property_age >= 0]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's add another interaction feature. How about the number of quality schools nearby?\n",
    "* Well, we do know the number of schools nearby (<code style=\"color:steelblue\">'num_schools'</code>) \n",
    "* We also have their median quality score (<code style=\"color:steelblue\">'median_schools'</code>)\n",
    "* But what if it's the **interaction** of those two factors that's really important? \n",
    "* In other words, what if it's good to have many school options, but only if they are good?\n",
    "\n",
    "We can represent this with an interaction feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a school score feature that num_schools * median_school\n",
    "df['school_score'] = df.num_schools * df.median_school"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display median school score\n",
    "df.school_score.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3 Handling Sparse Classes\n",
    "* Sparse classes are those that have very few total observations.\n",
    "* As mentioned earlier, they can be problematic for certain machine learning algorithms.\n",
    "* At best, they are ignored.\n",
    "* At worst, they can cause models to be overfit.\n",
    "\n",
    "The easiest way to check for sparse classes is simply by plotting the distributions of your categorical features. We already did this during exploratory analysis, but since we've done some data cleaning since then, let's plot them again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcaef5c0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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picUOEfFibrsf+D/gIWrzP8Aq+fg/JE0Fv1mh382kB4kgTfveULb9OuDrZbE9B1ydj3kZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd6600f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot for exterior_walls\n",
    "sns.countplot(y='exterior_walls', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group 'Wood Siding', 'Wood Shingle', and 'Wood' together. Label all of them as 'Wood'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.exterior_walls.replace(['Wood Siding', 'Wood Shingle', 'Wood'], 'Wood', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we can group the remaining sparse classes into a single 'Other' class, even though there's already an 'Other' class.\n",
    "Let's label 'Stucco', 'Other', 'Asbestos shingle', 'Concrete Block', and 'Masonry' as 'Other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "other_exterior_walls = ['Concrete Block', 'Stucco', 'Masonry', 'Other', 'Asbestos shingle']\n",
    "df.exterior_walls.replace(other_exterior_walls, 'Other', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, now that we've grouped together sparse classes, let's look at the bar plot for exterior walls again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcad85c0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A/umzaiozMzOzpJbXxh0HfJn33ov6a0mTI+I/6xKZ1c3zi17hZ//y+2aHYV34+ln7NzsE\nM1sOtQwFfxHYKSKWAEj6IdnD+J1YzczMklpmBYvsxdslnfgxhmZmZu9TS4/1YmBmessKZC8Gv7D4\nkMzMzFpXLW+3+YmkaWTvZBVwRETU+vJvMzOzPq3HxCpplYj4u6TVgYXpU9q2ekQsrl94ZmZmraWa\nHusVwH5AB+9/g43S+sZ1iMvMzKwl9Th5KSL2Sw/h3zUiNs59NooIJ1VAUki6LLc+UNILkm5M6wdI\nOnEZ2p1RZJxmZlZ/tTyE//oeK/ZfS4CtJQ1J63sC/1PaGBE3RMQZtTYaEbsUFJ+ZmTWIH8JfnJuB\nT6Xlg4ErSxskHS7pZ2n585LmS5oj6e5UtpWkWZJmS5oradNU/lr6niBpmqRrJT0q6fI0ioCkfVPZ\nPZLOLfWSzcysOWpJrLuRJdcn0i//eZLm1iuwFnQV8AVJg4GPAjO7qHcK8MmI2AY4IJUdBZwTEWOB\nNmBRhf22BY4HtiS7rj0+Het8YJ+I+BiwZlEnY2Zmy8YP4S9IRMyVNJqst3pTN1WnA5dIupr3Hg95\nL3CypPWA30TE4xX2mxURiwAkzQZGA68BT0bEU6nOlcCkSgeVNKm0bbXhzr9mZvVS60P41wd2T8uv\n17J/P3ED8GNyw8DlIuIo4N/I/i1nSxoZEVeQ9V7fAG6VtHuFXZfmljvJ/iiq+slXETE5Itoiom3Y\n0BHV7mZmZjWq5SH83yEbptyc7ClMg4BfA+PrE1pLugh4JSLmSZpQqYKkTSJiJtlTrPYH1pc0gqzn\nea6kjcmGku+s4niPAhtLGh0RC4GJhZyFmZkts1qGgj9Ldp3vAYCIeEbS8LpE1aLSUO05PVQ7M01O\nEnAHMAc4EfhnSW8BfwVOq/J4b0g6GrhF0ovArGUO3szMClFLYv1HRISkAJC0cp1iajkRMaxC2TRg\nWlq+BLgkLX+uQhM/SJ+K7ebbSutfz1WbGhFj0izhnwPty3IOZmZWjFqukV4t6XxgVUlfBv4IXFCf\nsKwGX06TmR4CRpDNEjYzsyap5SH8P5a0J/B3suusp0TE7XWLzKoSEWcDZzc7DjMzy9QyeemHEfEt\n4PYKZWZmZkZtQ8F7Vijzva1mZmY51bw27qvA0cAmZU9aGg74IfFmZmY51b427mayWav5N7S86nex\nmpmZvV+PiTUiXgFekXRheuLSuyQdFhGX1i06q4u11hvB18/av9lhmJn1SbVcYz1F0nmSVpa0tqTf\nA/7tbGZmllNLYt0VeAKYDdwDXBERB9UlKjMzsxZVS2JdDdiJLLkuBTYsvRPUzMzMMjW96By4OSL2\nBnYA1iF7BZqZmZkliojqKkobkA0HbxQRp6X10RFxdz0DtOKtO3K1OHqfTzQ7jLo5+dfXNjsEM+uD\nJHVERFtP9WrpsZ4E7Ez2Im+AV4GzliE2MzOzPquWt9vsFBHbSXoQICJelrRineIyMzNrSbX0WN+S\nNAAovTZuTeCdukRlZmbWompJrOcC1wNrSTqd7Jab/6hLVGZmZi2qltfGXS6pA/gEIOAzEfFI3SIz\nMzNrQbVcYyUiHgUerVMsZmZmLa+WoWBbTpJC0mW59YGSXpB0Yw/7jZW0bxXtT+ipLTMzqy8n1sZa\nAmwtaUha3xP4nyr2Gwv0mFjNzKz5nFgb72bgU2n5YODK0ob0goOLJN0v6UFJn063NJ0GTJQ0W9JE\nSTtKmpHqzJC0eRPOw8zMKnBibbyrgC9IGgx8FJiZ23YycGdE7ADsBpwJDAJOAaZExNiImEJ2nfvj\nEbFt2ubZ2WZmvURNk5ds+UXEXEmjyXqrN5Vt3gs4QNIJaX0wsEGFZkYAl0ralOy+4kE9HVfSJGAS\nwIihQ3qobWZmy8qJtTluAH4MTABG5soFHBgRC/KVJe1Utv/3gKkR8dmUpKf1dMCImAxMhuxZwcsY\nt5mZ9cBDwc1xEXBaRMwrK78VOKb0Oj5J26byV4HhuXojeG/S0+F1jNPMzGrkxNoEEbEoIs6psOl7\nZMO6cyXNT+sAU4EtS5OXgB8BP5A0HRjQkKDNzKwqHgpuoIgYVqFsGmkoNyLeAL5Soc5isnfg5m2W\nW/738rbMzKw53GM1MzMrkBOrmZlZgZxYzczMCuTEamZmViAnVjMzswI5sZqZmRXIt9v0Q6M22oST\nf31ts8MwM+uT3GM1MzMrkBOrmZlZgZxYzczMCuTEamZmViBPXuqH3nz2VR45/c66tL3FybvXpV0z\ns1bhHquZmVmBnFjNzMwK5MRqZmZWICdWMzOzAjmxmpmZFciJ1czMrEB9PrFK6pQ0W9IcSQ9I2qWb\nujN6aGuapLbiozQzs76iP9zH+kZEjAWQ9EngB8Cu+QqSBkREZ0R0mXRbSel8mh2HmVl/1Od7rGVW\nAV4GkDRB0lRJVwDzUtlrpYqSvilpXurpnpFvRNIKki6V9P2y8n0kXZ1bnyDp92l5L0n3pl7zNZKG\npfKFkr6byudJGpPKV5Z0kaT7JT0o6dOpfICkM1P5XElf6ep8zMys8fpDj3WIpNnAYGAUkH800I7A\n1hHxVH4HSfsAnwF2iojXJa2e2zwQuByYHxGnlx3rduB8SStHxBJgIjBF0hrAvwF7RMQSSd8CvgGc\nlvZ7MSK2k3Q0cALwJeBk4M6IOFLSqsAsSX8EDgFeiYgdJK0ETJd0W3fnY2ZmjdMfeqxvRMTYiBgD\n7A38SpLStlldJKE9gIsj4nWAiFic23Y+lZMqEfE2cAuwv6SBwKeA3wE7A1uSJcHZwGHAhrldf5O+\nO4DRaXkv4MRUfxrZHwYbpPJDU/lMYCSwaQ/ng6RJktoltS9e8rdKVczMrAD9ocf6roi4N/Ue10xF\nS7qoKiC62DYD2E3SWRHxZoXtU4CvAYuB+yPi1ZTIb4+Ig7toc2n67uS9/yYCDoyIBe8LLGvrmIi4\ntax8QjfnQ0RMBiYDbL3u5l2dm5mZLaf+0GN9V7p+OQB4qYeqtwFHShqa9ssPBV8I3ARck3ql5aYB\n2wFfJkuyAPcB4yV9OLU3VNJmPcRwK3BMqXctadtc+VclDUrlm0lauYe2zMysQfpDYh2SbreZTZbo\nDutpxmxE3ALcALSn/U4o2/4T4AHgMkkrlG3rBG4E9knfRMQLwOHAlZLmkiXaMT3E/T1gEDBX0vy0\nDnAB8DDwQCo/n3428mBm1pspwqOC/c3W624e1xx9Xl3a9mvjzKyvktQRET0+y6A/9FjNzMwaxonV\nzMysQE6sZmZmBXJiNTMzK5ATq5mZWYGcWM3MzArk+x/7ocGjhvu2GDOzOnGP1czMrEBOrGZmZgVy\nYjUzMyuQE6uZmVmBPHmpH3rmmWc49dRTmx3GcusL52BmfY97rGZmZgVyYjUzMyuQE6uZmVmBnFjN\nzMwK5MRqZmZWICdWMzOzAjmxmpmZFajPJ1ZJH5J0laQnJD0s6SZJmy1nm5dIOqhCeZukc5ej3W+X\nrc9Y1rbMzKw5+nRilSTgemBaRGwSEVsC3wbWrsfxIqI9Io5djibel1gjYpflDMnMzBqsTydWYDfg\nrYj4RakgImYD90g6U9J8SfMkTQSQNEHSXZKulvSYpDMkHSJpVqq3Sa7tPST9KdXbL7f/jWn5VEkX\nSZom6UlJ7yZcSb+V1CHpIUmTUtkZwBBJsyVdnspeS9/qJt5pkq6V9Kiky9MfE2Zm1iR9/ZGGWwMd\nFco/B4wFtgHWAO6XdHfatg2wBbAYeBK4ICJ2lHQccAxwfKo3GtgV2ASYKunDFY4zhiy5DwcWSDov\nIt4CjoyIxZKGpGNfFxEnSvp6RIytMd5tga2AZ4DpwHjgnvIGUgKfBDBixIgKhzAzsyL09R5rVz4G\nXBkRnRHxHHAXsEPadn9EPBsRS4EngNtS+TyyZFpydUS8ExGPkyXgMRWO84eIWBoRLwLP894Q9LGS\n5gD3AesDmy5HvLMiYlFEvAPMLovxXRExOSLaIqJt6NChPRzOzMyWVV9PrA8B21co7264dGlu+Z3c\n+ju8v4cfZfuVr5e31QkMlDQB2AMYFxHbAA8Cg7uJp5Z4O+n7oxBmZr1aX0+sdwIrSfpyqUDSDsDL\nwERJAyStCXwcmFVj25+XtEK67roxsKDK/UYAL0fE65LGADvntr0laVCFfe4uIF4zM2uAPt27iYiQ\n9Fngp5JOBN4EFpJdJx0GzCHraX4zIv6aEl21FpANya4NHBURb1Y5b+gW4ChJc1Mb9+W2TQbmSnog\nIg7JlV8PjFvOeM3MrAEUUWkE0/qyddZZJyZNmtTsMJab38dqZo0kqSMi2nqq19eHgs3MzBrKidXM\nzKxATqxmZmYFcmI1MzMrkBOrmZlZgTwruB9qa2uL9vb2ZodhZtZSPCvYzMysCZxYzczMCuSh4H5I\n0qtU/wjG3mYN4MVmB7GMWjX2Vo0bHHuztGrsPcW9YUSs2VMjffqRhtalBdVcJ+iNJLU79sZq1bjB\nsTdLq8ZeVNweCjYzMyuQE6uZmVmBnFj7p8nNDmA5OPbGa9W4wbE3S6vGXkjcnrxkZmZWIPdYzczM\nCuTE2s9I2lvSAkl/Ti9/71UkXSTpeUnzc2WrS7pd0uPpe7VULknnpnOZK2m7Jsa9vqSpkh6R9JCk\n41oo9sGSZkmak2L/birfSNLMFPsUSSum8pXS+p/T9tHNij3FM0DSg5JubLG4F0qaJ2m2pPZU1ut/\nXlI8q0q6VtKj6Wd+XCvELmnz9O9d+vxd0vFFx+7E2o9IGgD8HNgH2BI4WNKWzY3qAy4B9i4rOxG4\nIyI2Be5I65Cdx6bpMwk4r0ExVvI28C8RsQWwM/C19G/bCrEvBXaPiG2AscDeknYGfgicnWJ/Gfhi\nqv9F4OWI+DBwdqrXTMcBj+TWWyVugN0iYmzuFo9W+HkBOAe4JSLGANuQ/fv3+tgjYkH69x4LbA+8\nDlxP0bFHhD/95AOMA27NrZ8EnNTsuCrEORqYn1tfAIxKy6PI7sMFOB84uFK9Zn+A3wF7tlrswFDg\nAWAnshvlB5b/7AC3AuPS8sBUT02Kd730i3B34EZArRB3imEhsEZZWa//eQFWAZ4q/7drhdjL4t0L\nmF6P2N1j7V/WBZ7OrS9KZb3d2hHxLED6XiuV98rzSUOM2wIzaZHY03DqbOB54HbgCeBvEfF2hfje\njT1tfwUY2diI3/VT4JvAO2l9JK0RN0AAt0nqkDQplbXCz8vGwAvAxWkI/gJJK9Mased9AbgyLRca\nuxNr/6IKZa08LbzXnY+kYcB1wPER8ffuqlYoa1rsEdEZ2fDYesCOwBaVqqXvXhG7pP2A5yOiI19c\noWqvijtnfERsRzbc+DVJH++mbm+KfSCwHXBeRGwLLOG9odNKelPsAKTr7gcA1/RUtUJZj7E7sfYv\ni4D1c+vrAc80KZZaPCdpFED6fj6V96rzkTSILKleHhG/ScUtEXtJRPwNmEZ2nXhVSaXHnubjezf2\ntH0EsLixkQIwHjhA0kLgKrLh4J/S++MGICKeSd/Pk13n25HW+HlZBCyKiJlp/VqyRNsKsZfsAzwQ\nEc+l9UJjd2LtX+4HNk2zJlckGwq5ockxVeMG4LC0fBjZ9ctS+aFp5t7OwCul4ZxGkyTgQuCRiPhJ\nblMrxL6mpFXT8hBgD7LJKFOBg1K18thL53QQcGekC1CNFBEnRcR6ETGa7Gf5zog4hF4eN4CklSUN\nLy2TXe+bTwv8vETEX4GnJW2eij4BPEwLxJ5zMO8NA0PRsTf7ArI/jf0A+wKPkV1DO7nZ8VSI70rg\nWeAtsr8Wv0h2HewO4PH0vXqqK7JZzk8A84C2Jsb9MbIhornA7PTZt0Vi/yjwYIp9PnBKKt8YmAX8\nmWzIbKVUPjit/zlt37gX/NxMAG5slbhTjHPS56HS/4ut8POS4hkLtKefmd8Cq7VQ7EOBl4ARubJC\nY/eTl8zMzArkoWAzM7MCObGamZkVyInVzMysQE6sZmZmBXJiNTMzK5ATq5m1pPRWkqHNjsOsnG+3\nMbOWlJ641BYRLzY7FrM891jNrG4kHZreYzlH0mWSNpR0Ryq7Q9IGqd4lkg7K7fda+p4gaVru3Z+X\np6fgHAusA0yVNLU5Z2dW2cCeq5iZ1U7SVsDJZA+bf1HS6sClwK8i4lJJRwLnAp/poaltga3IntE6\nPbV3rqRvkL3P1D1W61XcYzWzetkduLaU+CJiMdn7Ua9I2y8jexRkT2ZFxKKIeIfsUZGj6xCrWWGc\nWM2sXkTPr9gqbX+b9PsovdBKW5ZBAAAAsElEQVRgxVydpbnlTjzSZr2cE6uZ1csdwD9JGgmQhoJn\nkL2JBuAQ4J60vBDYPi1/GhhURfuvAsOLCtasKP7Lz8zqIiIeknQ6cJekTrI36BwLXCTpX4EXgCNS\n9V8Cv5M0iywhL6niEJOBmyU9GxG7FX8GZsvGt9uYmZkVyEPBZmZmBXJiNTMzK5ATq5mZWYGcWM3M\nzArkxGpmZlYgJ1YzM7MCObGamZkVyInVzMysQP8LzY82PKESn2gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd6bde48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='exterior_walls', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly we check for 'roof'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc963ba8>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n6PZ3OHBfRMwB3pK0C/AV4P5UcWoAUCqasS7wVETsAjwM/DS1jwK+ExG7AqcB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\nYHUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc97c978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='roof', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group 'Composition' and 'Wood Shake/ Shingles' into the 'Composition Shingle' class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.roof.replace(['Composition', 'Wood Shake/ Shingles'], 'Composition Shingle', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's group remaining sparse classes into a single 'Other' class.\n",
    "\n",
    "Label 'Other', 'Gravel/Rock', 'Roll Composition', 'Slate', 'Built-up', 'Asbestos', and 'Metal' as 'Other'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "other_roof = ['Other', 'Gravel/Rock', 'Roll Composition', 'Slate', 'Built-up', 'Asbestos', 'Metal']\n",
    "df.roof.replace(other_roof, 'Other', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, display bar plot again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc705a58>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/1PofBZwwfOCq+jFwPHBaVS2pqtNGMkJJ0rRsPu4C5rmXAu9ry59pz78IfDzJFsAXqmoi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\nkqQODFxJkjowcCVJ6sDAlSSpg/8BB/NhjWUxKKQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xce12e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='roof', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now these plots look much nicer**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4 Encode dummy variables (One Hot Encoding)\n",
    "* Machine learning algorithms cannot directly handle categorical features. Specifically, they cannot handle text values.\n",
    "* Therefore, we need to create dummy variables for our categorical features.\n",
    "* *Dummy variables* are a set of binary (0 or 1) features that each represent a single class from a categorical feature.\n",
    "\n",
    "The information you represent is exactly the same, but this numeric representation allows you to pass Scikit-Learn's technical requirement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a new dataframe with dummy variables for for our categorical features.\n",
    "df = pd.get_dummies(df, columns=['exterior_walls', 'roof', 'property_type'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** There are many ways to perform one-hot encoding, you can also use LabelEncoder and OneHotEncoder classes in SKLEARN or use the above pandas function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's display the first 5 rows again to see these new features<br>\n",
    "Click the table and scroll over to the right.<br>\n",
    "Check dummy variables created for 'exterior_walls', 'roof' and 'property_type'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tx_price</th>\n",
       "      <th>beds</th>\n",
       "      <th>baths</th>\n",
       "      <th>sqft</th>\n",
       "      <th>year_built</th>\n",
       "      <th>lot_size</th>\n",
       "      <th>basement</th>\n",
       "      <th>restaurants</th>\n",
       "      <th>groceries</th>\n",
       "      <th>nightlife</th>\n",
       "      <th>cafes</th>\n",
       "      <th>shopping</th>\n",
       "      <th>arts_entertainment</th>\n",
       "      <th>beauty_spas</th>\n",
       "      <th>active_life</th>\n",
       "      <th>median_age</th>\n",
       "      <th>married</th>\n",
       "      <th>college_grad</th>\n",
       "      <th>property_tax</th>\n",
       "      <th>insurance</th>\n",
       "      <th>median_school</th>\n",
       "      <th>num_schools</th>\n",
       "      <th>tx_year</th>\n",
       "      <th>two_and_two</th>\n",
       "      <th>3-4 bed</th>\n",
       "      <th>older_property</th>\n",
       "      <th>tax_and_insurance</th>\n",
       "      <th>during_recession</th>\n",
       "      <th>property_age</th>\n",
       "      <th>school_score</th>\n",
       "      <th>exterior_walls_Brick</th>\n",
       "      <th>exterior_walls_Brick veneer</th>\n",
       "      <th>exterior_walls_Combination</th>\n",
       "      <th>exterior_walls_Metal</th>\n",
       "      <th>exterior_walls_Missing</th>\n",
       "      <th>exterior_walls_Other</th>\n",
       "      <th>exterior_walls_Siding (Alum/Vinyl)</th>\n",
       "      <th>exterior_walls_Wood</th>\n",
       "      <th>roof_Asphalt</th>\n",
       "      <th>roof_Composition Shingle</th>\n",
       "      <th>roof_Missing</th>\n",
       "      <th>roof_Other</th>\n",
       "      <th>roof_Shake Shingle</th>\n",
       "      <th>property_type_Apartment / Condo / Townhouse</th>\n",
       "      <th>property_type_Single-Family</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>295850</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>584</td>\n",
       "      <td>2013</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>107</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>19</td>\n",
       "      <td>89</td>\n",
       "      <td>6</td>\n",
       "      <td>47</td>\n",
       "      <td>58</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2013</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>216500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>612</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>105</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>87</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "      <td>39.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>279900</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>615</td>\n",
       "      <td>1963</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>183</td>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "      <td>101</td>\n",
       "      <td>10</td>\n",
       "      <td>74</td>\n",
       "      <td>62</td>\n",
       "      <td>28.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>290.0</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>379900</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>618</td>\n",
       "      <td>2000</td>\n",
       "      <td>33541</td>\n",
       "      <td>0.0</td>\n",
       "      <td>198</td>\n",
       "      <td>9</td>\n",
       "      <td>38</td>\n",
       "      <td>25</td>\n",
       "      <td>127</td>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "      <td>83</td>\n",
       "      <td>36.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>340000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>634</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>83</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>73</td>\n",
       "      <td>37.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tx_price  beds  baths  sqft  year_built  lot_size  basement  restaurants  \\\n",
       "0    295850     1      1   584        2013         0       0.0          107   \n",
       "1    216500     1      1   612        1965         0       1.0          105   \n",
       "2    279900     1      1   615        1963         0       0.0          183   \n",
       "3    379900     1      1   618        2000     33541       0.0          198   \n",
       "4    340000     1      1   634        1992         0       0.0          149   \n",
       "\n",
       "   groceries  nightlife  cafes  shopping  arts_entertainment  beauty_spas  \\\n",
       "0          9         30     19        89                   6           47   \n",
       "1         15          6     13        87                   2           26   \n",
       "2         13         31     30       101                  10           74   \n",
       "3          9         38     25       127                  11           72   \n",
       "4          7         22     20        83                  10           50   \n",
       "\n",
       "   active_life  median_age  married  college_grad  property_tax  insurance  \\\n",
       "0           58        33.0     65.0          84.0         234.0       81.0   \n",
       "1           14        39.0     73.0          69.0         169.0       51.0   \n",
       "2           62        28.0     15.0          86.0         216.0       74.0   \n",
       "3           83        36.0     25.0          91.0         265.0       92.0   \n",
       "4           73        37.0     20.0          75.0          88.0       30.0   \n",
       "\n",
       "   median_school  num_schools  tx_year  two_and_two  3-4 bed  older_property  \\\n",
       "0            9.0          3.0     2013            0        0               0   \n",
       "1            3.0          3.0     2006            0        0               1   \n",
       "2            8.0          3.0     2012            0        0               1   \n",
       "3            9.0          3.0     2005            0        0               0   \n",
       "4            9.0          3.0     2002            0        0               0   \n",
       "\n",
       "   tax_and_insurance  during_recession  property_age  school_score  \\\n",
       "0              315.0                 1             0          27.0   \n",
       "1              220.0                 0            41           9.0   \n",
       "2              290.0                 1            49          24.0   \n",
       "3              357.0                 0             5          27.0   \n",
       "4              118.0                 0            10          27.0   \n",
       "\n",
       "   exterior_walls_Brick  exterior_walls_Brick veneer  \\\n",
       "0                     0                            0   \n",
       "1                     1                            0   \n",
       "2                     0                            0   \n",
       "3                     0                            0   \n",
       "4                     1                            0   \n",
       "\n",
       "   exterior_walls_Combination  exterior_walls_Metal  exterior_walls_Missing  \\\n",
       "0                           0                     0                       0   \n",
       "1                           0                     0                       0   \n",
       "2                           0                     0                       0   \n",
       "3                           0                     0                       0   \n",
       "4                           0                     0                       0   \n",
       "\n",
       "   exterior_walls_Other  exterior_walls_Siding (Alum/Vinyl)  \\\n",
       "0                     0                                   0   \n",
       "1                     0                                   0   \n",
       "2                     0                                   0   \n",
       "3                     0                                   0   \n",
       "4                     0                                   0   \n",
       "\n",
       "   exterior_walls_Wood  roof_Asphalt  roof_Composition Shingle  roof_Missing  \\\n",
       "0                    1             0                         0             1   \n",
       "1                    0             0                         1             0   \n",
       "2                    1             0                         0             1   \n",
       "3                    1             0                         0             1   \n",
       "4                    0             0                         0             1   \n",
       "\n",
       "   roof_Other  roof_Shake Shingle  \\\n",
       "0           0                   0   \n",
       "1           0                   0   \n",
       "2           0                   0   \n",
       "3           0                   0   \n",
       "4           0                   0   \n",
       "\n",
       "   property_type_Apartment / Condo / Townhouse  property_type_Single-Family  \n",
       "0                                            1                            0  \n",
       "1                                            1                            0  \n",
       "2                                            1                            0  \n",
       "3                                            1                            0  \n",
       "4                                            1                            0  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.5 Remove unused or redundant features\n",
    "* Unused features are those that don't make sense to pass into our machine learning algorithms.\n",
    "* Example ID columns, Features that wouldn't be available at the time of prediction (e.g. price of the property 5 years after the transaction), Other text descriptions, etc.\n",
    "* For this dataset, we don't have any unused features.\n",
    "* Redundant features would typically be those that have been **replaced by other features** that you've added.\n",
    "* For example, since we used 'tx_year' and 'year_built' to create the 'property_age' feature, we might consider removing them.\n",
    "\n",
    "Sometimes it's not quite black and white. There might be no clear right or wrong decision for this step, and that's OK."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Drop 'tx_year' and 'year_built' from the dataset\n",
    "df = df.drop(['tx_year', 'year_built'], axis=1)  ## axis=1 because we are dropping columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, before we move on to the next module, let's save our new DataFrame we that augmented through feature engineering. We'll call it the analytical base table because we'll be building our models on it.\n",
    "* Remember to set the argument index=None to save only the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save analytical base table\n",
    "df.to_csv('Files/analytical_base_table.csv', index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Tip\n",
    "Not all of the features you engineer need to be winners.\n",
    "\n",
    "* In fact, you'll often find that many of them don't improve your model.\n",
    "* That's OK.\n",
    "* One highly predictive feature makes up for 10 duds.\n",
    "\n",
    "The key is choosing machine learning algorithms that can automatically select the best features among many options (built-in feature selection).\n",
    "\n",
    "This will allow you to avoid overfitting your model despite providing many input features. We'll cover this next."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Machine Learning Models\n",
    "## 6.1 Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1863, 43)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"Files/analytical_base_table.csv\") # Loading the file\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1.1 Train and Test Splits\n",
    "Separate your dataframe into separate objects for the target variable (y) and the input features (X) and perform the train and test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create separate object for target variable\n",
    "y = df.tx_price\n",
    "# Create separate object for input features\n",
    "X = df.drop('tx_price', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Split X and y into train and test sets: 80-20\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Let's confirm we have the right number of observations in each subset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1490, 42) (373, 42) (1490,) (373,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1.2 Data standardization\n",
    "* In Data Standardization we perform zero mean centring and unit scaling; i.e. we make the mean of all the features as zero and the standard deviation as 1.\n",
    "* Thus we use **mean** and **standard deviation** of each feature.\n",
    "* It is very important to save the **mean** and **standard deviation** for each of the feature from the **training set**, because we use the same mean and standard deviation in the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_mean = X_train.mean()\n",
    "train_std = X_train.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Standardize the train data set\n",
    "X_train = (X_train - train_mean) / train_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beds</th>\n",
       "      <th>baths</th>\n",
       "      <th>sqft</th>\n",
       "      <th>lot_size</th>\n",
       "      <th>basement</th>\n",
       "      <th>restaurants</th>\n",
       "      <th>groceries</th>\n",
       "      <th>nightlife</th>\n",
       "      <th>cafes</th>\n",
       "      <th>shopping</th>\n",
       "      <th>...</th>\n",
       "      <th>exterior_walls_Other</th>\n",
       "      <th>exterior_walls_Siding (Alum/Vinyl)</th>\n",
       "      <th>exterior_walls_Wood</th>\n",
       "      <th>roof_Asphalt</th>\n",
       "      <th>roof_Composition Shingle</th>\n",
       "      <th>roof_Missing</th>\n",
       "      <th>roof_Other</th>\n",
       "      <th>roof_Shake Shingle</th>\n",
       "      <th>property_type_Apartment / Condo / Townhouse</th>\n",
       "      <th>property_type_Single-Family</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1689</th>\n",
       "      <td>1.459364</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>1.356266</td>\n",
       "      <td>-0.153472</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.691612</td>\n",
       "      <td>-0.530994</td>\n",
       "      <td>-0.474378</td>\n",
       "      <td>-0.562417</td>\n",
       "      <td>-0.660384</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>1.526966</td>\n",
       "      <td>1.666188</td>\n",
       "      <td>0.422213</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.840593</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.736814</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>-0.404719</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>0.128914</td>\n",
       "      <td>-0.291266</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.542631</td>\n",
       "      <td>-0.753298</td>\n",
       "      <td>-0.474378</td>\n",
       "      <td>-0.562417</td>\n",
       "      <td>-0.488414</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>-1.343434</td>\n",
       "      <td>2.069013</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1740</th>\n",
       "      <td>1.459364</td>\n",
       "      <td>1.526966</td>\n",
       "      <td>0.557562</td>\n",
       "      <td>0.209459</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.116971</td>\n",
       "      <td>-0.530994</td>\n",
       "      <td>0.117899</td>\n",
       "      <td>-0.024978</td>\n",
       "      <td>-0.144475</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>-1.336760</td>\n",
       "      <td>-1.697190</td>\n",
       "      <td>-1.056806</td>\n",
       "      <td>-0.366225</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>1.904914</td>\n",
       "      <td>0.802832</td>\n",
       "      <td>2.013186</td>\n",
       "      <td>2.662216</td>\n",
       "      <td>5.320335</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-2.268801</td>\n",
       "      <td>-1.697190</td>\n",
       "      <td>-1.248773</td>\n",
       "      <td>-0.366225</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>0.904613</td>\n",
       "      <td>0.358223</td>\n",
       "      <td>0.236354</td>\n",
       "      <td>0.378101</td>\n",
       "      <td>0.103926</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>3.767565</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>-0.557231</td>\n",
       "      <td>-0.320255</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.372367</td>\n",
       "      <td>-0.753298</td>\n",
       "      <td>-0.119012</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.431091</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>1.650203</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>-1.336760</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-0.652829</td>\n",
       "      <td>-0.327869</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>0.159708</td>\n",
       "      <td>0.358223</td>\n",
       "      <td>0.117899</td>\n",
       "      <td>-0.159338</td>\n",
       "      <td>0.065710</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>1.650203</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1667</th>\n",
       "      <td>1.459364</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>1.037093</td>\n",
       "      <td>0.347138</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.798027</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.474378</td>\n",
       "      <td>-0.562417</td>\n",
       "      <td>-0.679491</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>3.767565</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>-0.404719</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>-0.335197</td>\n",
       "      <td>-0.318503</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>0.925896</td>\n",
       "      <td>0.802832</td>\n",
       "      <td>-0.119012</td>\n",
       "      <td>0.512461</td>\n",
       "      <td>2.702576</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>-0.404719</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-0.970460</td>\n",
       "      <td>-0.366225</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.159537</td>\n",
       "      <td>-0.308690</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>0.243741</td>\n",
       "      <td>0.887342</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>1.526966</td>\n",
       "      <td>1.293819</td>\n",
       "      <td>-0.053344</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.202103</td>\n",
       "      <td>-0.308690</td>\n",
       "      <td>-0.474378</td>\n",
       "      <td>-0.428057</td>\n",
       "      <td>-0.450199</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>3.558261</td>\n",
       "      <td>-1.343434</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>-0.404719</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>0.096534</td>\n",
       "      <td>-0.222570</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>1.074877</td>\n",
       "      <td>0.135919</td>\n",
       "      <td>0.947087</td>\n",
       "      <td>2.124777</td>\n",
       "      <td>0.600726</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>3.558261</td>\n",
       "      <td>-1.343434</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>-0.404719</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-1.113086</td>\n",
       "      <td>-0.078943</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.287235</td>\n",
       "      <td>1.247440</td>\n",
       "      <td>-0.355923</td>\n",
       "      <td>-0.428057</td>\n",
       "      <td>-0.220906</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>1.650203</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802</th>\n",
       "      <td>1.459364</td>\n",
       "      <td>1.526966</td>\n",
       "      <td>2.111026</td>\n",
       "      <td>2.036697</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.819310</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.717707</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>-1.343434</td>\n",
       "      <td>2.069013</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>1.459364</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>0.818143</td>\n",
       "      <td>1.310807</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.819310</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.679491</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1858</th>\n",
       "      <td>1.459364</td>\n",
       "      <td>2.601685</td>\n",
       "      <td>4.255036</td>\n",
       "      <td>12.161520</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.840593</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.755922</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>-1.343434</td>\n",
       "      <td>2.069013</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1217</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>-0.403041</td>\n",
       "      <td>-0.305918</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.840593</td>\n",
       "      <td>-0.753298</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.622168</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>1.650203</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587</th>\n",
       "      <td>1.459364</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>0.420333</td>\n",
       "      <td>-0.116179</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.840593</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.717707</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>1.650203</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1670</th>\n",
       "      <td>1.459364</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>1.091059</td>\n",
       "      <td>0.259537</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.457499</td>\n",
       "      <td>1.914353</td>\n",
       "      <td>-0.474378</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.087152</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>1.650203</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>0.136624</td>\n",
       "      <td>1.536087</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.840593</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.755922</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-0.162505</td>\n",
       "      <td>0.109331</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.585197</td>\n",
       "      <td>-0.530994</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.545737</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>-1.336760</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>-0.505577</td>\n",
       "      <td>-0.366225</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.393650</td>\n",
       "      <td>-0.086385</td>\n",
       "      <td>-0.474378</td>\n",
       "      <td>-0.293698</td>\n",
       "      <td>-0.431091</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>3.767565</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>-0.404719</td>\n",
       "      <td>-1.697190</td>\n",
       "      <td>-0.736091</td>\n",
       "      <td>-0.327725</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.457499</td>\n",
       "      <td>-0.530994</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.316444</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>1.650203</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>-2.268801</td>\n",
       "      <td>-1.697190</td>\n",
       "      <td>-1.113086</td>\n",
       "      <td>-0.366225</td>\n",
       "      <td>-2.688343</td>\n",
       "      <td>3.373441</td>\n",
       "      <td>1.025136</td>\n",
       "      <td>3.908472</td>\n",
       "      <td>2.662216</td>\n",
       "      <td>1.651651</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>3.767565</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>-1.343434</td>\n",
       "      <td>2.069013</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1541</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>1.526966</td>\n",
       "      <td>1.962232</td>\n",
       "      <td>0.034257</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.840593</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.660384</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>1.650203</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1635</th>\n",
       "      <td>1.459364</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>0.492031</td>\n",
       "      <td>-0.003294</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.755461</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.474378</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.603060</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-0.516371</td>\n",
       "      <td>0.134385</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.819310</td>\n",
       "      <td>-0.753298</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.679491</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690</th>\n",
       "      <td>1.459364</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>1.363975</td>\n",
       "      <td>0.009232</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.840593</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.755922</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>1.650203</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>-1.336760</td>\n",
       "      <td>-1.697190</td>\n",
       "      <td>-1.013633</td>\n",
       "      <td>-0.308907</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.436216</td>\n",
       "      <td>-0.753298</td>\n",
       "      <td>-0.237467</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.603060</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>1.650203</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1776</th>\n",
       "      <td>1.459364</td>\n",
       "      <td>1.526966</td>\n",
       "      <td>1.572132</td>\n",
       "      <td>0.009232</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.414933</td>\n",
       "      <td>-0.753298</td>\n",
       "      <td>-0.000557</td>\n",
       "      <td>-0.428057</td>\n",
       "      <td>-0.603060</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1586</th>\n",
       "      <td>1.459364</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>0.409540</td>\n",
       "      <td>0.121858</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.840593</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.736814</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>1.524333</td>\n",
       "      <td>-0.065009</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.798027</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.717707</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>1.650203</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>-1.343434</td>\n",
       "      <td>2.069013</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>-0.404719</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-0.625075</td>\n",
       "      <td>-0.291151</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>0.053293</td>\n",
       "      <td>-0.086385</td>\n",
       "      <td>-0.474378</td>\n",
       "      <td>0.243741</td>\n",
       "      <td>-0.087152</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>-1.336760</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-0.902616</td>\n",
       "      <td>-0.366225</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.095688</td>\n",
       "      <td>-0.308690</td>\n",
       "      <td>-0.237467</td>\n",
       "      <td>-0.562417</td>\n",
       "      <td>-0.373768</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>-0.404719</td>\n",
       "      <td>-1.697190</td>\n",
       "      <td>-0.708337</td>\n",
       "      <td>-0.028319</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.776744</td>\n",
       "      <td>-0.753298</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.469306</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-0.988963</td>\n",
       "      <td>-0.078886</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.287235</td>\n",
       "      <td>1.247440</td>\n",
       "      <td>-0.474378</td>\n",
       "      <td>-0.428057</td>\n",
       "      <td>-0.297337</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>1.650203</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>-1.343434</td>\n",
       "      <td>2.069013</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1360</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>0.539059</td>\n",
       "      <td>-0.222570</td>\n",
       "      <td>-2.688343</td>\n",
       "      <td>0.244840</td>\n",
       "      <td>0.580527</td>\n",
       "      <td>-0.237467</td>\n",
       "      <td>-0.024978</td>\n",
       "      <td>0.256787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>0.478925</td>\n",
       "      <td>3.688704</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.840593</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.755922</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>1.650203</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>3.558261</td>\n",
       "      <td>-1.343434</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1500</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>1.526966</td>\n",
       "      <td>0.737964</td>\n",
       "      <td>-0.156230</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>0.500236</td>\n",
       "      <td>0.580527</td>\n",
       "      <td>0.473265</td>\n",
       "      <td>0.512461</td>\n",
       "      <td>-0.029829</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>1.650203</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>-1.343434</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>3.942729</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>-1.336760</td>\n",
       "      <td>-1.697190</td>\n",
       "      <td>-1.050639</td>\n",
       "      <td>-0.366225</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>0.691783</td>\n",
       "      <td>1.025136</td>\n",
       "      <td>0.354810</td>\n",
       "      <td>0.512461</td>\n",
       "      <td>1.154850</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>-0.404719</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-0.800080</td>\n",
       "      <td>-0.028319</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.712895</td>\n",
       "      <td>-0.753298</td>\n",
       "      <td>-0.474378</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.335552</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1398</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>0.857462</td>\n",
       "      <td>0.071780</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.798027</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.736814</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1818</th>\n",
       "      <td>1.459364</td>\n",
       "      <td>1.526966</td>\n",
       "      <td>2.687696</td>\n",
       "      <td>0.497316</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.755461</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.474378</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.679491</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>3.558261</td>\n",
       "      <td>-1.343434</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1772</th>\n",
       "      <td>1.459364</td>\n",
       "      <td>1.526966</td>\n",
       "      <td>1.474221</td>\n",
       "      <td>1.310807</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.819310</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.428057</td>\n",
       "      <td>-0.736814</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>3.558261</td>\n",
       "      <td>-1.343434</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1182</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>-0.637410</td>\n",
       "      <td>-0.321663</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>0.415104</td>\n",
       "      <td>-0.086385</td>\n",
       "      <td>0.473265</td>\n",
       "      <td>-0.024978</td>\n",
       "      <td>0.715373</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>-1.343434</td>\n",
       "      <td>2.069013</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707</th>\n",
       "      <td>1.459364</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>1.740970</td>\n",
       "      <td>0.034257</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.840593</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.717707</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>-2.268801</td>\n",
       "      <td>-1.697190</td>\n",
       "      <td>-1.233354</td>\n",
       "      <td>-0.366225</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>1.330273</td>\n",
       "      <td>0.802832</td>\n",
       "      <td>0.354810</td>\n",
       "      <td>0.781180</td>\n",
       "      <td>1.212174</td>\n",
       "      <td>...</td>\n",
       "      <td>5.058652</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>-1.343434</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>3.942729</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1178</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>-0.660538</td>\n",
       "      <td>0.021730</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.691612</td>\n",
       "      <td>-0.753298</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.293698</td>\n",
       "      <td>-0.564845</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>-0.039153</td>\n",
       "      <td>0.009232</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.053122</td>\n",
       "      <td>-0.086385</td>\n",
       "      <td>-0.000557</td>\n",
       "      <td>0.109381</td>\n",
       "      <td>0.466972</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>-1.336760</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-0.850192</td>\n",
       "      <td>-0.366225</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.500065</td>\n",
       "      <td>-0.753298</td>\n",
       "      <td>-0.474378</td>\n",
       "      <td>-0.293698</td>\n",
       "      <td>-0.660384</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>1.650203</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>-0.404719</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>0.445003</td>\n",
       "      <td>-0.266126</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.351084</td>\n",
       "      <td>-0.308690</td>\n",
       "      <td>-0.474378</td>\n",
       "      <td>0.109381</td>\n",
       "      <td>-0.335552</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>1.459364</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>1.208244</td>\n",
       "      <td>0.209459</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.627763</td>\n",
       "      <td>-0.308690</td>\n",
       "      <td>-0.355923</td>\n",
       "      <td>-0.562417</td>\n",
       "      <td>-0.431091</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>1.650203</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>3.558261</td>\n",
       "      <td>-1.343434</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>-0.404719</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>0.084199</td>\n",
       "      <td>-0.298994</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.053122</td>\n",
       "      <td>0.135919</td>\n",
       "      <td>-0.119012</td>\n",
       "      <td>-0.159338</td>\n",
       "      <td>-0.029829</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1396</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>0.846668</td>\n",
       "      <td>-0.053976</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.095688</td>\n",
       "      <td>0.135919</td>\n",
       "      <td>-0.237467</td>\n",
       "      <td>-0.159338</td>\n",
       "      <td>-0.144475</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>-0.302818</td>\n",
       "      <td>-0.063630</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.606480</td>\n",
       "      <td>-0.753298</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.562417</td>\n",
       "      <td>0.103926</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-0.032985</td>\n",
       "      <td>-0.282646</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.798027</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.603060</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>-1.343434</td>\n",
       "      <td>2.069013</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>0.250724</td>\n",
       "      <td>-0.061503</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.840593</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.474378</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.717707</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>-0.404719</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>-0.742259</td>\n",
       "      <td>-0.312211</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.202103</td>\n",
       "      <td>0.580527</td>\n",
       "      <td>-0.474378</td>\n",
       "      <td>-0.024978</td>\n",
       "      <td>-0.240014</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>-1.343434</td>\n",
       "      <td>2.069013</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.84974</td>\n",
       "      <td>0.84974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>-0.404719</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>-0.372203</td>\n",
       "      <td>-0.310171</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>0.606651</td>\n",
       "      <td>1.469744</td>\n",
       "      <td>-0.119012</td>\n",
       "      <td>-0.024978</td>\n",
       "      <td>0.237680</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>1.650203</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.17604</td>\n",
       "      <td>-1.17604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1490 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          beds     baths      sqft   lot_size  basement  restaurants  \\\n",
       "1689  1.459364  0.452247  1.356266  -0.153472  0.371727    -0.691612   \n",
       "1531  0.527323  1.526966  1.666188   0.422213  0.371727    -0.840593   \n",
       "668  -0.404719 -0.622471  0.128914  -0.291266  0.371727    -0.542631   \n",
       "1740  1.459364  1.526966  0.557562   0.209459  0.371727    -0.116971   \n",
       "117  -1.336760 -1.697190 -1.056806  -0.366225  0.371727     1.904914   \n",
       "22   -2.268801 -1.697190 -1.248773  -0.366225  0.371727     0.904613   \n",
       "1196  0.527323  0.452247 -0.557231  -0.320255  0.371727    -0.372367   \n",
       "319  -1.336760 -0.622471 -0.652829  -0.327869  0.371727     0.159708   \n",
       "1667  1.459364  0.452247  1.037093   0.347138  0.371727    -0.798027   \n",
       "821  -0.404719  0.452247 -0.335197  -0.318503  0.371727     0.925896   \n",
       "406  -0.404719 -0.622471 -0.970460  -0.366225  0.371727    -0.159537   \n",
       "1515  0.527323  1.526966  1.293819  -0.053344  0.371727    -0.202103   \n",
       "879  -0.404719  0.452247  0.096534  -0.222570  0.371727     1.074877   \n",
       "395  -0.404719 -0.622471 -1.113086  -0.078943  0.371727    -0.287235   \n",
       "1802  1.459364  1.526966  2.111026   2.036697  0.371727    -0.819310   \n",
       "1653  1.459364  0.452247  0.818143   1.310807  0.371727    -0.819310   \n",
       "1858  1.459364  2.601685  4.255036  12.161520  0.371727    -0.840593   \n",
       "1217  0.527323  0.452247 -0.403041  -0.305918  0.371727    -0.840593   \n",
       "1587  1.459364 -0.622471  0.420333  -0.116179  0.371727    -0.840593   \n",
       "1670  1.459364  0.452247  1.091059   0.259537  0.371727    -0.457499   \n",
       "1295  0.527323  0.452247  0.136624   1.536087  0.371727    -0.840593   \n",
       "1066  0.527323 -0.622471 -0.162505   0.109331  0.371727    -0.585197   \n",
       "347  -1.336760  0.452247 -0.505577  -0.366225  0.371727    -0.393650   \n",
       "381  -0.404719 -1.697190 -0.736091  -0.327725  0.371727    -0.457499   \n",
       "66   -2.268801 -1.697190 -1.113086  -0.366225 -2.688343     3.373441   \n",
       "1541  0.527323  1.526966  1.962232   0.034257  0.371727    -0.840593   \n",
       "1635  1.459364  0.452247  0.492031  -0.003294  0.371727    -0.755461   \n",
       "1016  0.527323 -0.622471 -0.516371   0.134385  0.371727    -0.819310   \n",
       "1690  1.459364  0.452247  1.363975   0.009232  0.371727    -0.840593   \n",
       "130  -1.336760 -1.697190 -1.013633  -0.308907  0.371727    -0.436216   \n",
       "...        ...       ...       ...        ...       ...          ...   \n",
       "1776  1.459364  1.526966  1.572132   0.009232  0.371727    -0.414933   \n",
       "1586  1.459364 -0.622471  0.409540   0.121858  0.371727    -0.840593   \n",
       "1455  0.527323  0.452247  1.524333  -0.065009  0.371727    -0.798027   \n",
       "510  -0.404719 -0.622471 -0.625075  -0.291151  0.371727     0.053293   \n",
       "246  -1.336760 -0.622471 -0.902616  -0.366225  0.371727    -0.095688   \n",
       "383  -0.404719 -1.697190 -0.708337  -0.028319  0.371727    -0.776744   \n",
       "969   0.527323 -0.622471 -0.988963  -0.078886  0.371727    -0.287235   \n",
       "1360  0.527323  0.452247  0.539059  -0.222570 -2.688343     0.244840   \n",
       "1349  0.527323  0.452247  0.478925   3.688704  0.371727    -0.840593   \n",
       "1500  0.527323  1.526966  0.737964  -0.156230  0.371727     0.500236   \n",
       "120  -1.336760 -1.697190 -1.050639  -0.366225  0.371727     0.691783   \n",
       "442  -0.404719 -0.622471 -0.800080  -0.028319  0.371727    -0.712895   \n",
       "1398  0.527323  0.452247  0.857462   0.071780  0.371727    -0.798027   \n",
       "1818  1.459364  1.526966  2.687696   0.497316  0.371727    -0.755461   \n",
       "1772  1.459364  1.526966  1.474221   1.310807  0.371727    -0.819310   \n",
       "1182  0.527323  0.452247 -0.637410  -0.321663  0.371727     0.415104   \n",
       "1707  1.459364  0.452247  1.740970   0.034257  0.371727    -0.840593   \n",
       "30   -2.268801 -1.697190 -1.233354  -0.366225  0.371727     1.330273   \n",
       "1178  0.527323  0.452247 -0.660538   0.021730  0.371727    -0.691612   \n",
       "1257  0.527323  0.452247 -0.039153   0.009232  0.371727    -0.053122   \n",
       "279  -1.336760 -0.622471 -0.850192  -0.366225  0.371727    -0.500065   \n",
       "689  -0.404719 -0.622471  0.445003  -0.266126  0.371727    -0.351084   \n",
       "1679  1.459364  0.452247  1.208244   0.209459  0.371727    -0.627763   \n",
       "664  -0.404719 -0.622471  0.084199  -0.298994  0.371727    -0.053122   \n",
       "1396  0.527323  0.452247  0.846668  -0.053976  0.371727    -0.095688   \n",
       "1228  0.527323  0.452247 -0.302818  -0.063630  0.371727    -0.606480   \n",
       "1077  0.527323 -0.622471 -0.032985  -0.282646  0.371727    -0.798027   \n",
       "1318  0.527323  0.452247  0.250724  -0.061503  0.371727    -0.840593   \n",
       "723  -0.404719  0.452247 -0.742259  -0.312211  0.371727    -0.202103   \n",
       "815  -0.404719  0.452247 -0.372203  -0.310171  0.371727     0.606651   \n",
       "\n",
       "      groceries  nightlife     cafes  shopping             ...               \\\n",
       "1689  -0.530994  -0.474378 -0.562417 -0.660384             ...                \n",
       "1531  -0.975602  -0.592834 -0.696777 -0.736814             ...                \n",
       "668   -0.753298  -0.474378 -0.562417 -0.488414             ...                \n",
       "1740  -0.530994   0.117899 -0.024978 -0.144475             ...                \n",
       "117    0.802832   2.013186  2.662216  5.320335             ...                \n",
       "22     0.358223   0.236354  0.378101  0.103926             ...                \n",
       "1196  -0.753298  -0.119012 -0.696777 -0.431091             ...                \n",
       "319    0.358223   0.117899 -0.159338  0.065710             ...                \n",
       "1667  -0.975602  -0.474378 -0.562417 -0.679491             ...                \n",
       "821    0.802832  -0.119012  0.512461  2.702576             ...                \n",
       "406   -0.308690  -0.592834  0.243741  0.887342             ...                \n",
       "1515  -0.308690  -0.474378 -0.428057 -0.450199             ...                \n",
       "879    0.135919   0.947087  2.124777  0.600726             ...                \n",
       "395    1.247440  -0.355923 -0.428057 -0.220906             ...                \n",
       "1802  -0.975602  -0.592834 -0.696777 -0.717707             ...                \n",
       "1653  -0.975602  -0.592834 -0.696777 -0.679491             ...                \n",
       "1858  -0.975602  -0.592834 -0.696777 -0.755922             ...                \n",
       "1217  -0.753298  -0.592834 -0.696777 -0.622168             ...                \n",
       "1587  -0.975602  -0.592834 -0.696777 -0.717707             ...                \n",
       "1670   1.914353  -0.474378 -0.696777 -0.087152             ...                \n",
       "1295  -0.975602  -0.592834 -0.696777 -0.755922             ...                \n",
       "1066  -0.530994  -0.592834 -0.696777 -0.545737             ...                \n",
       "347   -0.086385  -0.474378 -0.293698 -0.431091             ...                \n",
       "381   -0.530994  -0.592834 -0.696777 -0.316444             ...                \n",
       "66     1.025136   3.908472  2.662216  1.651651             ...                \n",
       "1541  -0.975602  -0.592834 -0.696777 -0.660384             ...                \n",
       "1635  -0.975602  -0.474378 -0.696777 -0.603060             ...                \n",
       "1016  -0.753298  -0.592834 -0.696777 -0.679491             ...                \n",
       "1690  -0.975602  -0.592834 -0.696777 -0.755922             ...                \n",
       "130   -0.753298  -0.237467 -0.696777 -0.603060             ...                \n",
       "...         ...        ...       ...       ...             ...                \n",
       "1776  -0.753298  -0.000557 -0.428057 -0.603060             ...                \n",
       "1586  -0.975602  -0.592834 -0.696777 -0.736814             ...                \n",
       "1455  -0.975602  -0.592834 -0.696777 -0.717707             ...                \n",
       "510   -0.086385  -0.474378  0.243741 -0.087152             ...                \n",
       "246   -0.308690  -0.237467 -0.562417 -0.373768             ...                \n",
       "383   -0.753298  -0.592834 -0.696777 -0.469306             ...                \n",
       "969    1.247440  -0.474378 -0.428057 -0.297337             ...                \n",
       "1360   0.580527  -0.237467 -0.024978  0.256787             ...                \n",
       "1349  -0.975602  -0.592834 -0.696777 -0.755922             ...                \n",
       "1500   0.580527   0.473265  0.512461 -0.029829             ...                \n",
       "120    1.025136   0.354810  0.512461  1.154850             ...                \n",
       "442   -0.753298  -0.474378 -0.696777 -0.335552             ...                \n",
       "1398  -0.975602  -0.592834 -0.696777 -0.736814             ...                \n",
       "1818  -0.975602  -0.474378 -0.696777 -0.679491             ...                \n",
       "1772  -0.975602  -0.592834 -0.428057 -0.736814             ...                \n",
       "1182  -0.086385   0.473265 -0.024978  0.715373             ...                \n",
       "1707  -0.975602  -0.592834 -0.696777 -0.717707             ...                \n",
       "30     0.802832   0.354810  0.781180  1.212174             ...                \n",
       "1178  -0.753298  -0.592834 -0.293698 -0.564845             ...                \n",
       "1257  -0.086385  -0.000557  0.109381  0.466972             ...                \n",
       "279   -0.753298  -0.474378 -0.293698 -0.660384             ...                \n",
       "689   -0.308690  -0.474378  0.109381 -0.335552             ...                \n",
       "1679  -0.308690  -0.355923 -0.562417 -0.431091             ...                \n",
       "664    0.135919  -0.119012 -0.159338 -0.029829             ...                \n",
       "1396   0.135919  -0.237467 -0.159338 -0.144475             ...                \n",
       "1228  -0.753298  -0.592834 -0.562417  0.103926             ...                \n",
       "1077  -0.975602  -0.592834 -0.696777 -0.603060             ...                \n",
       "1318  -0.975602  -0.474378 -0.696777 -0.717707             ...                \n",
       "723    0.580527  -0.474378 -0.024978 -0.240014             ...                \n",
       "815    1.469744  -0.119012 -0.024978  0.237680             ...                \n",
       "\n",
       "      exterior_walls_Other  exterior_walls_Siding (Alum/Vinyl)  \\\n",
       "1689             -0.197548                           -0.605579   \n",
       "1531             -0.197548                           -0.605579   \n",
       "668              -0.197548                           -0.605579   \n",
       "1740             -0.197548                           -0.605579   \n",
       "117              -0.197548                           -0.605579   \n",
       "22               -0.197548                           -0.605579   \n",
       "1196             -0.197548                            1.650203   \n",
       "319              -0.197548                            1.650203   \n",
       "1667             -0.197548                           -0.605579   \n",
       "821              -0.197548                           -0.605579   \n",
       "406              -0.197548                           -0.605579   \n",
       "1515             -0.197548                           -0.605579   \n",
       "879              -0.197548                           -0.605579   \n",
       "395              -0.197548                            1.650203   \n",
       "1802             -0.197548                           -0.605579   \n",
       "1653             -0.197548                           -0.605579   \n",
       "1858             -0.197548                           -0.605579   \n",
       "1217             -0.197548                            1.650203   \n",
       "1587             -0.197548                            1.650203   \n",
       "1670             -0.197548                            1.650203   \n",
       "1295             -0.197548                           -0.605579   \n",
       "1066             -0.197548                           -0.605579   \n",
       "347              -0.197548                           -0.605579   \n",
       "381              -0.197548                            1.650203   \n",
       "66               -0.197548                           -0.605579   \n",
       "1541             -0.197548                            1.650203   \n",
       "1635             -0.197548                           -0.605579   \n",
       "1016             -0.197548                           -0.605579   \n",
       "1690             -0.197548                            1.650203   \n",
       "130              -0.197548                            1.650203   \n",
       "...                    ...                                 ...   \n",
       "1776             -0.197548                           -0.605579   \n",
       "1586             -0.197548                           -0.605579   \n",
       "1455             -0.197548                            1.650203   \n",
       "510              -0.197548                           -0.605579   \n",
       "246              -0.197548                           -0.605579   \n",
       "383              -0.197548                           -0.605579   \n",
       "969              -0.197548                            1.650203   \n",
       "1360             -0.197548                           -0.605579   \n",
       "1349             -0.197548                            1.650203   \n",
       "1500             -0.197548                            1.650203   \n",
       "120              -0.197548                           -0.605579   \n",
       "442              -0.197548                           -0.605579   \n",
       "1398             -0.197548                           -0.605579   \n",
       "1818             -0.197548                           -0.605579   \n",
       "1772             -0.197548                           -0.605579   \n",
       "1182             -0.197548                           -0.605579   \n",
       "1707             -0.197548                           -0.605579   \n",
       "30                5.058652                           -0.605579   \n",
       "1178             -0.197548                           -0.605579   \n",
       "1257             -0.197548                           -0.605579   \n",
       "279              -0.197548                            1.650203   \n",
       "689              -0.197548                           -0.605579   \n",
       "1679             -0.197548                            1.650203   \n",
       "664              -0.197548                           -0.605579   \n",
       "1396             -0.197548                           -0.605579   \n",
       "1228             -0.197548                           -0.605579   \n",
       "1077             -0.197548                           -0.605579   \n",
       "1318             -0.197548                           -0.605579   \n",
       "723              -0.197548                           -0.605579   \n",
       "815              -0.197548                            1.650203   \n",
       "\n",
       "      exterior_walls_Wood  roof_Asphalt  roof_Composition Shingle  \\\n",
       "1689            -0.265245     -0.280848                  0.743862   \n",
       "1531            -0.265245     -0.280848                  0.743862   \n",
       "668             -0.265245     -0.280848                 -1.343434   \n",
       "1740            -0.265245     -0.280848                  0.743862   \n",
       "117             -0.265245     -0.280848                  0.743862   \n",
       "22               3.767565     -0.280848                  0.743862   \n",
       "1196            -0.265245     -0.280848                  0.743862   \n",
       "319             -0.265245     -0.280848                  0.743862   \n",
       "1667             3.767565     -0.280848                  0.743862   \n",
       "821             -0.265245     -0.280848                  0.743862   \n",
       "406             -0.265245     -0.280848                  0.743862   \n",
       "1515            -0.265245      3.558261                 -1.343434   \n",
       "879             -0.265245      3.558261                 -1.343434   \n",
       "395             -0.265245     -0.280848                  0.743862   \n",
       "1802            -0.265245     -0.280848                 -1.343434   \n",
       "1653            -0.265245     -0.280848                  0.743862   \n",
       "1858            -0.265245     -0.280848                 -1.343434   \n",
       "1217            -0.265245     -0.280848                  0.743862   \n",
       "1587            -0.265245     -0.280848                  0.743862   \n",
       "1670            -0.265245     -0.280848                  0.743862   \n",
       "1295            -0.265245     -0.280848                  0.743862   \n",
       "1066            -0.265245     -0.280848                  0.743862   \n",
       "347              3.767565     -0.280848                  0.743862   \n",
       "381             -0.265245     -0.280848                  0.743862   \n",
       "66               3.767565     -0.280848                 -1.343434   \n",
       "1541            -0.265245     -0.280848                  0.743862   \n",
       "1635            -0.265245     -0.280848                  0.743862   \n",
       "1016            -0.265245     -0.280848                  0.743862   \n",
       "1690            -0.265245     -0.280848                  0.743862   \n",
       "130             -0.265245     -0.280848                  0.743862   \n",
       "...                   ...           ...                       ...   \n",
       "1776            -0.265245     -0.280848                  0.743862   \n",
       "1586            -0.265245     -0.280848                  0.743862   \n",
       "1455            -0.265245     -0.280848                 -1.343434   \n",
       "510             -0.265245     -0.280848                  0.743862   \n",
       "246             -0.265245     -0.280848                  0.743862   \n",
       "383             -0.265245     -0.280848                  0.743862   \n",
       "969             -0.265245     -0.280848                 -1.343434   \n",
       "1360            -0.265245     -0.280848                  0.743862   \n",
       "1349            -0.265245      3.558261                 -1.343434   \n",
       "1500            -0.265245     -0.280848                 -1.343434   \n",
       "120             -0.265245     -0.280848                  0.743862   \n",
       "442             -0.265245     -0.280848                  0.743862   \n",
       "1398            -0.265245     -0.280848                  0.743862   \n",
       "1818            -0.265245      3.558261                 -1.343434   \n",
       "1772            -0.265245      3.558261                 -1.343434   \n",
       "1182            -0.265245     -0.280848                 -1.343434   \n",
       "1707            -0.265245     -0.280848                  0.743862   \n",
       "30              -0.265245     -0.280848                 -1.343434   \n",
       "1178            -0.265245     -0.280848                  0.743862   \n",
       "1257            -0.265245     -0.280848                  0.743862   \n",
       "279             -0.265245     -0.280848                  0.743862   \n",
       "689             -0.265245     -0.280848                  0.743862   \n",
       "1679            -0.265245      3.558261                 -1.343434   \n",
       "664             -0.265245     -0.280848                  0.743862   \n",
       "1396            -0.265245     -0.280848                  0.743862   \n",
       "1228            -0.265245     -0.280848                  0.743862   \n",
       "1077            -0.265245     -0.280848                 -1.343434   \n",
       "1318            -0.265245     -0.280848                  0.743862   \n",
       "723             -0.265245     -0.280848                 -1.343434   \n",
       "815             -0.265245     -0.280848                  0.743862   \n",
       "\n",
       "      roof_Missing  roof_Other  roof_Shake Shingle  \\\n",
       "1689     -0.482998   -0.253461           -0.186276   \n",
       "1531     -0.482998   -0.253461           -0.186276   \n",
       "668       2.069013   -0.253461           -0.186276   \n",
       "1740     -0.482998   -0.253461           -0.186276   \n",
       "117      -0.482998   -0.253461           -0.186276   \n",
       "22       -0.482998   -0.253461           -0.186276   \n",
       "1196     -0.482998   -0.253461           -0.186276   \n",
       "319      -0.482998   -0.253461           -0.186276   \n",
       "1667     -0.482998   -0.253461           -0.186276   \n",
       "821      -0.482998   -0.253461           -0.186276   \n",
       "406      -0.482998   -0.253461           -0.186276   \n",
       "1515     -0.482998   -0.253461           -0.186276   \n",
       "879      -0.482998   -0.253461           -0.186276   \n",
       "395      -0.482998   -0.253461           -0.186276   \n",
       "1802      2.069013   -0.253461           -0.186276   \n",
       "1653     -0.482998   -0.253461           -0.186276   \n",
       "1858      2.069013   -0.253461           -0.186276   \n",
       "1217     -0.482998   -0.253461           -0.186276   \n",
       "1587     -0.482998   -0.253461           -0.186276   \n",
       "1670     -0.482998   -0.253461           -0.186276   \n",
       "1295     -0.482998   -0.253461           -0.186276   \n",
       "1066     -0.482998   -0.253461           -0.186276   \n",
       "347      -0.482998   -0.253461           -0.186276   \n",
       "381      -0.482998   -0.253461           -0.186276   \n",
       "66        2.069013   -0.253461           -0.186276   \n",
       "1541     -0.482998   -0.253461           -0.186276   \n",
       "1635     -0.482998   -0.253461           -0.186276   \n",
       "1016     -0.482998   -0.253461           -0.186276   \n",
       "1690     -0.482998   -0.253461           -0.186276   \n",
       "130      -0.482998   -0.253461           -0.186276   \n",
       "...            ...         ...                 ...   \n",
       "1776     -0.482998   -0.253461           -0.186276   \n",
       "1586     -0.482998   -0.253461           -0.186276   \n",
       "1455      2.069013   -0.253461           -0.186276   \n",
       "510      -0.482998   -0.253461           -0.186276   \n",
       "246      -0.482998   -0.253461           -0.186276   \n",
       "383      -0.482998   -0.253461           -0.186276   \n",
       "969       2.069013   -0.253461           -0.186276   \n",
       "1360     -0.482998   -0.253461           -0.186276   \n",
       "1349     -0.482998   -0.253461           -0.186276   \n",
       "1500     -0.482998    3.942729           -0.186276   \n",
       "120      -0.482998   -0.253461           -0.186276   \n",
       "442      -0.482998   -0.253461           -0.186276   \n",
       "1398     -0.482998   -0.253461           -0.186276   \n",
       "1818     -0.482998   -0.253461           -0.186276   \n",
       "1772     -0.482998   -0.253461           -0.186276   \n",
       "1182      2.069013   -0.253461           -0.186276   \n",
       "1707     -0.482998   -0.253461           -0.186276   \n",
       "30       -0.482998    3.942729           -0.186276   \n",
       "1178     -0.482998   -0.253461           -0.186276   \n",
       "1257     -0.482998   -0.253461           -0.186276   \n",
       "279      -0.482998   -0.253461           -0.186276   \n",
       "689      -0.482998   -0.253461           -0.186276   \n",
       "1679     -0.482998   -0.253461           -0.186276   \n",
       "664      -0.482998   -0.253461           -0.186276   \n",
       "1396     -0.482998   -0.253461           -0.186276   \n",
       "1228     -0.482998   -0.253461           -0.186276   \n",
       "1077      2.069013   -0.253461           -0.186276   \n",
       "1318     -0.482998   -0.253461           -0.186276   \n",
       "723       2.069013   -0.253461           -0.186276   \n",
       "815      -0.482998   -0.253461           -0.186276   \n",
       "\n",
       "      property_type_Apartment / Condo / Townhouse  property_type_Single-Family  \n",
       "1689                                     -0.84974                      0.84974  \n",
       "1531                                     -0.84974                      0.84974  \n",
       "668                                       1.17604                     -1.17604  \n",
       "1740                                     -0.84974                      0.84974  \n",
       "117                                       1.17604                     -1.17604  \n",
       "22                                        1.17604                     -1.17604  \n",
       "1196                                      1.17604                     -1.17604  \n",
       "319                                       1.17604                     -1.17604  \n",
       "1667                                     -0.84974                      0.84974  \n",
       "821                                       1.17604                     -1.17604  \n",
       "406                                       1.17604                     -1.17604  \n",
       "1515                                     -0.84974                      0.84974  \n",
       "879                                      -0.84974                      0.84974  \n",
       "395                                      -0.84974                      0.84974  \n",
       "1802                                     -0.84974                      0.84974  \n",
       "1653                                     -0.84974                      0.84974  \n",
       "1858                                     -0.84974                      0.84974  \n",
       "1217                                      1.17604                     -1.17604  \n",
       "1587                                     -0.84974                      0.84974  \n",
       "1670                                     -0.84974                      0.84974  \n",
       "1295                                     -0.84974                      0.84974  \n",
       "1066                                     -0.84974                      0.84974  \n",
       "347                                       1.17604                     -1.17604  \n",
       "381                                      -0.84974                      0.84974  \n",
       "66                                        1.17604                     -1.17604  \n",
       "1541                                     -0.84974                      0.84974  \n",
       "1635                                     -0.84974                      0.84974  \n",
       "1016                                     -0.84974                      0.84974  \n",
       "1690                                     -0.84974                      0.84974  \n",
       "130                                       1.17604                     -1.17604  \n",
       "...                                           ...                          ...  \n",
       "1776                                     -0.84974                      0.84974  \n",
       "1586                                     -0.84974                      0.84974  \n",
       "1455                                     -0.84974                      0.84974  \n",
       "510                                       1.17604                     -1.17604  \n",
       "246                                       1.17604                     -1.17604  \n",
       "383                                      -0.84974                      0.84974  \n",
       "969                                      -0.84974                      0.84974  \n",
       "1360                                     -0.84974                      0.84974  \n",
       "1349                                     -0.84974                      0.84974  \n",
       "1500                                     -0.84974                      0.84974  \n",
       "120                                       1.17604                     -1.17604  \n",
       "442                                      -0.84974                      0.84974  \n",
       "1398                                     -0.84974                      0.84974  \n",
       "1818                                     -0.84974                      0.84974  \n",
       "1772                                     -0.84974                      0.84974  \n",
       "1182                                      1.17604                     -1.17604  \n",
       "1707                                     -0.84974                      0.84974  \n",
       "30                                        1.17604                     -1.17604  \n",
       "1178                                     -0.84974                      0.84974  \n",
       "1257                                     -0.84974                      0.84974  \n",
       "279                                       1.17604                     -1.17604  \n",
       "689                                       1.17604                     -1.17604  \n",
       "1679                                     -0.84974                      0.84974  \n",
       "664                                       1.17604                     -1.17604  \n",
       "1396                                     -0.84974                      0.84974  \n",
       "1228                                     -0.84974                      0.84974  \n",
       "1077                                      1.17604                     -1.17604  \n",
       "1318                                     -0.84974                      0.84974  \n",
       "723                                      -0.84974                      0.84974  \n",
       "815                                       1.17604                     -1.17604  \n",
       "\n",
       "[1490 rows x 42 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beds</th>\n",
       "      <th>baths</th>\n",
       "      <th>sqft</th>\n",
       "      <th>lot_size</th>\n",
       "      <th>basement</th>\n",
       "      <th>restaurants</th>\n",
       "      <th>groceries</th>\n",
       "      <th>nightlife</th>\n",
       "      <th>cafes</th>\n",
       "      <th>shopping</th>\n",
       "      <th>...</th>\n",
       "      <th>exterior_walls_Other</th>\n",
       "      <th>exterior_walls_Siding (Alum/Vinyl)</th>\n",
       "      <th>exterior_walls_Wood</th>\n",
       "      <th>roof_Asphalt</th>\n",
       "      <th>roof_Composition Shingle</th>\n",
       "      <th>roof_Missing</th>\n",
       "      <th>roof_Other</th>\n",
       "      <th>roof_Shake Shingle</th>\n",
       "      <th>property_type_Apartment / Condo / Townhouse</th>\n",
       "      <th>property_type_Single-Family</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.490000e+03</td>\n",
       "      <td>1.490000e+03</td>\n",
       "      <td>1.490000e+03</td>\n",
       "      <td>1.490000e+03</td>\n",
       "      <td>1.490000e+03</td>\n",
       "      <td>1.490000e+03</td>\n",
       "      <td>1.490000e+03</td>\n",
       "      <td>1.490000e+03</td>\n",
       "      <td>1.490000e+03</td>\n",
       "      <td>1.490000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>1.490000e+03</td>\n",
       "      <td>1.490000e+03</td>\n",
       "      <td>1.490000e+03</td>\n",
       "      <td>1.490000e+03</td>\n",
       "      <td>1.490000e+03</td>\n",
       "      <td>1.490000e+03</td>\n",
       "      <td>1.490000e+03</td>\n",
       "      <td>1.490000e+03</td>\n",
       "      <td>1.490000e+03</td>\n",
       "      <td>1.490000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-1.902281e-16</td>\n",
       "      <td>-4.254613e-17</td>\n",
       "      <td>7.663519e-17</td>\n",
       "      <td>3.911860e-17</td>\n",
       "      <td>9.746119e-17</td>\n",
       "      <td>1.409760e-16</td>\n",
       "      <td>1.621373e-16</td>\n",
       "      <td>2.827995e-16</td>\n",
       "      <td>8.946050e-18</td>\n",
       "      <td>2.315448e-17</td>\n",
       "      <td>...</td>\n",
       "      <td>1.184362e-16</td>\n",
       "      <td>1.359092e-16</td>\n",
       "      <td>-2.747988e-16</td>\n",
       "      <td>-8.278240e-17</td>\n",
       "      <td>-2.752459e-16</td>\n",
       "      <td>2.772577e-16</td>\n",
       "      <td>-2.613867e-16</td>\n",
       "      <td>7.708226e-17</td>\n",
       "      <td>8.941393e-17</td>\n",
       "      <td>-8.941393e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.268801e+00</td>\n",
       "      <td>-1.697190e+00</td>\n",
       "      <td>-1.405276e+00</td>\n",
       "      <td>-3.662250e-01</td>\n",
       "      <td>-2.688343e+00</td>\n",
       "      <td>-8.405927e-01</td>\n",
       "      <td>-9.756023e-01</td>\n",
       "      <td>-5.928336e-01</td>\n",
       "      <td>-6.967768e-01</td>\n",
       "      <td>-7.559221e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.975485e-01</td>\n",
       "      <td>-6.055792e-01</td>\n",
       "      <td>-2.652453e-01</td>\n",
       "      <td>-2.808475e-01</td>\n",
       "      <td>-1.343434e+00</td>\n",
       "      <td>-4.829980e-01</td>\n",
       "      <td>-2.534612e-01</td>\n",
       "      <td>-1.862765e-01</td>\n",
       "      <td>-8.497402e-01</td>\n",
       "      <td>-1.176040e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-4.047185e-01</td>\n",
       "      <td>-6.224713e-01</td>\n",
       "      <td>-7.491974e-01</td>\n",
       "      <td>-3.219217e-01</td>\n",
       "      <td>3.717266e-01</td>\n",
       "      <td>-7.128947e-01</td>\n",
       "      <td>-7.532980e-01</td>\n",
       "      <td>-5.928336e-01</td>\n",
       "      <td>-6.967768e-01</td>\n",
       "      <td>-6.412758e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.975485e-01</td>\n",
       "      <td>-6.055792e-01</td>\n",
       "      <td>-2.652453e-01</td>\n",
       "      <td>-2.808475e-01</td>\n",
       "      <td>-1.343434e+00</td>\n",
       "      <td>-4.829980e-01</td>\n",
       "      <td>-2.534612e-01</td>\n",
       "      <td>-1.862765e-01</td>\n",
       "      <td>-8.497402e-01</td>\n",
       "      <td>-1.176040e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.273226e-01</td>\n",
       "      <td>4.522474e-01</td>\n",
       "      <td>-3.155383e-01</td>\n",
       "      <td>-1.885809e-01</td>\n",
       "      <td>3.717266e-01</td>\n",
       "      <td>-3.936498e-01</td>\n",
       "      <td>-3.086896e-01</td>\n",
       "      <td>-3.559228e-01</td>\n",
       "      <td>-2.936977e-01</td>\n",
       "      <td>-3.737676e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.975485e-01</td>\n",
       "      <td>-6.055792e-01</td>\n",
       "      <td>-2.652453e-01</td>\n",
       "      <td>-2.808475e-01</td>\n",
       "      <td>7.438617e-01</td>\n",
       "      <td>-4.829980e-01</td>\n",
       "      <td>-2.534612e-01</td>\n",
       "      <td>-1.862765e-01</td>\n",
       "      <td>-8.497402e-01</td>\n",
       "      <td>8.497402e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.273226e-01</td>\n",
       "      <td>4.522474e-01</td>\n",
       "      <td>5.334699e-01</td>\n",
       "      <td>-2.831904e-02</td>\n",
       "      <td>3.717266e-01</td>\n",
       "      <td>3.512552e-01</td>\n",
       "      <td>5.805274e-01</td>\n",
       "      <td>1.178989e-01</td>\n",
       "      <td>1.093814e-01</td>\n",
       "      <td>1.994641e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.975485e-01</td>\n",
       "      <td>1.650203e+00</td>\n",
       "      <td>-2.652453e-01</td>\n",
       "      <td>-2.808475e-01</td>\n",
       "      <td>7.438617e-01</td>\n",
       "      <td>-4.829980e-01</td>\n",
       "      <td>-2.534612e-01</td>\n",
       "      <td>-1.862765e-01</td>\n",
       "      <td>1.176040e+00</td>\n",
       "      <td>8.497402e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.459364e+00</td>\n",
       "      <td>3.676403e+00</td>\n",
       "      <td>4.255036e+00</td>\n",
       "      <td>1.217405e+01</td>\n",
       "      <td>3.717266e-01</td>\n",
       "      <td>4.820685e+00</td>\n",
       "      <td>4.359699e+00</td>\n",
       "      <td>5.685304e+00</td>\n",
       "      <td>5.618130e+00</td>\n",
       "      <td>5.740705e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>5.058652e+00</td>\n",
       "      <td>1.650203e+00</td>\n",
       "      <td>3.767565e+00</td>\n",
       "      <td>3.558261e+00</td>\n",
       "      <td>7.438617e-01</td>\n",
       "      <td>2.069013e+00</td>\n",
       "      <td>3.942729e+00</td>\n",
       "      <td>5.364762e+00</td>\n",
       "      <td>1.176040e+00</td>\n",
       "      <td>8.497402e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               beds         baths          sqft      lot_size      basement  \\\n",
       "count  1.490000e+03  1.490000e+03  1.490000e+03  1.490000e+03  1.490000e+03   \n",
       "mean  -1.902281e-16 -4.254613e-17  7.663519e-17  3.911860e-17  9.746119e-17   \n",
       "std    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "min   -2.268801e+00 -1.697190e+00 -1.405276e+00 -3.662250e-01 -2.688343e+00   \n",
       "25%   -4.047185e-01 -6.224713e-01 -7.491974e-01 -3.219217e-01  3.717266e-01   \n",
       "50%    5.273226e-01  4.522474e-01 -3.155383e-01 -1.885809e-01  3.717266e-01   \n",
       "75%    5.273226e-01  4.522474e-01  5.334699e-01 -2.831904e-02  3.717266e-01   \n",
       "max    1.459364e+00  3.676403e+00  4.255036e+00  1.217405e+01  3.717266e-01   \n",
       "\n",
       "        restaurants     groceries     nightlife         cafes      shopping  \\\n",
       "count  1.490000e+03  1.490000e+03  1.490000e+03  1.490000e+03  1.490000e+03   \n",
       "mean   1.409760e-16  1.621373e-16  2.827995e-16  8.946050e-18  2.315448e-17   \n",
       "std    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "min   -8.405927e-01 -9.756023e-01 -5.928336e-01 -6.967768e-01 -7.559221e-01   \n",
       "25%   -7.128947e-01 -7.532980e-01 -5.928336e-01 -6.967768e-01 -6.412758e-01   \n",
       "50%   -3.936498e-01 -3.086896e-01 -3.559228e-01 -2.936977e-01 -3.737676e-01   \n",
       "75%    3.512552e-01  5.805274e-01  1.178989e-01  1.093814e-01  1.994641e-01   \n",
       "max    4.820685e+00  4.359699e+00  5.685304e+00  5.618130e+00  5.740705e+00   \n",
       "\n",
       "                  ...               exterior_walls_Other  \\\n",
       "count             ...                       1.490000e+03   \n",
       "mean              ...                       1.184362e-16   \n",
       "std               ...                       1.000000e+00   \n",
       "min               ...                      -1.975485e-01   \n",
       "25%               ...                      -1.975485e-01   \n",
       "50%               ...                      -1.975485e-01   \n",
       "75%               ...                      -1.975485e-01   \n",
       "max               ...                       5.058652e+00   \n",
       "\n",
       "       exterior_walls_Siding (Alum/Vinyl)  exterior_walls_Wood  roof_Asphalt  \\\n",
       "count                        1.490000e+03         1.490000e+03  1.490000e+03   \n",
       "mean                         1.359092e-16        -2.747988e-16 -8.278240e-17   \n",
       "std                          1.000000e+00         1.000000e+00  1.000000e+00   \n",
       "min                         -6.055792e-01        -2.652453e-01 -2.808475e-01   \n",
       "25%                         -6.055792e-01        -2.652453e-01 -2.808475e-01   \n",
       "50%                         -6.055792e-01        -2.652453e-01 -2.808475e-01   \n",
       "75%                          1.650203e+00        -2.652453e-01 -2.808475e-01   \n",
       "max                          1.650203e+00         3.767565e+00  3.558261e+00   \n",
       "\n",
       "       roof_Composition Shingle  roof_Missing    roof_Other  \\\n",
       "count              1.490000e+03  1.490000e+03  1.490000e+03   \n",
       "mean              -2.752459e-16  2.772577e-16 -2.613867e-16   \n",
       "std                1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "min               -1.343434e+00 -4.829980e-01 -2.534612e-01   \n",
       "25%               -1.343434e+00 -4.829980e-01 -2.534612e-01   \n",
       "50%                7.438617e-01 -4.829980e-01 -2.534612e-01   \n",
       "75%                7.438617e-01 -4.829980e-01 -2.534612e-01   \n",
       "max                7.438617e-01  2.069013e+00  3.942729e+00   \n",
       "\n",
       "       roof_Shake Shingle  property_type_Apartment / Condo / Townhouse  \\\n",
       "count        1.490000e+03                                 1.490000e+03   \n",
       "mean         7.708226e-17                                 8.941393e-17   \n",
       "std          1.000000e+00                                 1.000000e+00   \n",
       "min         -1.862765e-01                                -8.497402e-01   \n",
       "25%         -1.862765e-01                                -8.497402e-01   \n",
       "50%         -1.862765e-01                                -8.497402e-01   \n",
       "75%         -1.862765e-01                                 1.176040e+00   \n",
       "max          5.364762e+00                                 1.176040e+00   \n",
       "\n",
       "       property_type_Single-Family  \n",
       "count                 1.490000e+03  \n",
       "mean                 -8.941393e-17  \n",
       "std                   1.000000e+00  \n",
       "min                  -1.176040e+00  \n",
       "25%                  -1.176040e+00  \n",
       "50%                   8.497402e-01  \n",
       "75%                   8.497402e-01  \n",
       "max                   8.497402e-01  \n",
       "\n",
       "[8 rows x 42 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Check for mean and std dev.\n",
    "X_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Note: We use train_mean and train_std_dev to standardize test data set\n",
    "X_test = (X_test - train_mean) / train_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beds</th>\n",
       "      <th>baths</th>\n",
       "      <th>sqft</th>\n",
       "      <th>lot_size</th>\n",
       "      <th>basement</th>\n",
       "      <th>restaurants</th>\n",
       "      <th>groceries</th>\n",
       "      <th>nightlife</th>\n",
       "      <th>cafes</th>\n",
       "      <th>shopping</th>\n",
       "      <th>...</th>\n",
       "      <th>exterior_walls_Other</th>\n",
       "      <th>exterior_walls_Siding (Alum/Vinyl)</th>\n",
       "      <th>exterior_walls_Wood</th>\n",
       "      <th>roof_Asphalt</th>\n",
       "      <th>roof_Composition Shingle</th>\n",
       "      <th>roof_Missing</th>\n",
       "      <th>roof_Other</th>\n",
       "      <th>roof_Shake Shingle</th>\n",
       "      <th>property_type_Apartment / Condo / Townhouse</th>\n",
       "      <th>property_type_Single-Family</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.117360</td>\n",
       "      <td>-0.080790</td>\n",
       "      <td>-0.090918</td>\n",
       "      <td>-0.032233</td>\n",
       "      <td>0.010753</td>\n",
       "      <td>0.091066</td>\n",
       "      <td>0.141283</td>\n",
       "      <td>0.056925</td>\n",
       "      <td>0.109381</td>\n",
       "      <td>0.132152</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.042540</td>\n",
       "      <td>-0.025002</td>\n",
       "      <td>-0.005761</td>\n",
       "      <td>-0.002950</td>\n",
       "      <td>0.010790</td>\n",
       "      <td>-0.004068</td>\n",
       "      <td>0.027785</td>\n",
       "      <td>-0.052337</td>\n",
       "      <td>0.111555</td>\n",
       "      <td>-0.111555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.958651</td>\n",
       "      <td>0.989343</td>\n",
       "      <td>1.001612</td>\n",
       "      <td>1.034313</td>\n",
       "      <td>0.988393</td>\n",
       "      <td>1.003586</td>\n",
       "      <td>0.996496</td>\n",
       "      <td>1.034407</td>\n",
       "      <td>1.077763</td>\n",
       "      <td>1.121003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.890424</td>\n",
       "      <td>0.987522</td>\n",
       "      <td>0.990833</td>\n",
       "      <td>0.996149</td>\n",
       "      <td>0.997703</td>\n",
       "      <td>0.997762</td>\n",
       "      <td>1.050725</td>\n",
       "      <td>0.852944</td>\n",
       "      <td>1.012934</td>\n",
       "      <td>1.012934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.268801</td>\n",
       "      <td>-1.697190</td>\n",
       "      <td>-1.261108</td>\n",
       "      <td>-0.366225</td>\n",
       "      <td>-2.688343</td>\n",
       "      <td>-0.840593</td>\n",
       "      <td>-0.975602</td>\n",
       "      <td>-0.592834</td>\n",
       "      <td>-0.696777</td>\n",
       "      <td>-0.755922</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>-1.343434</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.849740</td>\n",
       "      <td>-1.176040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.404719</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-0.803935</td>\n",
       "      <td>-0.324824</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.627763</td>\n",
       "      <td>-0.753298</td>\n",
       "      <td>-0.474378</td>\n",
       "      <td>-0.562417</td>\n",
       "      <td>-0.564845</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>-1.343434</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.849740</td>\n",
       "      <td>-1.176040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.404719</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-0.386851</td>\n",
       "      <td>-0.266126</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>-0.287235</td>\n",
       "      <td>-0.086385</td>\n",
       "      <td>-0.355923</td>\n",
       "      <td>-0.293698</td>\n",
       "      <td>-0.259121</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>-0.605579</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>-0.849740</td>\n",
       "      <td>0.849740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.527323</td>\n",
       "      <td>0.452247</td>\n",
       "      <td>0.306233</td>\n",
       "      <td>-0.062883</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>0.500236</td>\n",
       "      <td>0.580527</td>\n",
       "      <td>0.117899</td>\n",
       "      <td>0.243741</td>\n",
       "      <td>0.333218</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197548</td>\n",
       "      <td>1.650203</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>-0.280848</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>-0.482998</td>\n",
       "      <td>-0.253461</td>\n",
       "      <td>-0.186276</td>\n",
       "      <td>1.176040</td>\n",
       "      <td>0.849740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.459364</td>\n",
       "      <td>3.676403</td>\n",
       "      <td>4.127830</td>\n",
       "      <td>12.149022</td>\n",
       "      <td>0.371727</td>\n",
       "      <td>4.820685</td>\n",
       "      <td>3.915091</td>\n",
       "      <td>5.803759</td>\n",
       "      <td>5.349410</td>\n",
       "      <td>5.740705</td>\n",
       "      <td>...</td>\n",
       "      <td>5.058652</td>\n",
       "      <td>1.650203</td>\n",
       "      <td>3.767565</td>\n",
       "      <td>3.558261</td>\n",
       "      <td>0.743862</td>\n",
       "      <td>2.069013</td>\n",
       "      <td>3.942729</td>\n",
       "      <td>5.364762</td>\n",
       "      <td>1.176040</td>\n",
       "      <td>0.849740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             beds       baths        sqft    lot_size    basement  \\\n",
       "count  373.000000  373.000000  373.000000  373.000000  373.000000   \n",
       "mean    -0.117360   -0.080790   -0.090918   -0.032233    0.010753   \n",
       "std      0.958651    0.989343    1.001612    1.034313    0.988393   \n",
       "min     -2.268801   -1.697190   -1.261108   -0.366225   -2.688343   \n",
       "25%     -0.404719   -0.622471   -0.803935   -0.324824    0.371727   \n",
       "50%     -0.404719   -0.622471   -0.386851   -0.266126    0.371727   \n",
       "75%      0.527323    0.452247    0.306233   -0.062883    0.371727   \n",
       "max      1.459364    3.676403    4.127830   12.149022    0.371727   \n",
       "\n",
       "       restaurants   groceries   nightlife       cafes    shopping  \\\n",
       "count   373.000000  373.000000  373.000000  373.000000  373.000000   \n",
       "mean      0.091066    0.141283    0.056925    0.109381    0.132152   \n",
       "std       1.003586    0.996496    1.034407    1.077763    1.121003   \n",
       "min      -0.840593   -0.975602   -0.592834   -0.696777   -0.755922   \n",
       "25%      -0.627763   -0.753298   -0.474378   -0.562417   -0.564845   \n",
       "50%      -0.287235   -0.086385   -0.355923   -0.293698   -0.259121   \n",
       "75%       0.500236    0.580527    0.117899    0.243741    0.333218   \n",
       "max       4.820685    3.915091    5.803759    5.349410    5.740705   \n",
       "\n",
       "                  ...               exterior_walls_Other  \\\n",
       "count             ...                         373.000000   \n",
       "mean              ...                          -0.042540   \n",
       "std               ...                           0.890424   \n",
       "min               ...                          -0.197548   \n",
       "25%               ...                          -0.197548   \n",
       "50%               ...                          -0.197548   \n",
       "75%               ...                          -0.197548   \n",
       "max               ...                           5.058652   \n",
       "\n",
       "       exterior_walls_Siding (Alum/Vinyl)  exterior_walls_Wood  roof_Asphalt  \\\n",
       "count                          373.000000           373.000000    373.000000   \n",
       "mean                            -0.025002            -0.005761     -0.002950   \n",
       "std                              0.987522             0.990833      0.996149   \n",
       "min                             -0.605579            -0.265245     -0.280848   \n",
       "25%                             -0.605579            -0.265245     -0.280848   \n",
       "50%                             -0.605579            -0.265245     -0.280848   \n",
       "75%                              1.650203            -0.265245     -0.280848   \n",
       "max                              1.650203             3.767565      3.558261   \n",
       "\n",
       "       roof_Composition Shingle  roof_Missing  roof_Other  roof_Shake Shingle  \\\n",
       "count                373.000000    373.000000  373.000000          373.000000   \n",
       "mean                   0.010790     -0.004068    0.027785           -0.052337   \n",
       "std                    0.997703      0.997762    1.050725            0.852944   \n",
       "min                   -1.343434     -0.482998   -0.253461           -0.186276   \n",
       "25%                   -1.343434     -0.482998   -0.253461           -0.186276   \n",
       "50%                    0.743862     -0.482998   -0.253461           -0.186276   \n",
       "75%                    0.743862     -0.482998   -0.253461           -0.186276   \n",
       "max                    0.743862      2.069013    3.942729            5.364762   \n",
       "\n",
       "       property_type_Apartment / Condo / Townhouse  \\\n",
       "count                                   373.000000   \n",
       "mean                                      0.111555   \n",
       "std                                       1.012934   \n",
       "min                                      -0.849740   \n",
       "25%                                      -0.849740   \n",
       "50%                                      -0.849740   \n",
       "75%                                       1.176040   \n",
       "max                                       1.176040   \n",
       "\n",
       "       property_type_Single-Family  \n",
       "count                   373.000000  \n",
       "mean                     -0.111555  \n",
       "std                       1.012934  \n",
       "min                      -1.176040  \n",
       "25%                      -1.176040  \n",
       "50%                       0.849740  \n",
       "75%                       0.849740  \n",
       "max                       0.849740  \n",
       "\n",
       "[8 rows x 42 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Check for mean and std dev. - not exactly 0 and 1\n",
    "X_test.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.3 Model 1 - Baseline Model\n",
    "* In this model, for every test data point, we will simply predict the average of the train labels as the output.\n",
    "* We will use this simple model to perform hypothesis testing for other complex models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Train results\n",
    "y_train_pred = np.ones(y_train.shape[0])*y_train.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Test results\n",
    "y_pred = np.ones(y_test.shape[0])*y_train.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for Baseline Model:\n",
      "*******************************\n",
      "Root mean squared error:  153791.70506675562\n",
      "R-squared:  0.0\n",
      "Mean Absolute Error:  127271.757171\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for Baseline Model:\")\n",
    "print(\"*******************************\")\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))\n",
    "print(\"Mean Absolute Error: \", mae(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for Baseline Model:\n",
      "*******************************\n",
      "Root mean squared error:  143268.37228905046\n",
      "R-squared:  -0.00731881601388\n",
      "Mean Absolute Error:  120855.475979\n"
     ]
    }
   ],
   "source": [
    "print(\"Results for Baseline Model:\")\n",
    "print(\"*******************************\")\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_pred)))\n",
    "print(\"R-squared: \", r2_score(y_test, y_pred))\n",
    "print(\"Mean Absolute Error: \", mae(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean Absolute Error : https://medium.com/@ewuramaminka/mean-absolute-error-mae-machine-learning-ml-b9b4afc63077"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.3 Model-2 Ridge Regression\n",
    "tune Algorithms Parameter : https://machinelearningmastery.com/how-to-tune-algorithm-parameters-with-scikit-learn/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise',\n",
       "       estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'alpha': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_mean_absolute_error', verbose=0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## prepare a range of alpha values to test\n",
    "tuned_params = {'alpha': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]}\n",
    "\n",
    "#create and fit a ridge regression model, testing each alpha\n",
    "model = GridSearchCV(Ridge(), tuned_params, scoring = 'neg_mean_absolute_error', cv=10, n_jobs=-1)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=100, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## summarize the results of the grid search\n",
    "model.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 497198.16692868,  453917.05201083,  330995.2047155 , ...,\n",
       "        491141.85962915,  455257.06349452,  376663.75055036])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Predict Train results\n",
    "y_train_pred = model.predict(X_train)\n",
    "y_train_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 443755.49971938,  453267.2257306 ,  281423.68168996,\n",
       "        315896.95788338,  449736.05232474,  334499.31332705,\n",
       "        361894.53995506,  373040.48928474,  306562.14885903,\n",
       "        498834.98684826,  359731.65857348,  349311.92923137,\n",
       "        351858.78446807,  338784.45004392,  309034.92669731,\n",
       "        369699.03811273,  458637.8616302 ,  437886.27331552,\n",
       "        460564.89845639,  399116.6399976 ,  398420.44210066,\n",
       "        257566.75024973,  379423.30172873,  415044.94924522,\n",
       "        321454.08128171,  634664.13040935,  402784.93435308,\n",
       "        359028.93790122,  343876.37787793,  563502.64834672,\n",
       "        546054.74805121,  396900.99839534,  383879.05526593,\n",
       "        396049.09036899,  445276.54990092,  492543.53488738,\n",
       "        254067.08446499,  319268.74246317,  338696.40206955,\n",
       "        302578.6021627 ,  424189.39335341,  426297.47620738,\n",
       "        468193.67372893,  268018.28727627,  316942.47690684,\n",
       "        708331.95286051,  473544.87710018,  581680.08560464,\n",
       "        546581.22828841,  390491.17834349,  468473.73773097,\n",
       "        452144.11226011,  464012.6477086 ,  347174.16138147,\n",
       "        462845.8115793 ,  397297.235203  ,  407168.62950201,\n",
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       "        386844.42103183,  472723.17211482,  407990.34567157,\n",
       "        549374.38117817,  686166.99557429,  400141.89421802,\n",
       "        414997.92055286,  548490.06553964,  422588.5760887 ,\n",
       "        491734.36567601,  366222.28673724,  488899.99031237,\n",
       "        458916.14651915,  408479.94797411,  442481.28599359,\n",
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       "        391015.22486578,  505638.07925233,  342188.90136018,\n",
       "        598162.80423323,  293942.46935766,  480648.79831065,\n",
       "        275651.69724573,  471346.03415363,  382550.03382092,\n",
       "        480963.97719702,  646418.47707565,  378188.19634871,\n",
       "        429273.8134092 ,  410259.97875595,  403545.07154395,\n",
       "        263682.27961673,  421696.45275412,  457460.05570884,\n",
       "        456467.33961661,  458727.2791864 ,  356994.66823499,\n",
       "        450385.29474733,  340384.12069548,  316114.49058094,\n",
       "        271931.84766626,  431095.97909246,  551588.7398901 ,\n",
       "        386978.59696132,  406766.80646809,  429262.21193722,\n",
       "        384706.91179157,  452155.5116325 ,  403732.99410223,\n",
       "        580466.83745211,  527125.45771826,  495568.73910605,\n",
       "        394029.03575123,  505595.71350787,  376956.78003834,\n",
       "        344601.49940674,  469245.65895558,  481268.18777314,\n",
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       "        423842.29991596,  324833.19809419,  432183.59818113,\n",
       "        478192.1393598 ,  779580.87436357,  415512.46961256,\n",
       "        264720.47176588,  360514.21309093,  421092.2038866 ,\n",
       "        353771.03048168,  393496.75343196,  467535.17398714,\n",
       "        331728.384858  ,  512697.55309972,  487149.99434021,\n",
       "        258926.53749008,  514645.44299296,  457438.45999833,\n",
       "        397960.53684133,  306389.77048301,  317293.55552429,\n",
       "        362864.84864622,  387796.76027155,  326115.8735935 ,\n",
       "        335028.96013667,  382038.93724745,  708927.32340821,\n",
       "        593798.55654738,  443900.29514221,  395635.05337429,\n",
       "        452362.54174536,  494306.38310995,  475525.56346648,\n",
       "        403818.04511803,  445598.50529452,  436883.68267467,\n",
       "        297878.22500931,  419842.7724094 ,  426402.42525636,\n",
       "        507716.49513484,  326045.65499431,  427229.25675119,\n",
       "        547653.22485468,  351504.08813795,  309804.2647506 ,\n",
       "        591026.97437901,  340813.99299086,  341001.26156611,\n",
       "        507654.27374671,  426412.77076057,  374813.98112941,\n",
       "        407185.65233702,  371265.08818235,  526678.26807627,\n",
       "        425269.30852468,  264050.23088203,  441909.34682763,\n",
       "        411932.45395453,  544403.84930678,  405511.16495667,\n",
       "        322283.61038009,  305881.37893958,  577132.71766631,\n",
       "        341472.8616329 ,  515396.52287705,  544388.85153295,\n",
       "        426915.7472981 ,  341980.530831  ,  401125.75028177,\n",
       "        396740.84252892,  372155.34386785,  480560.01447669,\n",
       "        524337.14637987,  272627.35422378,  407377.52959402,\n",
       "        357489.85831711,  390502.10236998,  703045.46582138,\n",
       "        553677.46857168,  365619.88559913,  510182.56179051,\n",
       "        453268.43411836,  329771.1059692 ,  365664.07183996,\n",
       "        327106.21721159,  293137.00429886,  557827.82682185,\n",
       "        395173.21201945,  383617.01727265,  501864.11912499,\n",
       "        441472.3491583 ,  492118.16111199,  385826.26428973,\n",
       "        470560.93027157,  325465.16076753,  417336.35185826,\n",
       "        381537.64514264,  339786.08316874,  352024.09748825,\n",
       "        406365.55676967,  435107.06836437,  337010.3917659 ,\n",
       "        494003.3662182 ,  318441.82558343,  394450.13589882,\n",
       "        268264.29617563,  373336.53501643,  342167.53805475,\n",
       "        418684.07946434,  454100.26600126,  368240.81092613,\n",
       "        365686.95582847,  359857.45383273,  462569.39299661,\n",
       "        244256.76742973,  442248.7165669 ,  696857.52355706,\n",
       "        451338.62562788,  310040.37535829,  479821.15755513,\n",
       "        406066.91945074,  415273.44946139,  512012.7494983 ,\n",
       "        435715.42574786,  345401.12284082,  534144.25109257,\n",
       "        401263.76266876,  483326.52134092,  423793.8617958 ,\n",
       "        463233.88983462,  422917.44077303,  325046.33653701,\n",
       "        365468.07361509,  488431.76747503,  299997.21148946,\n",
       "        397856.49435276,  519491.39549901,  774332.98696748,\n",
       "        467083.16463334,  309786.60452   ,  540040.56630997,\n",
       "        418645.94974609,  481871.0770951 ,  361419.39385816,\n",
       "        335308.06382998,  276232.92609182,  314392.36369122,\n",
       "        483486.60140238,  388435.18606082,  334704.34269981,\n",
       "        483278.98723328,  437121.60844655,  422973.78224812,\n",
       "        369414.85522251,  290899.76096847,  397104.90301467,\n",
       "        733758.51024562,  338267.10180547,  359677.43309848,\n",
       "        440296.39018884,  337752.06685793,  553479.63512722,\n",
       "        290002.66923198,  438256.07217164,  363031.65038225,\n",
       "        338845.73217884,  498823.99447681,  439563.49741052,\n",
       "        538828.32232554,  555387.77824905,  394255.81328286,\n",
       "        276664.31854377,  306666.70853161,  335236.83429502,\n",
       "        519239.29411089,  268295.30600849,  600625.30805379,\n",
       "        522495.62310277,  321462.89890672,  447263.75296476,\n",
       "        380204.30191253,  365960.03929144,  436485.52540383,\n",
       "        361944.80919894,  497625.03461828,  346364.11395804,\n",
       "        374413.43599567,  409026.02524678,  347019.10361604,\n",
       "        475100.23418087,  391193.92305136,  397359.86787062,\n",
       "        496387.71375088,  388786.65207038,  493384.40802698,\n",
       "        282782.32013078,  433878.4805536 ,  368962.47686014,\n",
       "        484807.38551106,  465020.7895975 ,  406732.31504415,\n",
       "        415397.34092459,  376140.74166173,  432317.0647237 ,\n",
       "        342613.6433383 ,  344949.76474918,  477167.1625913 ,\n",
       "        622257.21165622,  344975.97552966,  542005.34706559,\n",
       "        374747.72061974,  431811.80970215,  407267.89613609,\n",
       "        243699.63538965,  537572.68694718,  431562.65898461,\n",
       "        500057.82055507,  404738.01637205,  477385.93682704,\n",
       "        346420.08333148,  445615.68266533,  405783.64584885,\n",
       "        385418.27811898,  421527.74552825,  372207.62976764,\n",
       "        335894.02459542,  425032.39303143,  485595.42668161,\n",
       "        307480.08914875,  339017.32200501,  291083.02054236,\n",
       "        428031.71121405,  343575.8677846 ,  388903.07481822,\n",
       "        332535.05210379,  500175.99432077,  399648.15753909,\n",
       "        384077.96908208,  424697.00458861,  448133.78316609,\n",
       "        319981.21817609,  511264.39190178,  415227.62844227,\n",
       "        588596.26605189,  283413.30397692,  435366.74761451,\n",
       "        433508.85985752])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Predict Test results\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for Ridge Regression:\n",
      "*******************************\n",
      "Root mean squared error:  118366.31663162686\n",
      "R-squared:  0.407633564445\n",
      "Mean Absolute Error:  92194.9754984\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for Ridge Regression:\")\n",
    "print(\"*******************************\")\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))\n",
    "print(\"Mean Absolute Error: \", mae(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for Ridge Regression:\n",
      "*******************************\n",
      "Root mean squared error:  109589.65766218318\n",
      "R-squared:  0.410606376061\n",
      "Mean Absolute Error:  85241.315051\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for Ridge Regression:\")\n",
    "print(\"*******************************\")\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_pred)))\n",
    "print(\"R-squared: \", r2_score(y_test, y_pred))\n",
    "print(\"Mean Absolute Error: \", mae(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=100, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Building the model again with the best hyperparameters\n",
    "model = Ridge(alpha=100)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The features in order of importance are:\n",
      "--------------------------------------------------\n",
      "sqft\n",
      "college_grad\n",
      "3-4 bed\n",
      "baths\n",
      "property_tax\n",
      "tax_and_insurance\n",
      "older_property\n",
      "insurance\n",
      "property_age\n",
      "cafes\n",
      "beauty_spas\n",
      "during_recession\n",
      "groceries\n",
      "lot_size\n",
      "property_type_Apartment / Condo / Townhouse\n",
      "property_type_Single-Family\n",
      "shopping\n",
      "restaurants\n",
      "nightlife\n",
      "exterior_walls_Wood\n",
      "exterior_walls_Missing\n",
      "exterior_walls_Combination\n",
      "exterior_walls_Brick\n",
      "active_life\n",
      "exterior_walls_Brick veneer\n",
      "two_and_two\n",
      "school_score\n",
      "roof_Asphalt\n",
      "num_schools\n",
      "arts_entertainment\n",
      "roof_Composition Shingle\n",
      "median_age\n",
      "exterior_walls_Siding (Alum/Vinyl)\n",
      "married\n",
      "exterior_walls_Metal\n",
      "beds\n",
      "basement\n",
      "median_school\n",
      "roof_Shake Shingle\n",
      "exterior_walls_Other\n",
      "roof_Missing\n",
      "roof_Other\n"
     ]
    }
   ],
   "source": [
    "indices = np.argsort(-abs(model.coef_))\n",
    "print(\"The features in order of importance are:\")\n",
    "print(50*'-')\n",
    "for feature in X.columns[indices]:\n",
    "    print(feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.4 Model-3 Support Vector Regression\n",
    "With an RBF Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n",
       "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'C': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000], 'gamma': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_mean_absolute_error', verbose=0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## prepare a range of C and gamma  values to test\n",
    "tuned_params = {'C': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000], 'gamma': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]}\n",
    "model = GridSearchCV(SVR(), tuned_params, scoring = 'neg_mean_absolute_error', cv=5, n_jobs=-1)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=100000, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=0.01,\n",
       "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " model.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=100000, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=0.01,\n",
       "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Building the model again with the best hyperparameters\n",
    "model = SVR(C=100000, gamma=0.01)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Train results\n",
    "y_train_pred = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Test results\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for Support Vector Regression:\n",
      "*******************************\n",
      "Root mean squared error:  103092.4847586762\n",
      "R-squared:  0.550646436015\n",
      "Mean Absolute Error:  73906.8903487\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for Support Vector Regression:\")\n",
    "print(\"*******************************\")\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))\n",
    "print(\"Mean Absolute Error: \", mae(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for Support Vector Regression:\n",
      "*******************************\n",
      "Root mean squared error:  100062.95936478293\n",
      "R-squared:  0.508625088281\n",
      "Mean Absolute Error:  71665.4661373\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for Support Vector Regression:\")\n",
    "print(\"*******************************\")\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_pred)))\n",
    "print(\"R-squared: \", r2_score(y_test, y_pred))\n",
    "print(\"Mean Absolute Error: \", mae(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unfortunately getting feature importance from RBF SVM is non trivial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.5 Model-4 Random Forest Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise',\n",
       "          estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid=True, n_iter=20, n_jobs=-1,\n",
       "          param_distributions={'n_estimators': [100, 200, 300, 400, 500], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4]},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring='neg_mean_absolute_error',\n",
       "          verbose=0)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Reference for random search on random forest\n",
    "## https://towardsdatascience.com/hyperparameter-tuning-the-random-forest-in-python-using-scikit-learn-28d2aa77dd74\n",
    "\n",
    "# Create the parameter grid based on the results of random search \n",
    "tuned_params = {'n_estimators': [100, 200, 300, 400, 500], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4]}\n",
    "\n",
    "# Instantiate the grid search model\n",
    "model = RandomizedSearchCV(RandomForestRegressor(), tuned_params, n_iter=20, scoring = 'neg_mean_absolute_error', cv=5, n_jobs=-1)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=4, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=300, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Train results\n",
    "y_train_pred = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Test results\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for Random Forest Regression:\n",
      "*******************************\n",
      "Root mean squared error:  62922.989142979124\n",
      "R-squared:  0.832600996966\n",
      "Mean Absolute Error:  45862.5912466\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for Random Forest Regression:\")\n",
    "print(\"*******************************\")\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))\n",
    "print(\"Mean Absolute Error: \", mae(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for Random Forest Regression:\n",
      "*******************************\n",
      "Root mean squared error:  93579.56159047553\n",
      "R-squared:  0.570237709879\n",
      "Mean Absolute Error:  68469.2732765\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for Random Forest Regression:\")\n",
    "print(\"*******************************\")\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_pred)))\n",
    "print(\"R-squared: \", r2_score(y_test, y_pred))\n",
    "print(\"Mean Absolute Error: \", mae(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=4, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=300, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Building the model again with the best hyperparameters\n",
    "model = RandomForestRegressor(n_estimators=300, min_samples_split=2, min_samples_leaf=4)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The features in order of importance are:\n",
      "--------------------------------------------------\n",
      "insurance\n",
      "tax_and_insurance\n",
      "property_tax\n",
      "sqft\n",
      "property_age\n",
      "college_grad\n",
      "lot_size\n",
      "median_age\n",
      "married\n",
      "during_recession\n",
      "beauty_spas\n",
      "active_life\n",
      "shopping\n",
      "restaurants\n",
      "school_score\n",
      "baths\n",
      "median_school\n",
      "groceries\n",
      "arts_entertainment\n",
      "nightlife\n",
      "cafes\n",
      "beds\n",
      "exterior_walls_Siding (Alum/Vinyl)\n",
      "older_property\n",
      "exterior_walls_Brick\n",
      "roof_Composition Shingle\n",
      "exterior_walls_Metal\n",
      "num_schools\n",
      "exterior_walls_Missing\n",
      "roof_Missing\n",
      "roof_Asphalt\n",
      "roof_Shake Shingle\n",
      "exterior_walls_Brick veneer\n",
      "exterior_walls_Combination\n",
      "basement\n",
      "property_type_Apartment / Condo / Townhouse\n",
      "two_and_two\n",
      "property_type_Single-Family\n",
      "roof_Other\n",
      "3-4 bed\n",
      "exterior_walls_Wood\n",
      "exterior_walls_Other\n"
     ]
    }
   ],
   "source": [
    "indices = np.argsort(-model.feature_importances_)\n",
    "print(\"The features in order of importance are:\")\n",
    "print(50*'-')\n",
    "for feature in X.columns[indices]:\n",
    "    print(feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.5 Model-5 Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=20, error_score='raise',\n",
       "       estimator=Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=False, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'alpha': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_mean_absolute_error', verbose=0)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_params = {'alpha': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]}\n",
    "model = GridSearchCV(Lasso(), tuned_params, scoring = 'neg_mean_absolute_error', cv=20, n_jobs=-1)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=1000, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=False, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Train results\n",
    "y_train_pred = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Test results\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for Lasso Regression:\n",
      "*******************************\n",
      "Root mean squared error:  118486.30779921192\n",
      "R-squared:  0.406431959639\n",
      "Mean Absolute Error:  92407.9568146\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for Lasso Regression:\")\n",
    "print(\"*******************************\")\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))\n",
    "print(\"Mean Absolute Error: \", mae(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for Lasso Regression:\n",
      "*******************************\n",
      "Root mean squared error:  109712.56120046602\n",
      "R-squared:  0.409283638443\n",
      "Mean Absolute Error:  85172.2389506\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for Lasso Regression:\")\n",
    "print(\"*******************************\")\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_pred)))\n",
    "print(\"R-squared: \", r2_score(y_test, y_pred))\n",
    "print(\"Mean Absolute Error: \", mae(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=1000, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=False, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Building the model again with the best hyperparameters\n",
    "model = Lasso(alpha=1000)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The features in order of importance are:\n",
      "--------------------------------------------------\n",
      "sqft\n",
      "insurance\n",
      "college_grad\n",
      "3-4 bed\n",
      "property_age\n",
      "baths\n",
      "property_type_Apartment / Condo / Townhouse\n",
      "older_property\n",
      "property_tax\n",
      "groceries\n",
      "during_recession\n",
      "cafes\n",
      "exterior_walls_Wood\n",
      "nightlife\n",
      "lot_size\n",
      "active_life\n",
      "exterior_walls_Brick\n",
      "beauty_spas\n",
      "school_score\n",
      "two_and_two\n",
      "roof_Asphalt\n",
      "exterior_walls_Brick veneer\n",
      "exterior_walls_Combination\n",
      "shopping\n",
      "exterior_walls_Missing\n",
      "restaurants\n",
      "married\n",
      "median_age\n",
      "num_schools\n",
      "roof_Composition Shingle\n",
      "exterior_walls_Metal\n",
      "exterior_walls_Other\n",
      "arts_entertainment\n",
      "roof_Other\n",
      "roof_Missing\n",
      "roof_Shake Shingle\n",
      "beds\n",
      "tax_and_insurance\n",
      "median_school\n",
      "basement\n",
      "exterior_walls_Siding (Alum/Vinyl)\n",
      "property_type_Single-Family\n"
     ]
    }
   ],
   "source": [
    "indices = np.argsort(-abs(model.coef_))\n",
    "print(\"The features in order of importance are:\")\n",
    "print(50*'-')\n",
    "for feature in X.columns[indices]:\n",
    "    print(feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.5 Model-6 KNN Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The optimal number of neighbors is 13.\n"
     ]
    }
   ],
   "source": [
    "# creating odd list of K for KNN\n",
    "neighbors = list(range(1,50,2))\n",
    "# empty list that will hold cv scores\n",
    "cv_scores = []\n",
    "\n",
    "# perform 10-fold cross validation\n",
    "for k in neighbors:\n",
    "    knn = KNeighborsRegressor(n_neighbors=k)\n",
    "    scores = cross_val_score(knn, X_train, y_train, cv=10, scoring='neg_mean_absolute_error')\n",
    "    cv_scores.append(scores.mean())\n",
    "\n",
    "# changing to misclassification error\n",
    "MSE = [1 - x for x in cv_scores]\n",
    "\n",
    "# determining best k\n",
    "optimal_k = neighbors[MSE.index(min(MSE))]\n",
    "print('\\nThe optimal number of neighbors is %d.' % optimal_k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=1, n_neighbors=13, p=2,\n",
       "          weights='uniform')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = KNeighborsRegressor(n_neighbors = optimal_k)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Train results\n",
    "y_train_pred = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Test results\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for KN Regression:\n",
      "*******************************\n",
      "Root mean squared error:  114895.58193502143\n",
      "R-squared:  0.441862973362\n",
      "Mean Absolute Error:  89612.4573051\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for KN Regression:\")\n",
    "print(\"*******************************\")\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))\n",
    "print(\"Mean Absolute Error: \", mae(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for KN Regression:\n",
      "*******************************\n",
      "Root mean squared error:  107796.23123522308\n",
      "R-squared:  0.429739293929\n",
      "Mean Absolute Error:  83847.2445865\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for KN Regression:\")\n",
    "print(\"*******************************\")\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_pred)))\n",
    "print(\"R-squared: \", r2_score(y_test, y_pred))\n",
    "print(\"Mean Absolute Error: \", mae(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.5 Model-7 Decision Tree Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=10, error_score='raise',\n",
       "          estimator=DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best'),\n",
       "          fit_params=None, iid=True, n_iter=20, n_jobs=-1,\n",
       "          param_distributions={'min_samples_split': [2, 3, 4, 5, 7], 'min_samples_leaf': [1, 2, 3, 4, 6], 'max_depth': [2, 3, 4, 5, 6, 7]},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring='neg_mean_absolute_error',\n",
       "          verbose=0)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_params = {'min_samples_split': [2, 3, 4, 5, 7], 'min_samples_leaf': [1, 2, 3, 4, 6], 'max_depth': [2, 3, 4, 5, 6, 7]}\n",
    "model = RandomizedSearchCV(DecisionTreeRegressor(), tuned_params, n_iter=20, scoring = 'neg_mean_absolute_error', cv=10, n_jobs=-1)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=4, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=3,\n",
       "           min_samples_split=3, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Train results\n",
    "y_train_pred = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Test results\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for Decision Tree Regression:\n",
      "*******************************\n",
      "Root mean squared error:  104478.4751248797\n",
      "R-squared:  0.538482868183\n",
      "Mean Absolute Error:  78134.4814877\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for Decision Tree Regression:\")\n",
    "print(\"*******************************\")\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))\n",
    "print(\"Mean Absolute Error: \", mae(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for Decision Tree Regression:\n",
      "*******************************\n",
      "Root mean squared error:  103448.59365273765\n",
      "R-squared:  0.474811177254\n",
      "Mean Absolute Error:  77289.7832468\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for Decision Tree Regression:\")\n",
    "print(\"*******************************\")\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_pred)))\n",
    "print(\"R-squared: \", r2_score(y_test, y_pred))\n",
    "print(\"Mean Absolute Error: \", mae(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=4, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=3,\n",
       "           min_samples_split=3, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Building the model again with the best hyperparameters\n",
    "model = DecisionTreeRegressor(max_depth=4, min_samples_split=3, min_samples_leaf=3)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The features in order of importance are:\n",
      "--------------------------------------------------\n",
      "tax_and_insurance\n",
      "insurance\n",
      "during_recession\n",
      "sqft\n",
      "active_life\n",
      "cafes\n",
      "college_grad\n",
      "property_age\n",
      "median_school\n",
      "school_score\n",
      "exterior_walls_Brick\n",
      "exterior_walls_Brick veneer\n",
      "exterior_walls_Combination\n",
      "beds\n",
      "exterior_walls_Other\n",
      "exterior_walls_Siding (Alum/Vinyl)\n",
      "exterior_walls_Wood\n",
      "roof_Asphalt\n",
      "roof_Composition Shingle\n",
      "roof_Missing\n",
      "roof_Other\n",
      "roof_Shake Shingle\n",
      "exterior_walls_Metal\n",
      "exterior_walls_Missing\n",
      "two_and_two\n",
      "3-4 bed\n",
      "baths\n",
      "lot_size\n",
      "basement\n",
      "restaurants\n",
      "groceries\n",
      "nightlife\n",
      "older_property\n",
      "shopping\n",
      "beauty_spas\n",
      "median_age\n",
      "married\n",
      "property_tax\n",
      "num_schools\n",
      "property_type_Apartment / Condo / Townhouse\n",
      "arts_entertainment\n",
      "property_type_Single-Family\n"
     ]
    }
   ],
   "source": [
    "indices = np.argsort(-model.feature_importances_)\n",
    "print(\"The features in order of importance are:\")\n",
    "print(50*'-')\n",
    "for feature in X.columns[indices]:\n",
    "    print(feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.5 Model-8 Xgboost Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise',\n",
       "          estimator=XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1),\n",
       "          fit_params=None, iid=True, n_iter=20, n_jobs=-1,\n",
       "          param_distributions={'max_depth': [1, 2, 3, 4, 5], 'learning_rate': [0.01, 0.05, 0.1], 'n_estimators': [100, 200, 300, 400, 500], 'reg_lambda': [0.001, 0.1, 1.0, 10.0, 100.0]},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring='neg_mean_absolute_error',\n",
       "          verbose=0)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Reference for random search on xgboost\n",
    "## https://gist.github.com/wrwr/3f6b66bf4ee01bf48be965f60d14454d\n",
    "tuned_params = {'max_depth': [1, 2, 3, 4, 5], 'learning_rate': [0.01, 0.05, 0.1], 'n_estimators': [100, 200, 300, 400, 500], 'reg_lambda': [0.001, 0.1, 1.0, 10.0, 100.0]}\n",
    "model = RandomizedSearchCV(XGBRegressor(), tuned_params, n_iter=20, scoring = 'neg_mean_absolute_error', cv=5, n_jobs=-1)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.05, max_delta_step=0,\n",
       "       max_depth=2, min_child_weight=1, missing=None, n_estimators=300,\n",
       "       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=10.0, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Train results\n",
    "y_train_pred = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "## Predict Test results## Predic \n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for XGBoost Regression:\n",
      "*******************************\n",
      "Root mean squared error:  96052.55115848806\n",
      "R-squared:  0.609921526326\n",
      "Mean Absolute Error:  72587.6521393\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for XGBoost Regression:\")\n",
    "print(\"*******************************\")\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))\n",
    "print(\"Mean Absolute Error: \", mae(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for XGBoost Regression:\n",
      "*******************************\n",
      "Root mean squared error:  96529.90387668562\n",
      "R-squared:  0.542711752224\n",
      "Mean Absolute Error:  70395.2871146\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for XGBoost Regression:\")\n",
    "print(\"*******************************\")\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_pred)))\n",
    "print(\"R-squared: \", r2_score(y_test, y_pred))\n",
    "print(\"Mean Absolute Error: \", mae(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.05, max_delta_step=0,\n",
       "       max_depth=2, min_child_weight=1, missing=None, n_estimators=300,\n",
       "       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=10.0, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Building the model again with the best hyperparameters\n",
    "model = XGBRegressor(max_depth=2,learning_rate=0.05,n_estimators=300, reg_lambda=10.0)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Function to include figsize parameter\n",
    "## Reference: https://stackoverflow.com/questions/40081888/xgboost-plot-importance-figure-size\n",
    "def my_plot_importance(booster, figsize, **kwargs): \n",
    "    from matplotlib import pyplot as plt\n",
    "    from xgboost import plot_importance\n",
    "    fig, ax = plt.subplots(1,1,figsize=figsize)\n",
    "    return plot_importance(booster=booster, ax=ax, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbfc1e10>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jYm71E4mIUcAogN26doubZ/uPCCTLXJyLKltbPsoGF9e5f+bMmZSXl1NcXNVv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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbfc11d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_plot_importance(model, (10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare all models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](Images/Capture.jpg)\n",
    "* From Above table RSE and MAE of Random Forest Regressor is lower then the all other model.\n",
    "* Therfore Random Forest Regressor is best model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Save the winning model to disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Save/rfr_real_estate.pkl']"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "win_model = RandomForestRegressor(n_estimators=300, min_samples_split=2, min_samples_leaf=4)\n",
    "win_model.fit(X_train, y_train)\n",
    "joblib.dump(win_model, 'Save/rfr_real_estate.pkl') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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