Predicting house prices.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting House Prices - PyCon 2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load required libraries and dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import norm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_data = pd.read_csv('data/kc_house_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get to know the dataset and buid an intuitive sense of it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>20141013T000000</td>\n",
       "      <td>221900</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>1955.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>98178.00</td>\n",
       "      <td>47.51</td>\n",
       "      <td>-122.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>538000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570.00</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951.00</td>\n",
       "      <td>1991.00</td>\n",
       "      <td>98125.00</td>\n",
       "      <td>47.72</td>\n",
       "      <td>-122.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>20150225T000000</td>\n",
       "      <td>180000</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770.00</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>98028.00</td>\n",
       "      <td>47.74</td>\n",
       "      <td>-122.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>604000</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960.00</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>98136.00</td>\n",
       "      <td>47.52</td>\n",
       "      <td>-122.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>20150218T000000</td>\n",
       "      <td>510000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680.00</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>98074.00</td>\n",
       "      <td>47.62</td>\n",
       "      <td>-122.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id             date   price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7129300520  20141013T000000  221900         3       1.00      1180.00   \n",
       "1  6414100192  20141209T000000  538000         3       2.25      2570.00   \n",
       "2  5631500400  20150225T000000  180000         2       1.00       770.00   \n",
       "3  2487200875  20141209T000000  604000         4       3.00      1960.00   \n",
       "4  1954400510  20150218T000000  510000         3       2.00      1680.00   \n",
       "\n",
       "   sqft_lot  floors  waterfront  view  condition  grade  sqft_above  \\\n",
       "0      5650    1.00           0     0          3      7        1180   \n",
       "1      7242    2.00           0     0          3      7        2170   \n",
       "2     10000    1.00           0     0          3      6         770   \n",
       "3      5000    1.00           0     0          5      7        1050   \n",
       "4      8080    1.00           0     0          3      8        1680   \n",
       "\n",
       "   sqft_basement  yr_built  yr_renovated  zipcode   lat    long  \n",
       "0              0   1955.00           nan 98178.00 47.51 -122.26  \n",
       "1            400   1951.00       1991.00 98125.00 47.72 -122.32  \n",
       "2              0   1933.00           nan 98028.00 47.74 -122.23  \n",
       "3            910   1965.00           nan 98136.00 47.52 -122.39  \n",
       "4              0   1987.00           nan 98074.00 47.62 -122.05  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset contains 19 columns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['id',\n",
       " 'date',\n",
       " 'price',\n",
       " 'bedrooms',\n",
       " 'bathrooms',\n",
       " 'sqft_living',\n",
       " 'sqft_lot',\n",
       " 'floors',\n",
       " 'waterfront',\n",
       " 'view',\n",
       " 'condition',\n",
       " 'grade',\n",
       " 'sqft_above',\n",
       " 'sqft_basement',\n",
       " 'yr_built',\n",
       " 'yr_renovated',\n",
       " 'zipcode',\n",
       " 'lat',\n",
       " 'long']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst_cols = list(df_data.columns)\n",
    "print('The dataset contains %s columns' % len(lst_cols))\n",
    "lst_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>id</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>21613.00</td>\n",
       "      <td>21613.00</td>\n",
       "      <td>21613.00</td>\n",
       "      <td>21613.00</td>\n",
       "      <td>21605.00</td>\n",
       "      <td>21613.00</td>\n",
       "      <td>21613.00</td>\n",
       "      <td>21613.00</td>\n",
       "      <td>21613.00</td>\n",
       "      <td>21613.00</td>\n",
       "      <td>21613.00</td>\n",
       "      <td>21613.00</td>\n",
       "      <td>21613.00</td>\n",
       "      <td>21607.00</td>\n",
       "      <td>914.00</td>\n",
       "      <td>21609.00</td>\n",
       "      <td>21613.00</td>\n",
       "      <td>21613.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4580301520.86</td>\n",
       "      <td>540088.14</td>\n",
       "      <td>3.37</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2079.94</td>\n",
       "      <td>15106.97</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.23</td>\n",
       "      <td>3.41</td>\n",
       "      <td>7.66</td>\n",
       "      <td>1788.39</td>\n",
       "      <td>291.51</td>\n",
       "      <td>1971.01</td>\n",
       "      <td>1995.83</td>\n",
       "      <td>98077.94</td>\n",
       "      <td>47.56</td>\n",
       "      <td>-122.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2876565571.31</td>\n",
       "      <td>367127.20</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.77</td>\n",
       "      <td>918.51</td>\n",
       "      <td>41420.51</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.65</td>\n",
       "      <td>1.18</td>\n",
       "      <td>828.09</td>\n",
       "      <td>442.58</td>\n",
       "      <td>29.37</td>\n",
       "      <td>15.52</td>\n",
       "      <td>53.51</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000102.00</td>\n",
       "      <td>75000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>290.00</td>\n",
       "      <td>520.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>290.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1900.00</td>\n",
       "      <td>1934.00</td>\n",
       "      <td>98001.00</td>\n",
       "      <td>47.16</td>\n",
       "      <td>-122.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2123049194.00</td>\n",
       "      <td>321950.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1430.00</td>\n",
       "      <td>5040.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1190.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1951.00</td>\n",
       "      <td>1987.00</td>\n",
       "      <td>98033.00</td>\n",
       "      <td>47.47</td>\n",
       "      <td>-122.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3904930410.00</td>\n",
       "      <td>450000.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1910.00</td>\n",
       "      <td>7618.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1560.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1975.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>98065.00</td>\n",
       "      <td>47.57</td>\n",
       "      <td>-122.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7308900445.00</td>\n",
       "      <td>645000.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2550.00</td>\n",
       "      <td>10688.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2210.00</td>\n",
       "      <td>560.00</td>\n",
       "      <td>1997.00</td>\n",
       "      <td>2007.00</td>\n",
       "      <td>98118.00</td>\n",
       "      <td>47.68</td>\n",
       "      <td>-122.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9900000190.00</td>\n",
       "      <td>7700000.00</td>\n",
       "      <td>33.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>13540.00</td>\n",
       "      <td>1651359.00</td>\n",
       "      <td>3.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>9410.00</td>\n",
       "      <td>4820.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>98199.00</td>\n",
       "      <td>47.78</td>\n",
       "      <td>-121.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id      price  bedrooms  bathrooms  sqft_living   sqft_lot  \\\n",
       "count      21613.00   21613.00  21613.00   21613.00     21605.00   21613.00   \n",
       "mean  4580301520.86  540088.14      3.37       2.11      2079.94   15106.97   \n",
       "std   2876565571.31  367127.20      0.93       0.77       918.51   41420.51   \n",
       "min      1000102.00   75000.00      0.00       0.00       290.00     520.00   \n",
       "25%   2123049194.00  321950.00      3.00       1.75      1430.00    5040.00   \n",
       "50%   3904930410.00  450000.00      3.00       2.25      1910.00    7618.00   \n",
       "75%   7308900445.00  645000.00      4.00       2.50      2550.00   10688.00   \n",
       "max   9900000190.00 7700000.00     33.00       8.00     13540.00 1651359.00   \n",
       "\n",
       "        floors  waterfront     view  condition    grade  sqft_above  \\\n",
       "count 21613.00    21613.00 21613.00   21613.00 21613.00    21613.00   \n",
       "mean      1.49        0.01     0.23       3.41     7.66     1788.39   \n",
       "std       0.54        0.09     0.77       0.65     1.18      828.09   \n",
       "min       1.00        0.00     0.00       1.00     1.00      290.00   \n",
       "25%       1.00        0.00     0.00       3.00     7.00     1190.00   \n",
       "50%       1.50        0.00     0.00       3.00     7.00     1560.00   \n",
       "75%       2.00        0.00     0.00       4.00     8.00     2210.00   \n",
       "max       3.50        1.00     4.00       5.00    13.00     9410.00   \n",
       "\n",
       "       sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \n",
       "count       21613.00  21607.00        914.00 21609.00 21613.00 21613.00  \n",
       "mean          291.51   1971.01       1995.83 98077.94    47.56  -122.21  \n",
       "std           442.58     29.37         15.52    53.51     0.14     0.14  \n",
       "min             0.00   1900.00       1934.00 98001.00    47.16  -122.52  \n",
       "25%             0.00   1951.00       1987.00 98033.00    47.47  -122.33  \n",
       "50%             0.00   1975.00       2000.00 98065.00    47.57  -122.23  \n",
       "75%           560.00   1997.00       2007.00 98118.00    47.68  -122.12  \n",
       "max          4820.00   2015.00       2015.00 98199.00    47.78  -121.31  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### List down unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bedrooms : 0,1,2,3,4,5,6,7,8,9,10,11,33\n",
      "bathrooms : 0.0,0.5,0.75,1.0,1.25,1.5,1.75,2.0,2.25,2.5,2.75,3.0,3.25,3.5,3.75,4.0,4.25,4.5,4.75,5.0,5.25,5.5,5.75,6.0,6.25,6.5,6.75,7.5,7.75,8.0\n",
      "floors : 1.0,1.5,2.0,2.5,3.0,3.5\n",
      "waterfront : 0,1\n",
      "view : 0,1,2,3,4\n",
      "condition : 1,2,3,4,5\n",
      "grade : 1,3,4,5,6,7,8,9,10,11,12,13\n"
     ]
    }
   ],
   "source": [
    "lst_interesting_features = ['bedrooms', 'bathrooms', 'floors', 'waterfront', 'view', 'condition', 'grade']\n",
    "\n",
    "for feature in lst_interesting_features:\n",
    "    print('%s : %s' %( feature, ','.join(map(str, sorted(df_data[feature].unique())))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Some Observations and Questions**\n",
    "    - Can a house have no bedrooms or no bathrooms?\n",
    "    - does id and date provide any useful information\n",
    "    - Can we have decimal values in bathroom and floor?\n",
    "    - should waterfront, view, condition and grade be categorical features?\n",
    "    - How do we incorporate location information (zipcode, lat, long) in the model?\n",
    "    - Can we engineer more useful features from these base features?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observe the distribution of numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1273c358>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZAzEw4iWKIG89VZaFaaop5+jpscBiEJHSl02L4jKg2zl3wDkXB3YC26aV2Qbc7W/fB1xp\nZubv3+mciznnDgLdfn0z1umcGwLwz68CSrIn9sytpwA7swHaGyo5NjgeaAwiUtqySRSrgMMpr3v8\nfWnLOOcSwCDQnOHcjHWa2deA48CFwN+nC8rMbjSzPWa2p7e3N4uPUVj90ThlYSv4hIDTtTVWMRCN\na20KEZm3bBJFuomKpv+WP1OZue73Npz7MNAOPA9cky4o59ydzrktzrktra2t6YoEaiAaY1l1ORbA\nPE+p2hsqAdSqEJF5yyZR9ABrUl6vBo7OVMbMIkADMJDh3FnrdM4lgXuA92cRY9EZiMYD68hO1dbg\nrU1xbFD9FCIyP9kkit3AJjPbYGbleJ3Tu6aV2QVc729fDTzkvFFeu4Dt/lNRG4BNwBMz1WmejXCm\nj+I3gf0L+4jB6I/GaQ64fwKgrjJCTUWEY6fVohCR+Zn1BrpzLmFmNwEPAmHgq865vWb2GWCPc24X\ncBfwDTPrxmtJbPfP3Wtm9wL7gATwcb+lwAx1hoC7zawe7/bU08DHcvuRC2MgGmf1ssagw8DMaG+o\n5KhaFCIyT1n1tDrnHgAemLbv0ynb48AHZjj3VuDWLOucBN6WTUzFbmAkuAkBp2trqOKx7j4Sk5NB\nhyIiJUgjs/MglkgyHEsURR8FQFtjJUnnODmkqTxEZO6UKPLgVHQCCG5U9nTtZzq01U8hInMX7EP+\ni8zUFBlTI6H3HR0qimkzmmvLKQub+ilEZF7UosiDaDwBQE3Ag+2mhMxoa6jSk08iMi9KFHkwGvNG\nQdeUhwOO5Ky2hkqODY4xOVmSM6KISICUKPKg2FoU4PVTxBKT9JzS7ScRmRslijyIxhIYUFVMLYpG\nbyoPrU0hInOlRJEH0ViS6vIwoYDneUq1or6SkMFeraEtInOkRJEH0XiiqG47AZSFQ7TWVbDvmBKF\niMyNEkUeRGPFlyjAG6GtW08iMldKFHkQjSWL6omnKe0NlZwYitE3ohHaIpI9JYo8iMYTVBdji6LR\nG6G9T/0UIjIHShQ5NukcY/EkNeVFmCj8RYzUTyEic6FEkWOj8SQOqKkovltP1eURVjVW6cknEZkT\nJYoci8aKb7Bdqo72evapQ1tE5kCJIsfOJIoivPUE0NFWz4G+KKP+6HERkdkoUeRYNO7P81SEt54A\nNrfX4xzsPz4cdCgiUiKUKHKsFG49gUZoi0j2lChyrNhvPa1qrKKhqkyPyIpI1pQociwaT1BZFiIc\nKp55nlKZGR1t6tAWkewpUeSYNyq7OFsTUza317P/+DCJ5GTQoYhICVCiyLFinBBwuteuaiCWmKTr\nhDq0RWR2ShQ5NhpLFn2iuHTdMgB+8cqpgCMRkVKgRJFj0ViiKCcETLV6WRXL6yp4UolCRLKgRJFD\nzrmSuPVkZly6bhl7lChEJAvF/Y1WYsYnJpl0xTuGAmBH5yHASxY9p8b40iMvUV9VxnWXrw04MhEp\nVmpR5NDZMRTFfesJYF1TNQCvDIwGHImIFDslihyKxot7VHaqtsZKIiHjUH806FBEpMgpUeRQsY/K\nThUJhVi9rFotChGZlRJFDkVjxT0h4HTrmqs5enqMeEID70RkZlklCjPbamZdZtZtZjenOV5hZvf4\nxzvNbH3KsVv8/V1mdtVsdZrZt/z9z5nZV82sbGEfsXBK6dYTeIli0kHPabUqRGRmsyYKMwsDXwDe\nDXQA15pZx7RiNwCnnHMbgduB2/xzO4DtwGZgK3CHmYVnqfNbwIXA64Aq4KML+oQFFI0lKA+HKAuX\nRkNtrd+h/XKf+ilEZGbZfKNdBnQ75w445+LATmDbtDLbgLv97fuAK83M/P07nXMx59xBoNuvb8Y6\nnXMPOB/wBLB6YR+xcKLxZMncdgJvadTVy6q0NoWIZJRNolgFHE553ePvS1vGOZcABoHmDOfOWqd/\ny+lDwA+ziLEoRGPFP9huuova6uk5NcaJofGgQxGRIpVNokg3X7bLssxc96e6A3jUOffTtEGZ3Whm\ne8xsT29vb7oiBedN31F6iQLgx8+fCDgSESlW2SSKHmBNyuvVwNGZyphZBGgABjKcm7FOM/sfQCvw\nJzMF5Zy70zm3xTm3pbW1NYuPkX+ldusJYEVdBU015fxonxKFiKSXTaLYDWwysw1mVo7XOb1rWpld\nwPX+9tXAQ34fwy5gu/9U1AZgE16/w4x1mtlHgauAa51zJfPcpnOuJFsUZsZFK+t4vLufEX8ciIhI\nqlkThd/ncBPwIPA8cK9zbq+ZfcbM3ucXuwtoNrNuvFbAzf65e4F7gX14fQ0fd84lZ6rTr+tLwArg\n52b2lJl9OkefNa9G40kSk67k+igALmqvJ56c5NEXiuMWnogUl6y+1ZxzDwAPTNv36ZTtceADM5x7\nK3BrNnX6+0vvmxYYiMaB0hlsl2pdUw2N1WX8eN8J3vO6tqDDEZEiUxoP/JeA/qlEUWK3ngDCIeOK\nC5fzUNdJLY8qIq+iRJEjA9EYUDqjsqd750UrOD06we6XtUaFiJyrNL/VilD/yNStp9K8pMeHxomE\njM8/9CIH/ZHaWqNCREAtipw500dRAmtRpFMRCXN+ay37jg3hPbAmIuJRosiRgWicSMgoj5TuJb2o\nrZ5ToxOcGIoFHYqIFJHS/VYrMn0jcWoqInhTXJWmC9vqANh3bCjgSESkmChR5MhANFaSj8amqq8s\nY82yKp5XohCRFEoUOdIfjVNboh3ZqS5qq+fI6TEGxyaCDkVEioQSRY70j8RLcgzFdFOTBKpVISJT\nlChywDlHfzRWso/GplpeV0FzTbkShYicoUSRA6PxJOMTk4vi1pOZcVFbPQd6owyP6/aTiChR5ESp\nD7ab7qK2epLO8RNNEigiKFHkRJ8/fUdtiT/1NGVdczXV5WF+rDUqRAQlipwYWGQtipAZF66s56H9\nJ5nQJIEiS54SRQ70l/iEgOlc1FbH0HiC3QcHgg5FRAKmRJEDfSOlO8X4TDYtr6MiEuJfdftJZMlT\nosiBgWicmvJwSc/zNF15JMTbNrbw0P6TmiRQZIlbPN9sAeofidFUWx50GDn3jte0cmhg9My04yKy\nNClR5EB/NE5zTUXQYeTc21+zHICHu/SYrMhSpkSRA/0jcVoWYYtiTVM1G5fX8kjXyaBDEZEAKVHk\nQH80tihbFODdfuo8MEA0lgg6FBEJiBLFAjnn6B+JL8o+CoB3vGY58eQkj7/UH3QoIhIQJYoFGhpL\nkJh0NNcszkSxZX0TNeVh3X4SWcKUKBZoarBdS+3ivPVUHgnxK5taeKSrV4/JiixRShQL1B/1Bts1\nLdIWBcAVFy7nyOkx9h7V1OMiS5ESxQL1j3gtiuZF2kcB8K6OlYRDxv3PHgs6FBEJwOKZcyIgUy2K\nxXjraUfnoTPb57XUcM/uw6xurOKDb14XYFQiUmhqUSzQ1FoUy6oXb4sC4HWrGhiIxjl6ejzoUESk\nwJQoFqh/JEZ9ZWRRzfOUzub2BsJmPHvkdNChiEiBLe5vtwLoi8YX5W2n6arKw2xcXsuzRwb19JPI\nEqNEsUADI/FF3ZGd6nWrGjg1OsHTPYNBhyIiBZRVojCzrWbWZWbdZnZzmuMVZnaPf7zTzNanHLvF\n399lZlfNVqeZ3eTvc2bWsrCPl3+LefqO6S5qqyccMv75l0eCDkVECmjWRGFmYeALwLuBDuBaM+uY\nVuwG4JRzbiNwO3Cbf24HsB3YDGwF7jCz8Cx1Pgb8OvDKAj9bQSzm6TumqyoP09FWz/eeOkI8oSVS\nRZaKbFoUlwHdzrkDzrk4sBPYNq3MNuBuf/s+4EozM3//TudczDl3EOj265uxTufcL51zLy/wcxVE\nPDFJfzTOirrKoEMpmEvXLePU6AT/9rxWvhNZKrJJFKuAwymve/x9acs45xLAINCc4dxs6szIzG40\nsz1mtqe3N5j1Ek4MeY+KrmxYGreeADYur2VlfSX37jk8e2ERWRSySRSWZt/0x15mKjPX/Vlzzt3p\nnNvinNvS2to6l1Nz5myiqArk/YMQMuP9l67iJy/0nvn8IrK4ZZMoeoA1Ka9XA0dnKmNmEaABGMhw\nbjZ1Fr1jg36iqF86t54Arr50DZMOvvOLnqBDEZECyCZR7AY2mdkGMyvH65zeNa3MLuB6f/tq4CHn\nPWy/C9juPxW1AdgEPJFlnUXvbItiaSWKDS01XLa+iXt3H2ZyUmMqRBa7WROF3+dwE/Ag8Dxwr3Nu\nr5l9xsze5xe7C2g2s27gT4Cb/XP3AvcC+4AfAh93ziVnqhPAzP7QzHrwWhnPmNlXcvdxc+vY4DhV\nZWHqK5felFkffPNaXu4f5ScvaD1tkcUuq28459wDwAPT9n06ZXsc+MAM594K3JpNnf7+zwGfyyau\noB0fGmdlQyXeA15Lx47OQyQmJ6mvjPC/Hnj+zC246y5fG3BkIpIPS+9X4RzZ0XmIZ3sGiYTsnFlW\nl4pIKMTl5zXzo30nODk0zvIl1k8jspRoCo8FGBqfoL6qLOgwAvOm9U1EQsbjB7SetshipkQxT5PO\nMTyWoGEJJ4raiggXr2nkl4dOMRpPBB2OiOSJEsU8RWMJks4tyY7sVG/d2EIi6XikS53aIouVEsU8\nDY17v0Ev5RYFeGNILl23jMdf6uOl3pGgwxGRPFCimKehsQmAJd1HMeWdHSsoC4e49f7ngw5FRPJA\niWKeBpUozqirLOOKC5fz0P6TPNx1MuhwRCTHlCjmaWhsgpB5HboCbzm/mfNaavjz7z7H4OhE0OGI\nSA4pUczT0PgEdZVlhJbYYLuZREIh/u81F3NiaJz/9t1ntVyqyCKiRDFPg2MTS/6Jp+kuXtPIf3nX\na7j/2WPcs1vTkIssFkoU8zQ0llD/RBr/6dfO41c2tvCX/7KP7pPDQYcjIjmgRDFPg+MTS/7R2Ol2\ndB5i5+7D/MqmFszgQ3c9wd2Pvxx0WCKyQEoU8zA8PkE8MUl9pRJFOvWVZVx9yWqODY7z4N7jQYcj\nIgukRDEPx/3ZUtWimNmFbfW85fxmHn+pn39VshApaUoU83DcX7BIfRSZbd28klWNVXxi51M803M6\n6HBEZJ6UKObhQG8UgKaa8oAjKW5l4RC/+5Z1NNeW85Gv7+bwwGjQIYnIPChRzMOzRwapqYjo8dgs\n1FWW8fUPv4mJpOODX+nkhRN6Ekqk1ChRzMNzRwZZ1bj0VrabrycOnuK6y9ZyKhrnvX//M/78u88G\nHZKIzIESxRyNTyR58eQI7Y1VQYdSUtY0VfMH79jI8roKvtl5iE9991mGxzXVh0gpUKKYo/3Hh0lO\nOtoblCjmqqGqjP/4q+fxtvOb2fHEIa66/VEe6+4LOiwRmYUSxRw9e2QQgFXLlCjmoywc4jde3853\nPvZWqsrDfOiuTu762UHNDSVSxJQo5mjvkUGWVZfRqEdjF2T/sWH+w5vXceHKev7n9/dx9Rd/zjd+\n/krQYYlIGkoUc/TskUFeu6pBHdk5UBEJc93la3nHa5bz5KFTfPuJQ8QTk0GHJSLTKFHMQSyR5IUT\nw7x2VUPQoSwaITPe2bGC976+jX3Hhvj9bz7J+EQy6LBEJIUSxRy8cHyEiaTjte1KFLn21vNb2HZx\nOw/tP8l1X/53Tg6PBx2SiPiUKOZgqiP7dWpR5MXlG5q544OX8PyxYbZ9/jFN+yFSJJQo5uC5o4PU\nV0ZY06QnnvLlPa9r476PvYWQGe//4uN88ZGXSE7qiSiRIClRZGkkluCHzx3nsg1N6sjOox2dh3j6\n8CC/99b1XLCijtt+uJ8r/uYRnjqs1oVIUJQosnTXTw8yEI1z0xWbgg5lSaipiHDdZWv5wKWrOTkc\n47e+8BjXf/UJHt5/Up3dIgWmWe2yMBCN8+WfHmDr5pVcvKYx6HCWDDPjjWuX0dFWz3hiki//9AAf\n/vpuKstCXL6hmYva6rlgRS3L6ypprC6joaqMxuoyaisiavWJ5FBWicLMtgJ/B4SBrzjn/s+04xXA\nPwKXAv3ANc65l/1jtwA3AEngD51zD2aq08w2ADuBJuAXwIecc/GFfcyFuePhbkbjCT551QVBhrFk\nVZSFqSgL84krN3GwL0rX8WG6jg/zsxf7SKYZ0R0OmZc0qspYtayKC1bUccGKWi5YUcemFXXUVuj3\nI5G5mPV/jJmFgS8A7wR6gN1mtss5ty+l2A3AKefcRjPbDtwGXGNmHcB2YDPQDvzYzKa+bWeq8zbg\ndufcTjP7kl/3F3PxYedqctLD6g28AAAK5ElEQVSx44lD/OPPX+F3LlnNxuV1QYQhvrJwyP/S9/4e\nkpOO/miMaCzJWDzBaDzJ2ESSsXiS0Ykko/EkL/WO8O8H+plInk0odZURWmsraKmtoKWunNbaCtY0\nVbN6WTVrm6pZ01RFnZa5FTkjm1+tLgO6nXMHAMxsJ7ANSE0U24C/8LfvAz5vXtt/G7DTORcDDppZ\nt18f6eo0s+eBK4Dr/DJ3+/UWLFE45+g5NcYzPYN87bGD7HnlFOe11nDBijp2dB4qVBiShXDIWF5X\nCbPk70nnOBWNc2IoxsnhcYbGE4zEEpwcjnGgL0psIslwLHHOOXWVEeory875s7IsTChkhAzCZpgZ\nNRVhGqvKqK8qo7G6nPrKCJGw4RxMNXamUpRzLmX77JGpcpFwiOrysP8Tobo8TFk4RMgA8wYnej/e\nbbmQvy8cMsrDIUIh3W6T/MgmUawCDqe87gEun6mMcy5hZoNAs7//36edu8rfTldnM3DaOZdIUz7n\nbv7OM/ysu4+J5CTxhP+TnDzz2+ey6jKuvmQ1b1zbqHveJSxkRnNtBc21FXRQ/6rjzjnGJpKcik4w\nMBrnVDTO4NgE4xNJxhOT9I7E6Dk1ykTS4XBMOu+cSQfxxCTjE0mK4QHe8kiIykiIyjIvwWRjpskY\n0+1NV9ThvKToH59Khmf/9Lfd2TrtTNLzkr2ZEU5JgN4+0P+47Nz9kctY11yT1/fIJlGk+/ua/k9m\npjIz7U/3rzhT+VcHZXYjcKP/csTMutKVm4MW4Jw5r18BnlpgpXnyqliLXCnFW0qxQmnFW0qxQonE\nu/5Pz2zOJ9512RTKJlH0AGtSXq8Gjs5QpsfMIkADMDDLuen29wGNZhbxWxXp3gsA59ydwJ1ZxJ8V\nM9vjnNuSq/ryqZRihdKKt5RihdKKt5RiBcWbKpv26W5gk5ltMLNyvM7pXdPK7AKu97evBh5yXpt2\nF7DdzCr8p5k2AU/MVKd/zsN+Hfh1fm/+H09ERBZq1haF3+dwE/Ag3qOsX3XO7TWzzwB7nHO7gLuA\nb/id1QN4X/z45e7F6/hOAB93ziUB0tXpv+WfATvN7K+AX/p1i4hIQEwri3nM7Eb/dlbRK6VYobTi\nLaVYobTiLaVYQfGeU7cShYiIZKK5nkREJKMlnyjMbKuZdZlZt5ndHFAMa8zsYTN73sz2mtkn/P1N\nZvYjM3vR/3OZv9/M7HN+zM+Y2SUpdV3vl3/RzK6f6T1zFHfYzH5pZt/3X28ws07/ve/xH1TAf5jh\nHj/eTjNbn1LHLf7+LjO7Kk9xNprZfWa237/Gbynma2tmf+z/O3jOzL5tZpXFdG3N7KtmdtLMnkvZ\nl7PraWaXmtmz/jmfM5v/IKYZYv2s/2/hGTP7rpk1phxLe81m+p6Y6e8ll/GmHPukmTkza/FfF+7a\neoNhluYPXkf6S8B5QDnwNNARQBxtwCX+dh3wAtAB/DVws7//ZuA2f/s9wA/wxp28Gej09zcBB/w/\nl/nby/IY958AO4Dv+6/vBbb7218CPuZv/wHwJX97O3CPv93hX/MKYIP/dxHOQ5x3Ax/1t8uBxmK9\ntngDTA8CVSnX9PeK6doCvwZcAjyXsi9n1xPvyci3+Of8AHh3jmN9FxDxt29LiTXtNSPD98RMfy+5\njNffvwbv4Z9XgJZCX9u8fIGUyo9/wR5MeX0LcEsRxPU9vHmwuoA2f18b0OVv/wNwbUr5Lv/4tcA/\npOw/p1yOY1wN/BvelCvf9//h9aX8Bzxzbf1/4G/xtyN+OZt+vVPL5TDOerwvXpu2vyivLWdnOWjy\nr9X3gauK7doC6zn3yzcn19M/tj9l/znlchHrtGO/DXzL3057zZjheyLTv/lcx4s3NdIbgJc5mygK\ndm2X+q2ndNOT5G3KkGz4tw7eCHQCK5xzxwD8P5f7xWaKu5Cf52+BPwUm/deZpl85Z4oXIHWKl3zH\nex7QC3zNvNtkXzGzGor02jrnjgB/AxwCjuFdqycpzmubKlfXc5W/PX1/vnwE7zdrZokp3f6CTDlk\nZu8Djjjnnp52qGDXdqkniqynDCkEM6sFvgP8kXNuKFPRNPvmNAXKQpjZe4GTzrkns4gp07FCxBvB\na8p/0Tn3RiCKd2tkJkFf22V4k2luwJtxuQZ4d4b3DjTeLMw1voLFbWafwhvf9a2pXXOMKe+xmlk1\n8Cng0+kOzzGuece71BNFNtOTFISZleEliW855/7J333CzNr8423ASX//THEX6vO8DXifmb2Mt3bI\nFXgtjEbzpnCZ/t5n4rLsp3jJlR6gxznX6b++Dy9xFOu1/XXgoHOu1zk3AfwT8FaK89qmytX17PG3\np+/PKb+D973AB51/H2YesZ6ZciiPsZ6P90vD0/7/t9XAL8xs5Tzinf+1zdU9y1L8wftt84D/FzHV\nSbU5gDgMb+Gnv522/7Oc20H41/72b3BuJ9YT/v4mvPvxy/yfg0BTnmN/O2c7s/8f53bs/YG//XHO\n7XC919/ezLmdhwfIT2f2T4HX+Nt/4V/Xory2eLMo7wWq/RjuBv5zsV1bXt1HkbPriTfFz5s52+H6\nnhzHuhVvtojWaeXSXjMyfE/M9PeSy3inHXuZs30UBbu2efsCKZUfvCcHXsB7quFTAcXwK3hNwGfw\nJqx9yo+rGa/D+EX/z6m/bMNb+Okl4FlgS0pdHwG6/Z8PFyD2t3M2UZyH91RFt/8fqMLfX+m/7vaP\nn5dy/qf8z9HFAp5umSXGi4E9/vX9Z/8/T9FeW+Avgf3Ac8A3/C+uorm2wLfx+k8m8H5LvSGX1xPY\n4n/2l4DPM+1BhBzE2o13D3/q/9qXZrtmzPA9MdPfSy7jnXb8Zc4mioJdW43MFhGRjJZ6H4WIiMxC\niUJERDJSohARkYyUKEREJCMlChERyUiJQkREMlKiEMmCP533j83sKTO7xsz+yJ9eIdM5L6dMCf34\nLGW3mNnnchmzSK7Muma2iADeRI1lzrmLwUsCwDeB0WxOds69dZbje/AGBYoUHbUoZMkysxozu9/M\nnjZvkaBr/AVq9pvZz/yFXb5vZsvxksLFfoviE3gT9j1sZg9n+V4j/p/3mNl7UvZ/3czeb2Zvt7ML\nQP2Fv4DNI2Z2wMz+MKX8f/fj+5F5ixp9MpfXRCQdJQpZyrYCR51zb3DOvRb4IfBl4DeBXwVWAjjn\nTgIfBX7qnLvYOfd3eJOpvcM59445vudO4BoAfzW0K4EH0pS7EG8disuA/2FmZWa2BXg/Xuvmd/Cm\nYxDJOyUKWcqeBX7dzG4zs1/Fm/TtoHPuRefNbfPNPLznD4ArzKwCb/rwR51zY2nK3e+ciznn+vBm\nYl2BNyfY95xzY865YeBf8hCfyKsoUciS5Zx7AbgUL2H8b+B95HnNBufcOPAIXmvhGrwWRjqxlO0k\nXn/ivNeOFlkIJQpZssysHRh1zn0Tb1W5twIbzOx8v8i1GU4fxlvffD52Ah/Gu7314BzO+xnwm2ZW\n6S9y9RvzfH+ROdFTT7KUvQ74rJlN4k3r/DGgBbjfzPrwvphfO8O5dwI/MLNj8+in+Fe89Ud2Oefi\n2Z7knNttZrvw1kN4Be8pqcE5vrfInGmacZEZmNnbgU86594bdCxTzKzWOTfij+F4FLjROfeLoOOS\nxU0tCpHScqeZdeAtWHS3koQUgloUIgtkZp14q9Cl+pBz7tkg4hHJNSUKERHJSE89iYhIRkoUIiKS\nkRKFiIhkpEQhIiIZKVGIiEhG/x9/YDxiibCfqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cfa37b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_data['sqft_living'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a52e400>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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si9PMLA/qCYTFwLaax9s5+sP7SJ2IKAP7gY569k3DS+8BNtZ/2NPj4MAEQ0aph+CL08ws\nD+oJBI1SFnXWGXdfSXOArwOfi4gDo765dIOkLkldPT09dRxu/fpK5XF7CE3FAk0FuYdgZrlQTyBs\nB5bWPF4C7BirjqQmYD6wZ7x9JTWThcFXI+JvxnrziLgrIlZFxKrOzs46Drd+h0qVcecQIC1w59NO\nzSwH6gmETcAKScsltZBNEm8YUWcDcF3avgZ4OCIila9NZyEtB1YAj6X5hbuB5yPij6ajIVPRN8FZ\nRpCdaeTTTs0sD8b/95hsTkDSTcBDQBG4JyI2S7oN6IqIDWQf7vdJ6ibrGaxN+26WtB54juzMohsj\noiLpg8AvAs9IejK91W9FxIPT3cDxHBwYf8gIsmsR3EMwszyYMBAA0gf1gyPKbq3Z7gc+Oca+twO3\njyh7lNHnF06oQ6XKmFcpV7U1eQlsM8uH3F6pPFAeYqAyRHvL+ENGrc1Fn2VkZrmQ20CYaB2jqram\ngnsIZpYLuQ2EvjoDodWTymaWE7kNhEMD2Yf8RKedtqVJ5eykKTOzxpXbQBjuIUxw2mlTkWA4QMzM\nGlVuA6E6h1DPaacAvf3l435MZmYzKbeBcCjdPnP2RENG6SY5faXB435MZmYzKbeB0FfKhoAm6iG0\npR7CAfcQzKzB5TYQDtY5h9Ba7SE4EMysweU3EAbqvA4h3RPBcwhm1ujyGwilMsWCaB3jfspV1Ull\nzyGYWaPLcSBUaG8pki28OrbqpLJ7CGbW6HIcCOUJh4vAp52aWX7kNxAG6guEgkRLU+HIhWxmZo0q\nt4HQV8fS11VtTQV6+z2HYGaNLbeBcKhUnnDp66rW5qJ7CGbW8HIbCH11ziFAtYfgQDCzxpbbQKjn\n9plVbc1FB4KZNbzcBsKhUoXZ9Q4ZeQ7BzHIgt4HQV5pcD8FzCGbW6HIZCOXKEKXyUP1zCB4yMrMc\nyGUgHEw3u5nMkNGhgQqVId81zcwaVz4Doc6b41RVF7jziqdm1sjqCgRJqyVtkdQt6eZRnm+VdH96\nfqOkZTXP3ZLKt0i6qqb8Hkm7JD07HQ2ZjOHbZ9YXCNUF8Hq9wJ2ZNbAJA0FSEbgTuBpYCVwraeWI\natcDeyPiAuCLwB1p35XAWuBiYDXwpfR6AH+Ryk643X0lADrmtNRV30tgm1ke1NNDuAzojoitETEA\nrAPWjKizBrg3bT8AXKlsGdE1wLqIKEXEK0B3ej0i4vvAnmlow6Tt7hsAoHNOa131h5fAdiCYWeOq\nJxAWA9tqHm9PZaPWiYgysB/oqHPfE253b9ZDWFRnIAwvge0hIzNrXPUEwmg3DBh5us1YderZd/w3\nl26Q1CWpq6enZzK7jqmnr0RTQcyf1VxX/erkc08KEjOzRlRPIGwHltY8XgLsGKuOpCZgPtlwUD37\njisi7oqIVRGxqrOzczK7jml3b4mOOS0UCuPfHKdq3qxmCoIf7T08Le9vZnYyqicQNgErJC2X1EI2\nSbxhRJ0NwHVp+xrg4YiIVL42nYW0HFgBPDY9hz51u/tKdM6tb7gIoFgQZ8xrY/s+B4KZNa4JAyHN\nCdwEPAQ8D6yPiM2SbpP0s6na3UCHpG7gN4Cb076bgfXAc8C3gBsjogIg6a+AfwXeJmm7pOunt2lj\n2903UPf8QdXiBbPcQzCzhlbXifgR8SDw4IiyW2u2+4FPjrHv7cDto5RfO6kjnUa7+0q87cy5k9pn\n8cJZPP7a3uN0RGZmMy93VypHBLv7SlPqIbyxv9/LV5hZw8pdIOw/PMhgJVhU50VpVYsXzqI8FOzq\n7T9OR2ZmNrNyFwjVq5QnM6kMcPaCWYDPNDKzxpW7QOjpndxVylVLqoHgM43MrEHlLhCqPYRFk+wh\nLF6YBcJ29xDMrEHlLhB6JrlsRdXsliYWzm5mh3sIZtagchcIu/tKFAtiQZ3LVtRavHCWh4zMrGHl\nMhAWTWLZilpnz/fFaWbWuHIYCJO/Srmq2kPIVuUwM2ssuQuEnt7JX5RWtXjBLA4NVNh3yMtgm1nj\nyV0gTOUq5aolC33qqZk1rlwFQkTwZt8Ai+ZO7irlqsULZgMOBDNrTLkKhAOHywxUhiZ9UVpV9VoE\nTyybWSPKVSD0THHZiqqFs5tpay64h2BmDamu5a8bxVQvSquSxOIFs3jtzYPTcjz9gxVee/MQPz7Q\nz+6+EuVKEAQRw/cZFVCQkLL3rwwNcbBU4dBAmb70vbWpQMecVs6a38al5yxkycJZSJM/rdbM8i1X\ngXBk2YopBgLABy9YxNcee50f7TvM4rS+Ub3+/Hsvs3V3H1t7DrJ972F29fZzLKtpFwuipVigPDTE\nYGX4hc6c18YHzu/gJ87v4IMrFnHW/Mkdp5nlU04DYWqTygA3fPh8vrrxde7655f5/Jp3jFt3sDLE\n46/t5ZEtu/jnLT288EYvALOaiyw9bRYXndXJGfPamN/WzJy2JooFUf2/vvoffvWah2qvQYLWpgIt\nTQWaCoUjdQYqQ+w5OMBrbx7ild0H+fbmN/jGEz8C4LzOdi4/fxHvPXchlyxdwLkds92DMLOj5C4Q\nigWxcPbUA2Hxgln8/KWLWbdpGzddseKo+YjByhCf37CZJ7ft46VdfZTKQxQlzl00m9UXn8kFp8/h\nzPltFKbxA1kSrU1Fzpo/i7Pmz+L953UwFMGPD/Tz8q4+unv6uH/TNu77wWtA1oP46EWdfHzlGXz4\nwtMpTuGqbTNrPLkKhCde38f5ne1TWrai1mc+cgEPPL6dLz+6lVuufjsAW97o5a+7tvG3T/6I3X0D\nzGlt4l1L5nPhGXM5v3MObc3F6WhC3QrSkYD44IpOKunmPq/vOUT3rj7+5oc/4q8e28Zp7S1cfn4H\n7z33NH7p8mUn9BjN7OSSm0Do7R/ksVf2cP1PLj/m11q+qJ2fftfZ3PPoK3zr2TcYimDbnsM0F8WV\nF53B6XNbWXHG3JPqP+9iYTgg3re8g/LQEM/v7OXRl3r4+6d38r0Xe2gqik/9m6U0F3N18pmZJbkJ\nhEdf2k15KLjibadPy+v95lVvY3ZzkRfeOMBQwLveuYB3L13AnNZT40faVCjwzsXzeefi+dmcw3Nv\n8Dt/+yx/9r2X+U8/uZx/t2op7adIW8xseuTmL/7hF3Yxr62J9567cFpeb+lps7njmnfxtY2vT8vr\nzaTli9q54SfP4+wFs7jzkW4+//fP8cXvvMjHV57Jx1eewQfO72D+FJYLN7NTSy4CYWgoeGRLDx+6\nsJOmKQ6HNMIH/3gksXN/Pz9/6RJWnbuQja/s4cFndvL1H24Hssn0t581l5VnzePt6euc02Yf83yM\nmZ08chEIz+7Yz+6+EldcND3DRY3unI52zulopzIUvPbmQbbtPczO/Yd5evt+Hn5h15FrJ9pbirzt\nzLmsPHs4JC46cy6zW3Lxa2XWcOr6y5W0GvgToAh8OSL+YMTzrcBfAu8F3gQ+FRGvpuduAa4HKsCv\nR8RD9bzmdHr4hV1I8OELO4/XWzSkYkGc1zmH8zrnHCkbrAzx4wP9nNsxm+d39vLczgP83ZM7+MoP\nsh6UBEsXzuaC0+dwfmc7F5w+J23PYcExnO5rZsffhIEgqQjcCXwc2A5skrQhIp6rqXY9sDciLpC0\nFrgD+JSklcBa4GLgbOC7ki5M+0z0mtPmkRd2ccnSBXQcwxXKlmkuFliycDaVIbjwjLlceMZc1rz7\nbPYdGmTn/n52HjjMrgMlnttxgO+/2EO55lLsRXNaOOe02Zw5v40z5rVx5rzse8ecFhbObmH+rGYW\ntrfQ3lL0hXNmM6CeHsJlQHdEbAWQtA5YA9R+eK8BfjdtPwD8qbK/6DXAuogoAa9I6k6vRx2vOS0O\nD1To6S1x7WXnTPdLWyKJhe0tLGxvYeXZ846UD0Ww79Agu3r76ektsau3xN5DA7y+5zAH+gcZKA+N\n+nrNRTF/VgsLZ2dXcLc1FWlrLtDWXKz5So/f8tyIOk3Z46aiKEgUC6KQ1oQqSkfWiMrKs+cKhfRc\nIavflJ5rKsjzJTYjKkPBwYEy89qO/4kd9QTCYmBbzePtwPvGqhMRZUn7gY5U/oMR+y5O2xO95rSY\n1VLkX26+goHK6B8+dvwUJE5rb+G09hYuOvPo5/sHKxw4PMjBgQqHB7KF+g4PVjg0UP0q09tfZk9l\ngHIlGKwMMVgZyraHhhgsB5UTfDvTajAUa0KiNieqPZvhJUhq99ZRZSPrDS9eMnLfYzPdUXYy9+Cm\n+9COd1PH+hWOgL5SmQP9g5w+t5WNv/Wx43sg1BcIo/04RjZhrDpjlY92qs+oPxZJNwA3pId9kraM\ncZzTZRGw+zi/x8nCbW1MeWlrXtrJq7BIvz3ltp5bb8V6AmE7sLTm8RJgxxh1tktqAuYDeybYd6LX\nBCAi7gLuquM4p4WkrohYdaLebya5rY0pL23NSzvhxLW1npPyNwErJC2X1EI2SbxhRJ0NwHVp+xrg\n4ciW6dwArJXUKmk5sAJ4rM7XNDOzE2jCHkKaE7gJeIjsFNF7ImKzpNuArojYANwN3JcmjfeQfcCT\n6q0nmywuAzdGRAVgtNec/uaZmVm9FCd4Uu5kJ+mGNEzV8NzWxpSXtualnXDi2upAMDMzoL45BDMz\nywEHQg1JqyVtkdQt6eaZPp6pkHSPpF2Snq0pO03SdyS9lL4vTOWS9L9Te5+WdGnNPtel+i9Jum60\n95pJkpZKekTS85I2S/psKm/EtrZJekzSU6mtn0/lyyVtTMd9fzpBg3QSx/2prRslLat5rVtS+RZJ\nV81Mi8YnqSjpCUnfTI8btZ2vSnpG0pOSulLZzP7+RoS/smGzIvAycB7QAjwFrJzp45pCOz4EXAo8\nW1P2P4Gb0/bNwB1p+6eAfyS7XuT9wMZUfhqwNX1fmLYXznTbRrTzLODStD0XeBFY2aBtFTAnbTcD\nG1Mb1gNrU/mfA59J278K/HnaXgvcn7ZXpt/rVmB5+n0vznT7RmnvbwBfA76ZHjdqO18FFo0om9Hf\nX/cQhh1ZoiMiBoDqchqnlIj4PtmZXrXWAPem7XuBn6sp/8vI/ABYIOks4CrgOxGxJyL2At8BVh//\no69fROyMiB+m7V7gebKr4BuxrRERfelhc/oK4AqypWLg6LZWfwYPAFdKb11KJiJeAWqXkjkpSFoC\n/DTw5fRYNGA7xzGjv78OhGGjLdGxeIy6p5ozImInZB+kQHUd8LHafEr9LNJQwXvI/nNuyLamYZQn\ngV1kf/QvA/siopyq1B73W5aSAWqXkjnZ2/rHwG8C1bVmOmjMdkIW6t+W9LiyFRlghn9/vXD9sHqW\n6Gg0k11y5KQjaQ7wdeBzEXFAYy88c0q3NbLrdy6RtAD4BvD20aql76dkWyX9DLArIh6X9JFq8ShV\nT+l21rg8InZIOh34jqQXxql7QtrqHsKwepboOFX9OHUvSd93pfKx2nxK/CwkNZOFwVcj4m9ScUO2\ntSoi9gHfIxtHXqBsqRh463EfaZPqX0rmZHA58LOSXiUbsr2CrMfQaO0EICJ2pO+7yEL+Mmb499eB\nMKyRl9OoXVrkOuDvasr/QzqD4f3A/tRNfQj4hKSF6SyHT6Syk0YaK74beD4i/qjmqUZsa2fqGSBp\nFvAxsjmTR8iWioGj2zqZpWROChFxS0QsiYhlZH9/D0fEp2mwdgJIapc0t7pN9nv3LDP9+zvTM+0n\n0xfZTP6LZOOzvz3TxzPFNvwVsBMYJPvv4XqycdV/Al5K309LdUV2o6KXgWeAVTWv8x/JJuO6gV+e\n6XaN0s4PknWNnwaeTF8/1aBtfRfwRGrrs8Ctqfw8sg+6buCvgdZU3pYed6fnz6t5rd9OP4MtwNUz\n3bZx2vwRhs8yarh2pjY9lb42Vz9vZvr311cqm5kZ4CEjMzNLHAhmZgY4EMzMLHEgmJkZ4EAwM7PE\ngWBmZoADwXIgXaD03bTM8KckfU7S7An26Rvv+ZORpAWSfnWmj8NOXQ4Ey4P3AM0RcUlE3A98Dhg3\nEE5RC8iWhDabEgeCnZLSpf//oOymMc+m//xXS3pB0qPpZiLfTAuHfYVsYbgnld1I52zgEUmPTPAe\n/0vSDyX9k6TOVPafJW1K7/v1ak9D0ifTcTwl6fuprCjpC6n+05L+Syr/iKR/lrRe0ouS/kDSp5Xd\nBOcZSeenep3pPTalr8tT+e8quxHS9yRtlfTr6ZD/ADg/tfML0/9Tt4Y305dw+8tfU/kC/i3wf2oe\nzydbBngF2WX+6xle+uAj1e3irihlAAACTElEQVT0+FVG3JhklNcP4NNp+1bgT9N2R02d3wN+LW0/\nAyxO2wvS9xuA30nbrUAX2Q1bPgLsI7vJTyvwI+Dzqd5ngT9O218DPpi2zyFbtwngd4H/l/ZdBLxJ\ndo+EZdTcGMlf/prsl5e/tlPVM8AfSroD+CbQC7wSES8BSPoK2QfyVA0B96ftrwDV1VTfIen3yIZn\n5jC8kNi/AH8haX1N3U8A75JUXZhtPllgDQCbIq17L+ll4Ns17fpo2v4YsLJmSe951QXRgH+IiBJQ\nkrQLOOMY2moG+H4IdoqKiBclvZdsQbvfJ/tAPZ4Lc1Vf+y+An4uIpyT9Etl/+0TEr0h6H9ndvp6U\ndAlZT+XXIuItq0+mtf5LNUVDNY+HGP67LAAfiIjDI/ZnxP4V/Lds08BzCHZKknQ2cCgivgL8IfAT\nwPLq+Dtw7Ti795Ldh3k8BYaXXP4F4NG0PRfYme7F8Oma4zk/IjZGxK3AbrI16h8CPpPqIunCtNRx\nvb4N3FTzHpdMUL+edpmNyf9V2KnqncAXJA2RLfX9GbLx9H+QtJvsA/wdY+x7F/CPknZGxEfHqHMQ\nuFjS42S3ZvxUKv8fZLfqfI1seKf6AfwFSdX5i38iW9b4abJx/R+m+zf0MHyP3Hr8OnCnpKfJ/la/\nD/zKWJUj4k1J/yLpWeAfI+K/T+K9zLz8tTWmNCzz3yLiZ2b6WMxOFR4yMjMzwD0EyzlJG8lO36z1\nixHxzEwcj9lMciCYmRngISMzM0scCGZmBjgQzMwscSCYmRngQDAzs+T/A9TgGCgW7tdYAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1273ce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_data['sqft_basement'].dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observe the distribution of categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1273cc88>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6+0XAR4DfWeiBzteNf72XKHZmGjG33j/FtjMm+fwjz/HiyTm+9MQRtp0xWbrr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b21f9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_data['bedrooms'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b39d5f8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k56YhuM/IwQOU/7lyJ41tnXyij28kV86qYm71KB5a9s7WqIxM/Qa3mT0EvATMMrPtZvbZ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\n8/8ByUzKi5bHmwAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b470748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_data['condition'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b518780>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+7jp/Kv95w+mDTgZdIsy4riiP+OhInl59kJb2od+v8OrmSnZXH+fuC6ZrqKmE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\nBFBCEBERjxKCiIgASggiIuL5/yj6E2shbjNPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b664518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_data['yr_built'].dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observe the distribution of target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b664b70>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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73T0FbAHWFdRZBzwYbj8KXG1mFsq3uPuwu78O9IV4RWOGc64KMQgxb5hgG2PJ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\n04oSl4iITCtKXCIiMq38/w7KpcNVWBIVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b668550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_data['price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Relationship with numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b69be48>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+/pHxRCOZqeZARq3FfMvK72iL8Y171nZdm4zFk3z9nrU2ojI1p2Ij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\nGBNYFqSMMcYElgUpY4wxgfX/AXXTwN9Yz7Z9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b8898d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'sqft_living'\n",
    "data = pd.concat([df_data['price'], df_data[var]], axis=1)\n",
    "data.plot.scatter(x=var, y='price', ylim=(0,8000000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b874c88>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y5SffpbzewAOq+gsAEQk62YFyDcwwqhHbW2YY5Sdfi+l5PEUUozfwx3xOFJGgiLwuIk+5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\nRPyDDpNIjKxLnH8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M/uEQD109iQnDB5Ylai71monKNHWc1bg8Vuj/gVHdVGW4eE8lU327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\nDKOqMMVkGIZhVBX/P9El9HyVzzPTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1bc7f2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'sqft_basement'\n",
    "data = pd.concat([df_data['price'], df_data[var]], axis=1)\n",
    "data.plot.scatter(x=var, y='price', ylim=(0,8000000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Relationship with categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W4P156rPASRuwhxkW1/dyr9XA6jz1m4DZ/dGGiIiIlEYrVoqIiEgsSiJEREQkFiURIiIi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\n47bKzHgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b954da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'grade'\n",
    "data = pd.concat([df_data['price'], df_data[var]], axis=1)\n",
    "f, ax = plt.subplots(figsize=(8, 6))\n",
    "fig = sns.boxplot(x=var, y=\"price\", data=data)\n",
    "fig.axis(ymin=0, ymax=8000000);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s2TJ27do18sEiUvOURIjIgLa2NjZs2EBbW1vUoYhIFVASISJAehZizZo1uDvt7e2ajRCR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b509940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'bedrooms'\n",
    "data = pd.concat([df_data['price'], df_data[var]], axis=1)\n",
    "f, ax = plt.subplots(figsize=(8, 6))\n",
    "fig = sns.boxplot(x=var, y=\"price\", data=data)\n",
    "fig.axis(ymin=0, ymax=8000000);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check the correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1bb1d6a0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DJTksyQ+S3JzkLWNs/4ckNya5PskFSR7XtW1l8/r3a5MsnIzxWEGVJEkaoEHf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\n/wf+8tnb4/yT7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1bc9e5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = df_data.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "sns.heatmap(corrmat, vmax=.9, square=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sqft_living is a sum of sqft_above and sqft_basement, therefore sqft_living is strongly correlated to sqft_above"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove duplicate observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['price',\n",
       " 'bedrooms',\n",
       " 'bathrooms',\n",
       " 'sqft_living',\n",
       " 'sqft_lot',\n",
       " 'floors',\n",
       " 'waterfront',\n",
       " 'view',\n",
       " 'condition',\n",
       " 'grade',\n",
       " 'sqft_above',\n",
       " 'sqft_basement',\n",
       " 'yr_built',\n",
       " 'yr_renovated',\n",
       " 'zipcode',\n",
       " 'lat',\n",
       " 'long']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cols = [col for col in df_data.columns if col not in ['id', 'date']]\n",
    "df_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of observations before removing duplicates: 21613\n",
      "No. of observations after removing duplicates: 21608\n"
     ]
    }
   ],
   "source": [
    "print('No. of observations before removing duplicates: %s' % len(df_data))\n",
    "df_data = df_data.drop_duplicates(subset=df_cols, keep='first')\n",
    "print('No. of observations after removing duplicates: %s' % len(df_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove faulty observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of observations before filtering faulty observations: 21608\n",
      "No. of observations after filtering faulty observations: 21592\n"
     ]
    }
   ],
   "source": [
    "print('No. of observations before filtering faulty observations: %s' % len(df_data))\n",
    "df_data = df_data[(df_data.bedrooms != 0) & (df_data.bathrooms != 0)]\n",
    "print('No. of observations after filtering faulty observations: %s' % len(df_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of observations before removing outliers: 21592\n",
      "No. of observations after removing outliers: 21186\n"
     ]
    }
   ],
   "source": [
    "print('No. of observations before removing outliers: %s' % len(df_data))\n",
    "df_data = df_data[np.abs(df_data.price - df_data.price.mean()) <= (3 * df_data.price.std())]\n",
    "print('No. of observations after removing outliers: %s' % len(df_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Identify and fix Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QnR04Jss/l+6zoATMbDLgteQ1kO07EdgBhTu+lO139+uQV9/0wENm9hXwCJpI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\nXP+N3bt2xTV/9Edx82WX5fpPxMQjjsj22TkphAEAAAAcPL8eESMi4vM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evfPO\nDJnkpxAGAAAA0GCTFi6sHTPlqKMyZJJfd7sTAAAAYK9lRdHuFAbMxcuXtzsFoA8OnTSpdszICRMy\nZJKfQhhSd97vAAAgAElEQVQAAEAHWVaW7U4BaJj+THw/eurUDJnkZ2gkAAAAQIM9fMsttWMeuumm\nDJnkpxAGAAAA0GDDx4ypHTNYh0YqhAEAAAA0WPeIEe1O4aBRCAMAAABosLIfcxN2HXJIhkzyM1k+\nAAAAQIMtPv/8+MX//b8xcsKEGD1tWkRUxbGqQNbzvOdxzoteFIvPP7+dKfebQhgAAABAgxXDhkVE\nxK7t22PHli0RRbH/7YpinwLZYKQQBgAAANBgd3zpSxERsWPLllQIO4AV//iPMef002PpBz+YO7UB\nZ44wAAAAgAbbvWtX7Zhhg3SOMIUwAAAAgAYbP39+/Zh58zJkkp9CGAAAAECDjZ87t3bMxAULMmSS\nn0IYAAAAQIM9vW7dQYnpBAphAAAAAA126OTJtWNGjBuXIZP8FMIAAAAAGmzH5s21Y7Zu2JAhk/wU\nwgAAAAAabPMjj9SOWb9yZYZM8utudwIAAAAAtM+i88+P27/0pXjln/95jJ83L4qi2P+GRRFRljFu\n7tyYd8YZBzfJAaIQBgAAAEAsOvfcmLZkSbvTyEohDAAAAKDBdu/cGRERf3vaaTFu9uwohg2Loqsr\niqKIoqsronrsGjYsoihi3plnxq996lPp9SCjEAYAAADQYLd/8YsREbHzmWf6NPfXwzffHNOOPTZO\nfs97cqc24EyWDwAAANBgo6ZMqR1TdA3OktLgzBoAAACAATF76dLaMfPOPDNDJvkphAEAAAA02KYH\nH6wds3XjxgyZ5KcQBgAAANBg6++7r3bME/2I6QQmywcAAOggy4qi3SkMmIuXL293CkAfjJw4sXbM\niHHjMmSSn0IYAABAB1lWlu1OAWiYR26/vXbM2p/9LBafd16GbPIyNBIAAACgwaYde2ztmDn9mGC/\nEyiEAQAAADRY3TtATl60yF0jAQAAABh8nrj33trbr1+5MlM2eSmEAQAAADTYtk2basc8s359hkzy\nUwgDAAAAaLB7v/3t2jH39COmEyiEAQAAADTYovPPrx9z7rkZMslPIQwAAACgwbb2Y5jj5kceyZBJ\nfgphAAAAAA22+dFHa8ds3bgxQyb5KYQBAAAANNhhL3957ZgjXvWqDJnkpxAGAAAA0GCHjBpVP2b0\n6AyZ5KcQBgAAANBgz/RjjrBtTz6ZIZP8FMIAAAAAGuyptWtrx6xfuTJDJvkphAEAAAA02OgZM2rH\njJ09O0Mm+SmEAQAAADTYTZdeWjvm3z7+8QyZ5KcQBgAAANBgi849t3bM8e94R4ZM8lMIAwAAAGiw\nEePG1Y4ZNWVKhkzyUwgDAAAAaLB1t95aO+bB66/PkEl+CmEAAAAADbb49a+vHXPsW96SIZP8FMIA\nAAAAqKXoGpwlpe52JwAAAMBey4qi3SkMmIuXL293CkAf7Hzmmdox2zZtypBJfgphAAAAHWRZWbY7\nBaBh7vvud+vHXHVVzDjxxAzZ5DU4+7EBAAAAMCBOfNe7asec9O53Z8gkP4UwAAAAgAZbv2pV/ZiV\nKzNkkp9CGAAAAECDdY8ceVBiOoFCGAAAAECDbX7kkfoxjz6aIZP8FMIAAAAAGuyJe+6pHfPYnXdm\nyCQ/hTAAAACABlt03nm1Y46+4IIMmeSnEAYAAADQYOYIAwAAAKAR1v70p7VjHvzJTzJkkp9CGAAA\nAECDHTJ6dO0YPcIAAAAAGHTGz5tXO2bSggUZMslPIQwAAACgwYaPGXNQYjqBQhgAAABAgz104421\nY9bccEOGTPLrbncCAAAAALTP4te/Pu74ylfizd/4RkxauDCtLMsoy3Lf59XjoZMmxcTDD29fws+D\nQhgAAABAg23dsCEiIi6/4II+bT96+vT4wJ13xqjJk3OmlYWhkQAAAAANtvKqq2ptv/mRR2Ll976X\nKZu8FMIAAAAAGmzSkUfWjjE0EgAAAIBBZ9Zpp0VExBsvvzwmL1oURVFEFEV6jNj7vHocMX58jJ05\ns40Z959CGAAAAECDrbvttoiI+Kc3v7nPMe+75ZaYedJJuVLKxtBIAAAAgAbrT++u0dOmZcgkP4Uw\nAAAAgAZ78Prra8esueGGDJnkZ2gkAAAAQIMtPOecWPHVr0YURYyeOjXKsowoyz2PEbHPunlnnhkL\nzz67zVn3j0IYAAAAQIM9/fDD6UlZxuZHHz3g9vd997ux+dFHY/ggvHOkoZEAAAAADXbdxz9eO2b5\nH/9xhkzyUwgDAAAAaLBjLrywfswb35ghk/wUwgAAAAAabOapp9aOmfOiF2XIJD+FMAAAAIAG2/70\n0wclphMohAEAAAA02I4tW2rH7Ny6NUMm+SmEAQAAADTYxCOOqLX93DPOiPHz5mXKJi+FMAAAAIAG\nq9u769EVK2LXtm2ZsslLIQwAAACgwbZu2FBr+21PPhlbN27MlE1e3e1OAAAAAID2mbhgQUREXLJi\nRUxbsqTN2eSlEAYAAADQYOXu3RERcc1HPhLj5s6NKIooiuJZH6ccfXSc/J73pHWDjEIYAAAAQIOt\nXr48IiLu/c53+hwzbvbsOPI1r8mVUjbmCAMAAABosHFz5tSOmbRwYYZM8tMjDAAAAKDBJi9eHBHm\nCAMAAABgiCt37YqIiCve9a4YN3v2PnOCRcS/myds+gknxJkf/n/t3XuQn3V9L/DPd7O5bza3zSYh\nm0BukGQDgqAJqIBYFApVoSrei1ZsmdNR8dTROfWSVnt62mlr7TkznjljR63VFntAQBQpYpCiEkEM\nEG4BQiD3kJALuZHbc/7YDQQOJvss+e5v1+/rNeP89vK8N2/88z3P830+Halp4D1oaAgDAAAAKNiK\nW2+NiIi1d90Va++666jXP3D11THxlFPixIsuyl3tmBt40x0AAAAAx0xvzvtqmzMnQ5P8DGEAAAAA\nJauq2pHU/djkQGMIAwAAACjYs+vW1c5sX706Q5P8DGEAAAAABRs0ZEjtzP7nnsvQJD9DGAAAAEDB\nnl2zpnZm58aNGZrkZwgDAAAAKNjI9vbameFjx2Zokp8hDAAAAKBgB/burZ1pam7O0CQ/QxgAAABA\nwUZMmFA7M3LixAxN8huY8x0AAMBvqUUpNbrCMXP54sWNrgD0wO7Nm2tndm3alKFJfoYwAACAfmRR\nVTW6AlCYJ37yk15lZrzpTRna5GUIAwAA6EfcEQb0taGjRvVJpj8whAEAAPQj7ggD+trk00+PNb/8\nZa3MpNNOy9QmL4flAwAAABSsbc6c2pmJJ5+coUl+hjAAAACAgh3cv7925sC+fRma5OfRSAAAAICC\ntXZ0RETEnEsuidapUyOlFJFS12fEC193f7bNnRujp01rYOPeM4QBAAAAFGztr34VEREPf+97Pc5M\nmDs3OhYuzFUpG49GAgAAABSsZdKk2plDd5ENNIYwAAAAgIL97K//unbm9r/8ywxN8jOEAQAAABRs\n6pln1s5Mee1rMzTJzxAGAAAAULC9O3bUzhx47rkMTfIzhAEAAAAUbMeGDbUz21atytAkP2+NBAAA\n6EcWpdToCsfM5YsXN7oC0AMtkybFxvvvr5VpnTIlU5u8DGEAAAD9yKKqanQFoDBPP/hgn2T6A49G\nAgAAABSs813vqp2Z/573ZGiSnzvCAAAAAAo28VWvev7r5mHDIiKiqqqI7jtUD3196PP4c86JSYdl\nBhJDGAAAQD/ijDCgzx32SPb+PXte/pqUIqXU9dk0cB8wNIQBAAD0I84IA/ralhUrjn7RoTvCIuKJ\nW2+NzY8+GpNPOy1zs2Nv4E54AAAAALxivTn4fu3dd2dokp8hDAAAAKBgY2fMqJ2ZeMopGZrkZwgD\nAAAAKNiBvXtrZ37jWWL9nCEMAAAAoGCtHR21MyPa2jI0yc9h+QAAAP2It0YCfe2RG26onXn4uuui\nvbMzQ5u8DGEAAAD9iLdGAn2tN3eEjZ42LUOT/DwaCQAAAFCw3jzmOGry5AxN8jOEAQAAABRsw333\n1c5seuSRDE3yM4QBAAAAFGzHunW1M1tXrjz2RfqAIQwAAACgYKd88IO1M6d96EMZmuTnsHwAAACA\ngo0/8cSIiLhy2bIB+SbIOgxhAAAAAAWrDh6MiIif/NmfRevUqZFSikip6zPiha+7P9vmzo3TPvzh\nF34/gBjCAAAAAAq27p57IiLikeuv73GmvbMzOhYuzFUpG2eEAQAAABRsRFtb7UzL5MkZmuRnCAMA\nAAAo2OARI2pnBg0ZkqFJfh6NBAAAACjY0NbWiOgat0a2t7/oTLCIePGZYSnF1LPOiuFjxzaycq8Z\nwgAAAAAKtvyGGyIi4sDevbF99eqjXr/l8cdj9kUXxfzLLstd7ZjzaCQAAABAwWZecEGt65uHDYuZ\nb35zpjZ5GcIAAAAACrZzw4Za1+/fsyd2b96cqU1ehjAAAACAgv3ks5+tnbnpYx/L0CQ/QxgAAABA\nwU686KLameNe85oMTfJzWD4AAABAwU754Adj+Y03xrl/8RcxeurUo17fOnVqTD/vvD5oduwZwgAA\nAAAKduhNkbd9/vM9zly1alW0dnTkqpSNRyMBAAAACrZr06bamR01D9jvLwxhAAAAAAUbNmZM7czw\nceMyNMnPEAYAAABQsCEtLbUzQ0eNytAkP0MYAAAAQMEWf+5ztTO3fPrTGZrkZwgDAAAAKNjJ731v\n7cwp739/hib5GcIAAAAACrZj3br6mfXrMzTJzxAGAAAAULDxJ51UOzNh3rwMTfIzhAEAAAAUbNzs\n2c9/nZqaIlI64vVTX/e6GDtjRu5aWTQ3ugAAAAAAjTN4+PCIiGidOjVaJk580RiWUopI6UWfHWee\nGYNHjGhk5V4zhAEAAAAU7KFrromIiO2rVsX2VauOev1Td9wR42bPjtOvuCJ3tWPOo5EAAAAABZt9\n8cW1rm+ZNClOed/7MrXJyx1hAAAAAAVrHjYsIiKuXLYs2js7G9wmL3eEAQAAAFAEd4QBAAAAEEu/\n/vUYc8IJXd+85ID8ww/PHzN9esw8//zGFX0FDGEAAAAABVt7990REfGLv/u7Hmf+8M47o2PBglyV\nsvFoJAAAAEDBdm3cWD+zaVOGJvkZwgAAAAAK9tyzz9bO7H7mmQxN8vNoJAAAAEDBOi+7LB665pq4\n5FvfinGzZkVqaorU1NR1PlhT0wtnhFVVREQMHzcuRk+b1uDWvWMIAwAAACjYhnvvjYiI733gAz3O\n/NGvfx2TTj01V6VsDGEAAAD9yKLuN7P9Nrh88eJGVwB6YNRxx9XOtEyalKFJfs4IAwAAACjYiltu\nqZ157Ec/ytAkP3eEAQAA9COLus/gAegrB/fvr53Zs3Vrhib5uSMMAAAAoGDz3/OeWtdPfvWr47V/\n8ieZ2uRlCAMAAAAo2MZly2pdv+6ee2Ldr3+dqU1ehjAAAACAgqWm+vPQoMGDMzTJzxAGAAAAULDW\nqVPrZzo6MjTJzxAGAAAAULDl3/9+7czD112XoUl+3hoJAAAAULBX/cEfxKM/+EE0DxsWI9vbI1KK\nlNLzv09NTRGHfT/j/PPj1R/5SCOqvmKGMAAAAICCHbojbP+ePbHtqaeOev2vHn88Zr75zTH3kkty\nVzvmPBoJAAAAULDenPc1uhfnivUHhjAAAACAgqVBg/ok0x8YwgAAAAAKNmjw4NqZ5qFDMzTJzxlh\nAAAAAAVrmzs3IiLe+KUvxdgZM7oOyj90YP5hnxERKaVo7eiICfPmNbJyrxnCAAAAAAq29u67IyJi\n8Wc/2+PMH955Z3QsWJCrUjYejQQAAAAo2JbHH6+dWbNkSYYm+RnCAAAAAAo2ePjw2plWb40EAAAA\nYKAZ0d5eP9PWlqFJfoYwAAAAgIJtfuSRPsn0B4YwAAAAgILtWLeufmbDhgxN8jOEAQAAABRsQmdn\n/cy8eRma5GcIAwAAAChYa0dH7cyY44/P0CQ/QxgAAABAwQ7u3187s3/PngxN8jOEAQAAABSsbc6c\n2pn2k0/O0CQ/QxgAAABAwdbcdVftzOo778zQJD9DGAAAAEDBenPe19jp0zM0ya+50QUAAAAAaJyx\nM2dGRMSVy5ZFey/eIDmQGMIAAAAAiFU//3ns3LgxUkoRKXV9RrzwdffniAkTYvzs2Y0t20uGMAAA\nAICCLf/+9yMi4saPfrTHmXdff32c9Na35qqUjTPCAAAAAArWNHhw/UzzwLy3yhAGAAAAULDmYcNq\nZ9KgQRma5GcIAwAAACjY2BkzamcmzJ2boUl+hjAAAACAgm15/PHamWceeyxDk/wMYQAAAAAF2/TQ\nQ7UzG+67L0OT/AbmyWYAAAC/pRal1OgKx8zlixc3ugLQAye+9a2x8rbbamVOetvb8pTJzBAGAADQ\njyyqqkZXAAqzb9eu2pkDe/dmaJKfIQwAAACgYONPPDEiIs5ZtCjGzpgRKaWIlLo+I174uvuzderU\naDvppAY27j1DGAAAAEDBln3nOxER8dNFi3qcueRb34pT3v/+TI3ycVg+AAAAQMFmXXhh/cwFF2Ro\nkp8hDAAAAKBgVc2zCYeNGZOpSX4ejQQAAOhHvDUS6Gurf/7zWtfv2bo1nvrZz2LOAHxzpCEMAACg\nH/HWSKCv9eatkXu2bs3QJD+PRgIAAAAUbNrZZ9fOnHDuuce+SB8whAEAAAAUbPvq1bUzO9aty9Ak\nP0MYAAAAQMHGTp9eOzNu1qwMTfIzhAEAAAAUbFXNw/J7m+kPDGEAAAAABevNeV/Hn3POsS/SBwxh\nAAAAAAVb9q//Wjtz/7e/naFJfoYwAAAAgILtf+652pl9u3ZlaJKfIQwAAACgYHMuuaR2Zv67352h\nSX6GMAAAAICCpZRqZ6qqytAkP0MYAAAAQMGeXbu2fmbNmgxN8jOEAQAAABRs25NP1s5sX706Q5P8\nDGEAAAAABZv6+tfXzkxZsCBDk/yaG10AAAAAgMZpmTQpIiKuXLYs2js7G9wmL0MYAAAAQMH27dwZ\nERFfnT//xb/oPkQ/pRSR0vOfJ5x7blx27bUxpKWlr6u+Yh6NBAAAACjY1pUrX/4XVRVRVVEdPBjV\ngQNxcP/+OLhvX6y45ZbfnOnn3BEGAAAAULC2uXMjIqLjzDOjdcqUSE1NkQYNipRSpKamrrvBmpqe\nvyOs/eSTY8IAfYTSEAYAAABQsJW33RYREat/8YseZ9rnz4+Z55+fqVE+Ho0EAAAAKNjyG26onXno\n2mszNMnPEAYAAABQsAWf+ETtzOs/85kMTfIzhAEAAAAUrHnYsNqZQYMHZ2iSnyEMAAAAgHpSanSD\nXjGEAQAAABRs08MP185sfuSRDE3yM4QBAAAAFKx9/vzamba5czM0ya+50QUAAAAAaJwRbW0REXHl\nsmXR3tnZ4DZ5GcIAAAAACrZ+6dKIiPhqjTvDrrj77jju9NNzVcrGEAYAANCPLBqgB1C/nMsXL250\nBaAHtq9aVTuz7cknDWEAAAC8MouqqtEVgMJsfeKJ2pneHLDfHzgsHwAAAKBgJ7397bUzcy+9NEOT\n/AxhAAAAAAXbt2tX7Uw1QO9eNYQBAAAAFOzZNWtqZ3pzrlh/YAgDAAAAKNjG+++vnXligL4MwxAG\nAAAAULB9u3fXzgwZOTJDk/wMYQAAAAAFO/7ss/sk0x8YwgAAAAAKNmjo0PqZIUMyNMnPEAYAAABQ\nsJ0bNtTPPP10hib5GcIAAAAACjahs7N+Zt68DE3yM4QBAAAAFGz3M8/Uzjy3bVuGJvkZwgAAAAAK\ntnXFitqZ9ffem6FJfoYwAAAAgIINaW2tnRk8YkSGJvkZwgAAAAAK9szy5bUzW594IkOT/AxhAAAA\nAAWbsmBB7czUs87K0CQ/QxgAAABAwVbedlvtzIpbbz32RfqAIQwAAACgYGdceWXtzJlXXZWhSX7N\njS4AAAAAQOMcOvh+0qmnxsiJEyOlFJHS878/9P2hz0mnnRZDRo1qVN1XxBAGAAAAULAN990XERHr\nly7t0fXLv//9mH7eeXHCOefkrJWFRyMBAAAACtY0aFDtzLAxYzI0yc8QBgAAAFCwoa2ttTOGMAAA\nAAAGnPu/853amXu/+c0MTfIzhAEAAAAUrDd3hPUm0x8YwgAAAAAKtu3JJ2tntq5ceeyL9AFDGAAA\nAEDBpr7udbUzHQsXZmiSnyEMAAAAoGDjZs2qnWk/+eQMTfIzhAEAAAAU7MDevbUz+/fsydAkP0MY\nAAAAQMFGTJhQOzN83LgMTfIzhAEAAAAUrDcH329fterYF+kDhjAAAACAgrVMmlQ/M3lyhib5NTe6\nAAAAAACNM3rq1K4vUopRkydHamqKNGhQ12dTUzQNGtT1fUoRKUXHwoUxetq0xpbuJUMYAAAAQMEe\nuuaari+qKp5du/ao1z/9wANxwhvfGKe8732Zmx17Ho0EAAAAKFjzsGH1M0OHZmiSnyEMAAAAoGD7\ndu2qndmzbVuGJvkZwgAAAAAKtvrOO/sk0x8YwgAAAAAK9jt/8ze1Mxd8+csZmuRnCAMAAAAo2NMP\nPlg7s/GBBzI0yc8QBgAAAFCwrStW1M5s6UWmPzCEAQAAABTshPPOq52Z3otMf2AIAwAAACjY1pUr\n+yTTHxjCAAAAAAq2f/fu2pkDe/dmaJKfIQwAAACgYBvuu692Zs0vf5mhSX6GMAAAAICCDR01qnZm\nxPjxGZrkZwgDAAAAKNjYmTNrZ8accMKxL9IHmhtdAAAAAIDGmfq618WSr3wlLr/99mibMydSShER\nUVVVdH/R9XX35+Dhw2PYmDENbNx7hjAAAAAAYvi4cTFywoRG18jKEAYAAABQsGfXro2IiK/On9/j\nzCfXrIlRxx2Xq1I2zggDAAAAKNiGpUtrZ9bdc0+GJvkZwgAAAAAK1jZvXv3M3LkZmuRnCAMAAAAo\n2Ppe3N219u67MzTJzxAGAAAAULARvTggv2XixAxN8jOEAQAAABRs6xNP1M+sXHnsi/QBQxgAAABA\nwXpz3lfbnDkZmuRnCAMAAAAo2Mj29voZj0YCAAAAMNBsfvTR2plnHnssQ5P8DGEAAAAABdv8yCO1\nM08/+GCGJvkZwgAAAAAKNub442tnWqdMydAkP0MYAAAAQMF2b9lSO7N3x44MTfIzhAEAAAAUbNRx\nx/VJpj8whAEAAAAUrDdvgByob41sbnQBAAAAABqnff78iIi4ctmyaO/sbHCbvNwRBgAAAEAR3BEG\nAAAAULBn166NiIivdt8Z1hNXrV49IN8c6Y4wAAAAgII9t21b7cyeXrxpsj8whAEAAAAU7LGbbqqd\nWf6DH2Rokp8hDAAAAKBgw8aOrZ0ZOWFChib5GcIAAAAACjZs9OjamcEjRmRokp8hDAAAAKBgQ0aN\nqp0ZNmZMhib5GcIAAAAACtabUcsQBgAAAMDAk1LfZPoBQxgAAABAwXpz3tfQXjxO2R8YwgAAAAAK\ntvuZZ2pndmzYkKFJfoYwAAAAgIKNaGurnRk1eXKGJvkZwgAAAAAKtnLx4tqZx26+OUOT/AxhAAAA\nAAXbt3Nn7cz+3bszNMnPEAYAAABALQf27Wt0hV4xhAEAAAAUbOYFF9TOzL300gxN8jOEAQAAABRs\n16ZNtTPPrl2boUl+hjAAAACAgg0eMaJ2ZkhLS4Ym+RnCAAAAAAq2+ZFHamc2PfRQhib5GcIAAAAA\nCvb4zTfXzjx2000ZmuRnCAMAAAAo2Fv/6Z9qXT983Lh4+z//c6Y2eRnCAAAAAAq2qeajkbufeSY2\nL1+eqU1eh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H1E/O/ub8897Od7q6q6qaqqdT0tUlXV6qqq/rOqqu09zUTEjIjYfvgIdliv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\nBATD3S0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c5129e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import missingno as msno\n",
    "msno.matrix(df=df_data, figsize=(20,14), color=(0.5,0,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>yr_renovated</th>\n",
       "      <td>20336</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>8</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_built</th>\n",
       "      <td>6</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zipcode</th>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Total  Percent\n",
       "yr_renovated  20336     0.96\n",
       "sqft_living       8     0.00\n",
       "yr_built          6     0.00\n",
       "zipcode           4     0.00"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = df_data.isnull().sum().sort_values(ascending=False)\n",
    "percent = (df_data.isnull().sum() / df_data.isnull().count()).sort_values(ascending=False)\n",
    "missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "missing_data[missing_data.Total > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Its plausible to say that sqft_living, yr_built and zipcode cannot have missing values but the same cannot be said for yr_renovated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of observations before removing missing values: 21186\n",
      "No. of observations after removing missing values: 21168\n"
     ]
    }
   ],
   "source": [
    "print('No. of observations before removing missing values: %s' % len(df_data))\n",
    "df_data = df_data.dropna(subset = ['sqft_living', 'yr_built', 'zipcode'])\n",
    "print('No. of observations after removing missing values: %s' % len(df_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For yr_renovated, we will fill the nan values with yr_built, the reason will become evident when we derive a new feature from it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_data['yr_renovated'] = df_data['yr_renovated'].fillna(df_data['yr_built'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Feature Engineering and Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id',\n",
       " 'date',\n",
       " 'price',\n",
       " 'bedrooms',\n",
       " 'bathrooms',\n",
       " 'sqft_living',\n",
       " 'sqft_lot',\n",
       " 'floors',\n",
       " 'waterfront',\n",
       " 'view',\n",
       " 'condition',\n",
       " 'grade',\n",
       " 'sqft_above',\n",
       " 'sqft_basement',\n",
       " 'yr_built',\n",
       " 'yr_renovated',\n",
       " 'zipcode',\n",
       " 'lat',\n",
       " 'long']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df_data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### remove unwanted features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['price',\n",
       " 'bedrooms',\n",
       " 'bathrooms',\n",
       " 'sqft_lot',\n",
       " 'floors',\n",
       " 'waterfront',\n",
       " 'view',\n",
       " 'condition',\n",
       " 'grade',\n",
       " 'sqft_above',\n",
       " 'sqft_basement',\n",
       " 'yr_built',\n",
       " 'yr_renovated',\n",
       " 'zipcode']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst_selected_cols = [col for col in df_data.columns if col not in ['id', 'date', 'sqft_living', 'lat', 'long']]\n",
    "lst_selected_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
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       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>221900</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.00</td>\n",
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       "      <td>1955.00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>7242</td>\n",
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       "      <td>400</td>\n",
       "      <td>1951.00</td>\n",
       "      <td>1991.00</td>\n",
       "      <td>98125.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180000</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
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       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933.00</td>\n",
       "      <td>1933.00</td>\n",
       "      <td>98028.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>604000</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5000</td>\n",
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       "      <td>0</td>\n",
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       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965.00</td>\n",
       "      <td>1965.00</td>\n",
       "      <td>98136.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>510000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987.00</td>\n",
       "      <td>1987.00</td>\n",
       "      <td>98074.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    price  bedrooms  bathrooms  sqft_lot  floors  waterfront  view  condition  \\\n",
       "0  221900         3       1.00      5650    1.00           0     0          3   \n",
       "1  538000         3       2.25      7242    2.00           0     0          3   \n",
       "2  180000         2       1.00     10000    1.00           0     0          3   \n",
       "3  604000         4       3.00      5000    1.00           0     0          5   \n",
       "4  510000         3       2.00      8080    1.00           0     0          3   \n",
       "\n",
       "   grade  sqft_above  sqft_basement  yr_built  yr_renovated  zipcode  \n",
       "0      7        1180              0   1955.00       1955.00 98178.00  \n",
       "1      7        2170            400   1951.00       1991.00 98125.00  \n",
       "2      6         770              0   1933.00       1933.00 98028.00  \n",
       "3      7        1050            910   1965.00       1965.00 98136.00  \n",
       "4      8        1680              0   1987.00       1987.00 98074.00  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data = df_data[lst_selected_cols]\n",
    "df_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Can we derive better features from yr_built and yr_renovated?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "df_data['built_age'] = df_data['yr_built'].apply(lambda x: datetime.now().year - int(x))\n",
    "df_data['renovation_age'] = df_data['yr_renovated'].apply(lambda x: datetime.now().year - int(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>bedrooms</th>\n",
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       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
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       "      <th>2</th>\n",
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       "      <td>84</td>\n",
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       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965.00</td>\n",
       "      <td>1965.00</td>\n",
       "      <td>98136.00</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>510000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987.00</td>\n",
       "      <td>1987.00</td>\n",
       "      <td>98074.00</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    price  bedrooms  bathrooms  sqft_lot  floors  waterfront  view  condition  \\\n",
       "0  221900         3       1.00      5650    1.00           0     0          3   \n",
       "1  538000         3       2.25      7242    2.00           0     0          3   \n",
       "2  180000         2       1.00     10000    1.00           0     0          3   \n",
       "3  604000         4       3.00      5000    1.00           0     0          5   \n",
       "4  510000         3       2.00      8080    1.00           0     0          3   \n",
       "\n",
       "   grade  sqft_above  sqft_basement  yr_built  yr_renovated  zipcode  \\\n",
       "0      7        1180              0   1955.00       1955.00 98178.00   \n",
       "1      7        2170            400   1951.00       1991.00 98125.00   \n",
       "2      6         770              0   1933.00       1933.00 98028.00   \n",
       "3      7        1050            910   1965.00       1965.00 98136.00   \n",
       "4      8        1680              0   1987.00       1987.00 98074.00   \n",
       "\n",
       "   built_age  renovation_age  \n",
       "0         62              62  \n",
       "1         66              26  \n",
       "2         84              84  \n",
       "3         52              52  \n",
       "4         30              30  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### use dummy variables for zipcode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   zipcode_98103  zipcode_98038  zipcode_98115  zipcode_98052  zipcode_98117  \\\n",
       "0           0.00           0.00           0.00           0.00           0.00   \n",
       "1           0.00           0.00           0.00           0.00           0.00   \n",
       "2           0.00           0.00           0.00           0.00           0.00   \n",
       "3           0.00           0.00           0.00           0.00           0.00   \n",
       "4           0.00           0.00           0.00           0.00           0.00   \n",
       "\n",
       "   zipcode_98042  zipcode_98034  zipcode_98118  zipcode_98023  zipcode_98133  \\\n",
       "0           0.00           0.00           0.00           0.00           0.00   \n",
       "1           0.00           0.00           0.00           0.00           0.00   \n",
       "2           0.00           0.00           0.00           0.00           0.00   \n",
       "3           0.00           0.00           0.00           0.00           0.00   \n",
       "4           0.00           0.00           0.00           0.00           0.00   \n",
       "\n",
       "       ...        zipcode_98188  zipcode_98032  zipcode_98014  zipcode_98070  \\\n",
       "0      ...                 0.00           0.00           0.00           0.00   \n",
       "1      ...                 0.00           0.00           0.00           0.00   \n",
       "2      ...                 0.00           0.00           0.00           0.00   \n",
       "3      ...                 0.00           0.00           0.00           0.00   \n",
       "4      ...                 0.00           0.00           0.00           0.00   \n",
       "\n",
       "   zipcode_98109  zipcode_98010  zipcode_98102  zipcode_98024  zipcode_98148  \\\n",
       "0           0.00           0.00           1.00           0.00           0.00   \n",
       "1           0.00           0.00           0.00           0.00           0.00   \n",
       "2           0.00           0.00           0.00           0.00           0.00   \n",
       "3           0.00           0.00           0.00           0.00           0.00   \n",
       "4           0.00           0.00           0.00           0.00           0.00   \n",
       "\n",
       "   zipcode_98039  \n",
       "0           0.00  \n",
       "1           0.00  \n",
       "2           0.00  \n",
       "3           0.00  \n",
       "4           0.00  \n",
       "\n",
       "[5 rows x 70 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "enc = OneHotEncoder(sparse=False)\n",
    "zip_codes = enc.fit_transform(df_data[['zipcode']])\n",
    "\n",
    "df_zip_codes = pd.DataFrame(zip_codes, \n",
    "                            columns=[('zipcode' + \"_\" + str(int(i))) for i in df_data['zipcode'].value_counts().index])\n",
    "\n",
    "df_zip_codes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .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>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>...</th>\n",
       "      <th>zipcode_98188</th>\n",
       "      <th>zipcode_98032</th>\n",
       "      <th>zipcode_98014</th>\n",
       "      <th>zipcode_98070</th>\n",
       "      <th>zipcode_98109</th>\n",
       "      <th>zipcode_98010</th>\n",
       "      <th>zipcode_98102</th>\n",
       "      <th>zipcode_98024</th>\n",
       "      <th>zipcode_98148</th>\n",
       "      <th>zipcode_98039</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>221900</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>538000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180000</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>604000</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>510000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 86 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    price  bedrooms  bathrooms  sqft_lot  floors  waterfront  view  condition  \\\n",
       "0  221900         3       1.00      5650    1.00           0     0          3   \n",
       "1  538000         3       2.25      7242    2.00           0     0          3   \n",
       "2  180000         2       1.00     10000    1.00           0     0          3   \n",
       "3  604000         4       3.00      5000    1.00           0     0          5   \n",
       "4  510000         3       2.00      8080    1.00           0     0          3   \n",
       "\n",
       "   grade  sqft_above      ...        zipcode_98188  zipcode_98032  \\\n",
       "0      7        1180      ...                 0.00           0.00   \n",
       "1      7        2170      ...                 0.00           0.00   \n",
       "2      6         770      ...                 0.00           0.00   \n",
       "3      7        1050      ...                 0.00           0.00   \n",
       "4      8        1680      ...                 0.00           0.00   \n",
       "\n",
       "   zipcode_98014  zipcode_98070  zipcode_98109  zipcode_98010  zipcode_98102  \\\n",
       "0           0.00           0.00           0.00           0.00           1.00   \n",
       "1           0.00           0.00           0.00           0.00           0.00   \n",
       "2           0.00           0.00           0.00           0.00           0.00   \n",
       "3           0.00           0.00           0.00           0.00           0.00   \n",
       "4           0.00           0.00           0.00           0.00           0.00   \n",
       "\n",
       "   zipcode_98024  zipcode_98148  zipcode_98039  \n",
       "0           0.00           0.00           0.00  \n",
       "1           0.00           0.00           0.00  \n",
       "2           0.00           0.00           0.00  \n",
       "3           0.00           0.00           0.00  \n",
       "4           0.00           0.00           0.00  \n",
       "\n",
       "[5 rows x 86 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data = df_data.reset_index(drop=True)\n",
    "df_zip_codes = df_zip_codes.reset_index(drop=True)\n",
    "\n",
    "df_data = pd.concat([df_data, df_zip_codes], axis=1)\n",
    "\n",
    "df_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Algorithm Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_data = df_data.drop(['zipcode', 'yr_built', 'yr_renovated'], 1)\n",
    "lst_features = [feat for feat in df_data.columns if feat != 'price']\n",
    "target = ['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_features = df_data[lst_features]\n",
    "df_target = df_data[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>221900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>538000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>604000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>510000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    price\n",
       "0  221900\n",
       "1  538000\n",
       "2  180000\n",
       "3  604000\n",
       "4  510000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>...</th>\n",
       "      <th>zipcode_98188</th>\n",
       "      <th>zipcode_98032</th>\n",
       "      <th>zipcode_98014</th>\n",
       "      <th>zipcode_98070</th>\n",
       "      <th>zipcode_98109</th>\n",
       "      <th>zipcode_98010</th>\n",
       "      <th>zipcode_98102</th>\n",
       "      <th>zipcode_98024</th>\n",
       "      <th>zipcode_98148</th>\n",
       "      <th>zipcode_98039</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  sqft_lot  floors  waterfront  view  condition  grade  \\\n",
       "0         3       1.00      5650    1.00           0     0          3      7   \n",
       "1         3       2.25      7242    2.00           0     0          3      7   \n",
       "2         2       1.00     10000    1.00           0     0          3      6   \n",
       "3         4       3.00      5000    1.00           0     0          5      7   \n",
       "4         3       2.00      8080    1.00           0     0          3      8   \n",
       "\n",
       "   sqft_above  sqft_basement      ...        zipcode_98188  zipcode_98032  \\\n",
       "0        1180              0      ...                 0.00           0.00   \n",
       "1        2170            400      ...                 0.00           0.00   \n",
       "2         770              0      ...                 0.00           0.00   \n",
       "3        1050            910      ...                 0.00           0.00   \n",
       "4        1680              0      ...                 0.00           0.00   \n",
       "\n",
       "   zipcode_98014  zipcode_98070  zipcode_98109  zipcode_98010  zipcode_98102  \\\n",
       "0           0.00           0.00           0.00           0.00           1.00   \n",
       "1           0.00           0.00           0.00           0.00           0.00   \n",
       "2           0.00           0.00           0.00           0.00           0.00   \n",
       "3           0.00           0.00           0.00           0.00           0.00   \n",
       "4           0.00           0.00           0.00           0.00           0.00   \n",
       "\n",
       "   zipcode_98024  zipcode_98148  zipcode_98039  \n",
       "0           0.00           0.00           0.00  \n",
       "1           0.00           0.00           0.00  \n",
       "2           0.00           0.00           0.00  \n",
       "3           0.00           0.00           0.00  \n",
       "4           0.00           0.00           0.00  \n",
       "\n",
       "[5 rows x 82 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### split dataset into training and testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_features, df_target, test_size=0.20, random_state=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training dataset shape: (16934, 82)\n",
      "Test dataset shape: (4234, 82)\n"
     ]
    }
   ],
   "source": [
    "print('Training dataset shape:', X_train.shape)\n",
    "print('Test dataset shape:', X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------\n",
      "model: Linear Regression\n",
      "best score: 111943.287504\n",
      "-----------------------------\n",
      "-----------------------------\n",
      "model: ridge_regression\n",
      "best score: 106132.654773\n",
      "best alpha vaue: 0.001\n",
      "-----------------------------\n",
      "-----------------------------\n",
      "model: lasso_regression\n",
      "best score: 106133.335283\n",
      "best alpha vaue: 0.1\n",
      "-----------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "lst_alphas = np.array([1, 0.1, 0.01, 0.001, 0.0001, 0])\n",
    "\n",
    "linear_model = LinearRegression(normalize=True)\n",
    "ridge_model = Ridge(normalize=True)\n",
    "lasso_model = Lasso(normalize=True)\n",
    "\n",
    "scores = cross_val_score(linear_model, X_train, y_train, scoring=\"neg_mean_squared_error\", cv=10)\n",
    "rmse_scores = np.sqrt(-scores)\n",
    "print('-----------------------------')\n",
    "print('model: Linear Regression')\n",
    "print('best score:', np.max(np.sqrt(-scores)))\n",
    "print('-----------------------------')\n",
    "\n",
    "dict_regularized_models = {'ridge_regression': ridge_model, 'lasso_regression': lasso_model}\n",
    "\n",
    "for model_name, model in dict_regularized_models.items():\n",
    "    \n",
    "    grid = GridSearchCV(estimator=model, param_grid=dict(alpha=lst_alphas), scoring=\"neg_mean_squared_error\")\n",
    "    grid.fit(X_train, y_train)\n",
    "    \n",
    "    # summarize the results of the grid search\n",
    "    print('-----------------------------')\n",
    "    print('model:', model_name)\n",
    "    print('best score:', np.sqrt(-grid.best_score_))\n",
    "    print('best alpha vaue:', grid.best_estimator_.alpha)\n",
    "    print('-----------------------------')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrain the models on entire training set using the best parameters obtained through cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------\n",
      "model: linear model\n",
      "rmse score: 106870.94148\n",
      "-----------------------------\n",
      "-----------------------------\n",
      "model: ridge model\n",
      "rmse score: 106900.955148\n",
      "-----------------------------\n",
      "-----------------------------\n",
      "model: lasso model\n",
      "rmse score: 106870.34435\n",
      "-----------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "linear_model = LinearRegression(normalize=True)\n",
    "ridge_model = Ridge(normalize=True, alpha=0.01)\n",
    "lasso_model = Lasso(normalize=True, alpha=0.1)\n",
    "\n",
    "lst_models = {'linear model': linear_model, 'ridge model': ridge_model, 'lasso model': lasso_model}\n",
    "\n",
    "for model_name, model in lst_models.items():\n",
    "    \n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "    # summarize the results of the grid search\n",
    "    print('-----------------------------')\n",
    "    print('model:', model_name)\n",
    "    print('rmse score:', rmse)\n",
    "    print('-----------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Conclusion: The lasso model, only very slightly performed better than the other models but still the RMSE error is high which means there is plenty of room for improvement. Remember machine learning is an iterative process and you should always strive to improve your models performance. \n",
    "\n",
    "Attack the problem from different fronts: \n",
    "\n",
    "1. quality or the quantity of the training data.\n",
    "2. Feature Selection and Feature Engineering\n",
    "3. Modeling technique\n",
    "4. Ensembling Techniques\n",
    "5. Scoring Mechanisms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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