exploratory-data-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the Libraries : pandas , numpy, seaborn, matplotlib.pyplot , scipy.stats\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import scipy.stats as st\n",
    "\n",
    "\n",
    "# Import warnings library and set it to ignore[so as to ignore warnings]\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "#Display all the columns of the Dataframe\n",
    "pd.pandas.set_option('display.max_columns',None)\n",
    "#Display all Numbers of float datatype as numbers with 2 decimal places \n",
    "pd.options.display.float_format = \"{:.2f}\".format\n",
    "\n",
    "#from SciKit Learn import Models and metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import ensemble\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import explained_variance_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>review_score</th>\n",
       "      <th>basement_size</th>\n",
       "      <th>built</th>\n",
       "      <th>renovation</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>living_room_size</th>\n",
       "      <th>lot_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7787980.00</td>\n",
       "      <td>13 October 2014</td>\n",
       "      <td>221900.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>fair</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1955.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98178.00</td>\n",
       "      <td>47.51</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>1340.00</td>\n",
       "      <td>5650.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6774848.00</td>\n",
       "      <td>09 December 2014</td>\n",
       "      <td>538000.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>fair</td>\n",
       "      <td>7.00</td>\n",
       "      <td>400.00</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",
       "      <td>1690.00</td>\n",
       "      <td>7639.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6999329.00</td>\n",
       "      <td>25 February 2015</td>\n",
       "      <td>180000.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>fair</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1933.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98028.00</td>\n",
       "      <td>47.74</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>2720.00</td>\n",
       "      <td>8062.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7773052.00</td>\n",
       "      <td>09 December 2014</td>\n",
       "      <td>604000.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>excellent</td>\n",
       "      <td>6.00</td>\n",
       "      <td>910.00</td>\n",
       "      <td>1965.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98136.00</td>\n",
       "      <td>47.52</td>\n",
       "      <td>-122.39</td>\n",
       "      <td>1360.00</td>\n",
       "      <td>5000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7760818.00</td>\n",
       "      <td>18 February 2015</td>\n",
       "      <td>510000.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>fair</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1987.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98074.00</td>\n",
       "      <td>47.62</td>\n",
       "      <td>-122.05</td>\n",
       "      <td>1800.00</td>\n",
       "      <td>7503.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7659918.00</td>\n",
       "      <td>12 May 2014</td>\n",
       "      <td>1225000.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>fair</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1530.00</td>\n",
       "      <td>2001.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98053.00</td>\n",
       "      <td>47.66</td>\n",
       "      <td>-122.00</td>\n",
       "      <td>4760.00</td>\n",
       "      <td>101930.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8057033.00</td>\n",
       "      <td>27 June 2014</td>\n",
       "      <td>257500.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>FAIR</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1995.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98003.00</td>\n",
       "      <td>47.31</td>\n",
       "      <td>-122.33</td>\n",
       "      <td>2238.00</td>\n",
       "      <td>6819.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7249853.00</td>\n",
       "      <td>15 January 2015</td>\n",
       "      <td>291850.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>fair</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1963.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98198.00</td>\n",
       "      <td>47.41</td>\n",
       "      <td>-122.31</td>\n",
       "      <td>1650.00</td>\n",
       "      <td>9711.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7906980.00</td>\n",
       "      <td>15 April 2015</td>\n",
       "      <td>229500.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>FAIR</td>\n",
       "      <td>7.00</td>\n",
       "      <td>730.00</td>\n",
       "      <td>1960.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98146.00</td>\n",
       "      <td>47.51</td>\n",
       "      <td>-122.34</td>\n",
       "      <td>1780.00</td>\n",
       "      <td>8113.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7605289.00</td>\n",
       "      <td>12 March 2015</td>\n",
       "      <td>323000.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>fair</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2003.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98038.00</td>\n",
       "      <td>47.37</td>\n",
       "      <td>-122.03</td>\n",
       "      <td>2390.00</td>\n",
       "      <td>7570.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7934973.00</td>\n",
       "      <td>03 April 2015</td>\n",
       "      <td>662500.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>fair</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>1965.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98007.00</td>\n",
       "      <td>47.60</td>\n",
       "      <td>-122.14</td>\n",
       "      <td>2210.00</td>\n",
       "      <td>8925.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6867014.00</td>\n",
       "      <td>27 May 2014</td>\n",
       "      <td>468000.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>good</td>\n",
       "      <td>6.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>1942.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98115.00</td>\n",
       "      <td>47.69</td>\n",
       "      <td>-122.29</td>\n",
       "      <td>1330.00</td>\n",
       "      <td>6000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>nan</td>\n",
       "      <td>28 May 2014</td>\n",
       "      <td>nan</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>good</td>\n",
       "      <td>5.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>1927.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>12697.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7158525.00</td>\n",
       "      <td>07 October 2014</td>\n",
       "      <td>400000.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>good</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1977.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98074.00</td>\n",
       "      <td>47.61</td>\n",
       "      <td>-122.05</td>\n",
       "      <td>1370.00</td>\n",
       "      <td>10208.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7728368.00</td>\n",
       "      <td>12 March 2015</td>\n",
       "      <td>530000.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>fair</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1900.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98107.00</td>\n",
       "      <td>47.67</td>\n",
       "      <td>-122.39</td>\n",
       "      <td>1360.00</td>\n",
       "      <td>4850.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6927268.00</td>\n",
       "      <td>24 January 2015</td>\n",
       "      <td>650000.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>fair</td>\n",
       "      <td>7.00</td>\n",
       "      <td>970.00</td>\n",
       "      <td>1979.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98126.00</td>\n",
       "      <td>47.57</td>\n",
       "      <td>-122.38</td>\n",
       "      <td>2140.00</td>\n",
       "      <td>4000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6828369.00</td>\n",
       "      <td>31 July 2014</td>\n",
       "      <td>395000.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>fair</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1994.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98019.00</td>\n",
       "      <td>47.73</td>\n",
       "      <td>-121.96</td>\n",
       "      <td>1890.00</td>\n",
       "      <td>14018.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7148719.00</td>\n",
       "      <td>29 May 2014</td>\n",
       "      <td>485000.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>good</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1916.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98103.00</td>\n",
       "      <td>47.66</td>\n",
       "      <td>-122.34</td>\n",
       "      <td>1610.00</td>\n",
       "      <td>4300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7651336.00</td>\n",
       "      <td>05 December 2014</td>\n",
       "      <td>189000.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>good</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1921.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98002.00</td>\n",
       "      <td>47.31</td>\n",
       "      <td>-122.21</td>\n",
       "      <td>1060.00</td>\n",
       "      <td>5095.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7681773.00</td>\n",
       "      <td>24 April 2015</td>\n",
       "      <td>230000.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>good</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1969.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98003.00</td>\n",
       "      <td>47.33</td>\n",
       "      <td>-122.31</td>\n",
       "      <td>1280.00</td>\n",
       "      <td>8850.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id              date      price  bedrooms  bathrooms  floors  \\\n",
       "0  7787980.00   13 October 2014  221900.00      3.00       1.00    1.00   \n",
       "1  6774848.00  09 December 2014  538000.00      3.00       2.25    2.00   \n",
       "2  6999329.00  25 February 2015  180000.00      2.00       1.00    1.00   \n",
       "3  7773052.00  09 December 2014  604000.00      4.00       3.00    1.00   \n",
       "4  7760818.00  18 February 2015  510000.00      3.00       2.00    1.00   \n",
       "5  7659918.00       12 May 2014 1225000.00      4.00       4.50    1.00   \n",
       "6  8057033.00      27 June 2014  257500.00      3.00       2.25    2.00   \n",
       "7  7249853.00   15 January 2015  291850.00      3.00       1.50    1.00   \n",
       "8  7906980.00     15 April 2015  229500.00      3.00       1.00    1.00   \n",
       "9  7605289.00     12 March 2015  323000.00      3.00       2.50    2.00   \n",
       "10 7934973.00     03 April 2015  662500.00      3.00       2.50    1.00   \n",
       "11 6867014.00       27 May 2014  468000.00      2.00       1.00    1.00   \n",
       "12        nan       28 May 2014        nan      3.00        nan    1.50   \n",
       "13 7158525.00   07 October 2014  400000.00      3.00       1.75    1.00   \n",
       "14 7728368.00     12 March 2015  530000.00      5.00       2.00    1.50   \n",
       "15 6927268.00   24 January 2015  650000.00      4.00       3.00    2.00   \n",
       "16 6828369.00      31 July 2014  395000.00      3.00       2.00    2.00   \n",
       "17 7148719.00       29 May 2014  485000.00      4.00       1.00    1.50   \n",
       "18 7651336.00  05 December 2014  189000.00      2.00       1.00    1.00   \n",
       "19 7681773.00     24 April 2015  230000.00      3.00       1.00    1.00   \n",
       "\n",
       "    waterfront  view  condition  review_score  basement_size   built  \\\n",
       "0         0.00  0.00       fair         10.00           0.00 1955.00   \n",
       "1         0.00  0.00       fair          7.00         400.00 1951.00   \n",
       "2         0.00  0.00       fair          8.00           0.00 1933.00   \n",
       "3         0.00  0.00  excellent          6.00         910.00 1965.00   \n",
       "4         0.00  0.00       fair          6.00           0.00 1987.00   \n",
       "5         0.00  0.00       fair          8.00        1530.00 2001.00   \n",
       "6         0.00  0.00       FAIR          7.00           0.00 1995.00   \n",
       "7         0.00  0.00       fair          7.00           0.00 1963.00   \n",
       "8         0.00  0.00       FAIR          7.00         730.00 1960.00   \n",
       "9         0.00  0.00       fair          7.00           0.00 2003.00   \n",
       "10        0.00  0.00       fair          7.00        1700.00 1965.00   \n",
       "11        0.00  0.00       good          6.00         300.00 1942.00   \n",
       "12        0.00   nan       good          5.00            nan 1927.00   \n",
       "13        0.00  0.00       good          7.00           0.00 1977.00   \n",
       "14        0.00  0.00       fair          9.00           0.00 1900.00   \n",
       "15        0.00  3.00       fair          7.00         970.00 1979.00   \n",
       "16        0.00  0.00       fair          7.00           0.00 1994.00   \n",
       "17        0.00  0.00       good          9.00           0.00 1916.00   \n",
       "18        0.00  0.00       good          8.00           0.00 1921.00   \n",
       "19        0.00  0.00       good          8.00           0.00 1969.00   \n",
       "\n",
       "    renovation  zipcode  latitude  longitude  living_room_size  lot_size  \n",
       "0         0.00 98178.00     47.51    -122.26           1340.00   5650.00  \n",
       "1      1991.00 98125.00     47.72    -122.32           1690.00   7639.00  \n",
       "2         0.00 98028.00     47.74    -122.23           2720.00   8062.00  \n",
       "3         0.00 98136.00     47.52    -122.39           1360.00   5000.00  \n",
       "4         0.00 98074.00     47.62    -122.05           1800.00   7503.00  \n",
       "5         0.00 98053.00     47.66    -122.00           4760.00 101930.00  \n",
       "6         0.00 98003.00     47.31    -122.33           2238.00   6819.00  \n",
       "7         0.00 98198.00     47.41    -122.31           1650.00   9711.00  \n",
       "8         0.00 98146.00     47.51    -122.34           1780.00   8113.00  \n",
       "9         0.00 98038.00     47.37    -122.03           2390.00   7570.00  \n",
       "10        0.00 98007.00     47.60    -122.14           2210.00   8925.00  \n",
       "11        0.00 98115.00     47.69    -122.29           1330.00   6000.00  \n",
       "12        0.00      nan       nan        nan               nan  12697.00  \n",
       "13        0.00 98074.00     47.61    -122.05           1370.00  10208.00  \n",
       "14        0.00 98107.00     47.67    -122.39           1360.00   4850.00  \n",
       "15        0.00 98126.00     47.57    -122.38           2140.00   4000.00  \n",
       "16        0.00 98019.00     47.73    -121.96           1890.00  14018.00  \n",
       "17        0.00 98103.00     47.66    -122.34           1610.00   4300.00  \n",
       "18        0.00 98002.00     47.31    -122.21           1060.00   5095.00  \n",
       "19        0.00 98003.00     47.33    -122.31           1280.00   8850.00  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verify that result of SQL query is stored in the DataFrame and Print the top 20 records\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23154 entries, 0 to 23153\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   id                21871 non-null  float64\n",
      " 1   date              21925 non-null  object \n",
      " 2   price             21896 non-null  float64\n",
      " 3   bedrooms          21905 non-null  float64\n",
      " 4   bathrooms         21938 non-null  float64\n",
      " 5   floors            21862 non-null  float64\n",
      " 6   waterfront        21871 non-null  float64\n",
      " 7   view              21908 non-null  float64\n",
      " 8   condition         21890 non-null  object \n",
      " 9   review_score      21851 non-null  float64\n",
      " 10  basement_size     21929 non-null  float64\n",
      " 11  built             21853 non-null  float64\n",
      " 12  renovation        21897 non-null  float64\n",
      " 13  zipcode           21884 non-null  float64\n",
      " 14  latitude          21903 non-null  float64\n",
      " 15  longitude         21883 non-null  float64\n",
      " 16  living_room_size  21912 non-null  float64\n",
      " 17  lot_size          21932 non-null  float64\n",
      "dtypes: float64(16), object(2)\n",
      "memory usage: 3.2+ MB\n"
     ]
    }
   ],
   "source": [
    "#display information : number of rows and columns, Column or  Feature name \n",
    "#the total memory usage, the data type of each column, and the number of non-Null elements.\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observation (from above two Displayed Tables ) that there are :\n",
    "## a) 2 Object DataTypes ('condition' Column is Categorical and \"date\" is a Date-Time Variable but an    Object or String datatype and the last four digit is Year in YYYY format).We will convert that into int and then to float datatype \n",
    "## b) 16 Numerical DataTypes ( of which \"id\"'s value does not matter and 'built' and 'renovation' and actually Year which is a Time or Temporal Variable)\n",
    "## c)Home Price or 'price' which is the Target Variable for prediction later , has only 21871 non-null values , hence we can safely drop all other values or null values from all rows from all features or variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data Cleaning and Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['fair', 'excellent', 'FAIR', 'good', 'TERRIBLE', 'poor', nan,\n",
       "       'GOOD', 'EXCELLENT', 'terrible', 'POOR'], dtype=object)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We drop 'id' as it will not be used in the Analysis\n",
    "df = df.drop('id',axis=1)\n",
    "\n",
    "#we put in the df DataFrame only those values for all columns , for which 'price' Column has non-null or non-na values\n",
    "#i.e. we execute on observation c) above\n",
    "df = df[df['price'].notna()]\n",
    "\n",
    "#get the unique vales of Column 'condition'\n",
    "df['condition'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['fair', 'excellent', 'good', 'terrible', 'poor', 'nan'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert all lower case in 'condition' column as fair = FAIR , same is true for others , except none , which we will \n",
    "#handle in the Data Prep stage\n",
    "df['condition'] = [str(i).lower() for i in df['condition']]\n",
    "# check the unique values \n",
    "df['condition'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we replace the 'condition' of the house with numerical rating ['excellent', 'good','fair','poor','terrible','none'] \n",
    "#with [5,4,3,2,1,0]\n",
    "df['condition'] = df['condition'].replace(to_replace =['excellent', 'good','fair','poor','terrible','none'],  \n",
    "                            value =[5.0,4.0,3.0,2.0,1.0,0.0]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3., 5., 4., 1., 2., 0.])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert string or object number to numerical or int datatype\n",
    "df['condition']=pd.to_numeric(df['condition'], errors='coerce').fillna(0, downcast='infer')\n",
    "\n",
    "#convert to float datatype\n",
    "df['condition']=df['condition'].astype(float)\n",
    "# check the unique values in the DataFrame Column 'condition'\n",
    "df['condition'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2014', '2015', nan], dtype=object)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the last 4 characters ie Year (YYYY) of the 'date' Column \n",
    "df['date']=df['date'].str[-4:]\n",
    "\n",
    "#get the unique values of date column\n",
    "df['date'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2014., 2015.,    0.])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert string or object number to numerical or int datatype\n",
    "df['date']=pd.to_numeric(df['date'], errors='coerce').fillna(0, downcast='infer')\n",
    "\n",
    "#convert to float datatype\n",
    "df['date']=df['date'].astype(float)\n",
    "\n",
    "#get the unique values of date column\n",
    "df['date'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1955., 1951., 1933., 1965., 1987., 2001., 1995., 1963., 1960.,\n",
       "       2003., 1942., 1977., 1900., 1979., 1994., 1916., 1921., 1969.,\n",
       "       1947., 1968., 1985., 1941., 1915., 1909., 1948., 2005., 1929.,\n",
       "       1981., 1930., 1904., 1996., 2000., 1984., 2014., 1922., 1959.,\n",
       "       1966.,   nan, 1953., 1950., 1927., 2008., 1991., 1954., 1925.,\n",
       "       1989., 1973., 1972., 1986., 1956., 2002., 1992., 1964., 1952.,\n",
       "       1961., 2006., 1988., 1939., 1946., 1967., 1975., 1910., 1983.,\n",
       "       1978., 1905., 1971., 2010., 1924., 1990., 1914., 1926., 2004.,\n",
       "       1962., 1923., 2007., 1976., 1949., 1999., 1980., 1901., 1993.,\n",
       "       1920., 1997., 1943., 1957., 1940., 1918., 1928., 1974., 1911.,\n",
       "       1936., 1937., 1982., 1908., 1931., 1998., 2013., 1907., 1958.,\n",
       "       2012., 1912., 2011., 1917., 1932., 1944., 1902., 2009., 1903.,\n",
       "       1970., 2015., 1934., 1938., 1913., 1919., 1906., 1945., 1935.])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the unique values of 'built' or Year Built column\n",
    "df['built'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "117"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of unique 'built' values\n",
    "len(df['built'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0., 1991., 2002.,   nan, 2010., 1999., 1992., 2013., 1994.,\n",
       "       2005., 2008., 2003., 1984., 1954., 2014., 2011., 1974., 1945.,\n",
       "       1990., 1988., 1957., 1977., 1981., 1995., 2000., 1998., 1970.,\n",
       "       1989., 2004., 1986., 2009., 2007., 1987., 1973., 2006., 1985.,\n",
       "       2001., 1980., 1971., 1979., 1997., 1969., 1948., 1983., 2015.,\n",
       "       1978., 1968., 2012., 1963., 1951., 1993., 1962., 1996., 1972.,\n",
       "       1953., 1955., 1950., 1956., 1982., 1940., 1976., 1946., 1975.,\n",
       "       1958., 1964., 1959., 1960., 1967., 1965., 1934., 1944.])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the unique values of 'renovation' or Renovation Year column\n",
    "df['renovation'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of unique 'renovation' values\n",
    "len(df['renovation'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observation 1a)\n",
    "We observe from the Above 3 Temporal Variables : Date or Date of Sale , Year Built and Renovation Year Unique Values that Date has only two years (viz. 2014 and 2015) while Year Built and Renovation Year has a wide range since early 1900s to 2000s ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 21896 entries, 0 to 23153\n",
      "Data columns (total 17 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   date              21896 non-null  float64\n",
      " 1   price             21896 non-null  float64\n",
      " 2   bedrooms          21308 non-null  float64\n",
      " 3   bathrooms         21326 non-null  float64\n",
      " 4   floors            21279 non-null  float64\n",
      " 5   waterfront        21274 non-null  float64\n",
      " 6   view              21313 non-null  float64\n",
      " 7   condition         21896 non-null  float64\n",
      " 8   review_score      20669 non-null  float64\n",
      " 9   basement_size     21337 non-null  float64\n",
      " 10  built             21283 non-null  float64\n",
      " 11  renovation        21313 non-null  float64\n",
      " 12  zipcode           21313 non-null  float64\n",
      " 13  latitude          21309 non-null  float64\n",
      " 14  longitude         21321 non-null  float64\n",
      " 15  living_room_size  21294 non-null  float64\n",
      " 16  lot_size          21313 non-null  float64\n",
      "dtypes: float64(17)\n",
      "memory usage: 3.0 MB\n"
     ]
    }
   ],
   "source": [
    "#display information : number of rows and columns, Column or  Feature name \n",
    "#the total memory usage, the data type of each column, and the number of non-Null elements.\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>review_score</th>\n",
       "      <th>basement_size</th>\n",
       "      <th>built</th>\n",
       "      <th>renovation</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>living_room_size</th>\n",
       "      <th>lot_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>21896.00</td>\n",
       "      <td>21896.00</td>\n",
       "      <td>21308.00</td>\n",
       "      <td>21326.00</td>\n",
       "      <td>21279.00</td>\n",
       "      <td>21274.00</td>\n",
       "      <td>21313.00</td>\n",
       "      <td>21896.00</td>\n",
       "      <td>20669.00</td>\n",
       "      <td>21337.00</td>\n",
       "      <td>21283.00</td>\n",
       "      <td>21313.00</td>\n",
       "      <td>21313.00</td>\n",
       "      <td>21309.00</td>\n",
       "      <td>21321.00</td>\n",
       "      <td>21294.00</td>\n",
       "      <td>21313.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1961.24</td>\n",
       "      <td>540653.80</td>\n",
       "      <td>3.37</td>\n",
       "      <td>2.11</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.23</td>\n",
       "      <td>3.32</td>\n",
       "      <td>7.66</td>\n",
       "      <td>290.97</td>\n",
       "      <td>1971.02</td>\n",
       "      <td>84.93</td>\n",
       "      <td>98077.92</td>\n",
       "      <td>47.56</td>\n",
       "      <td>-122.21</td>\n",
       "      <td>1986.02</td>\n",
       "      <td>12809.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>322.66</td>\n",
       "      <td>369404.74</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.18</td>\n",
       "      <td>441.94</td>\n",
       "      <td>29.35</td>\n",
       "      <td>402.88</td>\n",
       "      <td>53.48</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>685.21</td>\n",
       "      <td>27385.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>75000.00</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",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1900.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98001.00</td>\n",
       "      <td>47.16</td>\n",
       "      <td>-122.52</td>\n",
       "      <td>399.00</td>\n",
       "      <td>651.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2014.00</td>\n",
       "      <td>321000.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.50</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>0.00</td>\n",
       "      <td>1951.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98033.00</td>\n",
       "      <td>47.47</td>\n",
       "      <td>-122.33</td>\n",
       "      <td>1490.00</td>\n",
       "      <td>5100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.00</td>\n",
       "      <td>450000.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.25</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>0.00</td>\n",
       "      <td>1975.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98065.00</td>\n",
       "      <td>47.57</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>1840.00</td>\n",
       "      <td>7620.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2015.00</td>\n",
       "      <td>645000.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.50</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>560.00</td>\n",
       "      <td>1997.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98118.00</td>\n",
       "      <td>47.68</td>\n",
       "      <td>-122.12</td>\n",
       "      <td>2360.00</td>\n",
       "      <td>10091.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2015.00</td>\n",
       "      <td>7700000.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>8.00</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>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",
       "      <td>6210.00</td>\n",
       "      <td>871200.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date      price  bedrooms  bathrooms   floors  waterfront     view  \\\n",
       "count 21896.00   21896.00  21308.00   21326.00 21279.00    21274.00 21313.00   \n",
       "mean   1961.24  540653.80      3.37       2.11     1.49        0.01     0.23   \n",
       "std     322.66  369404.74      0.91       0.77     0.54        0.09     0.76   \n",
       "min       0.00   75000.00      0.00       0.00     1.00        0.00     0.00   \n",
       "25%    2014.00  321000.00      3.00       1.50     1.00        0.00     0.00   \n",
       "50%    2014.00  450000.00      3.00       2.25     1.50        0.00     0.00   \n",
       "75%    2015.00  645000.00      4.00       2.50     2.00        0.00     0.00   \n",
       "max    2015.00 7700000.00     11.00       8.00     3.50        1.00     4.00   \n",
       "\n",
       "       condition  review_score  basement_size    built  renovation  zipcode  \\\n",
       "count   21896.00      20669.00       21337.00 21283.00    21313.00 21313.00   \n",
       "mean        3.32          7.66         290.97  1971.02       84.93 98077.92   \n",
       "std         0.85          1.18         441.94    29.35      402.88    53.48   \n",
       "min         0.00          1.00           0.00  1900.00        0.00 98001.00   \n",
       "25%         3.00          7.00           0.00  1951.00        0.00 98033.00   \n",
       "50%         3.00          7.00           0.00  1975.00        0.00 98065.00   \n",
       "75%         4.00          8.00         560.00  1997.00        0.00 98118.00   \n",
       "max         5.00         13.00        4820.00  2015.00     2015.00 98199.00   \n",
       "\n",
       "       latitude  longitude  living_room_size  lot_size  \n",
       "count  21309.00   21321.00          21294.00  21313.00  \n",
       "mean      47.56    -122.21           1986.02  12809.11  \n",
       "std        0.14       0.14            685.21  27385.09  \n",
       "min       47.16    -122.52            399.00    651.00  \n",
       "25%       47.47    -122.33           1490.00   5100.00  \n",
       "50%       47.57    -122.23           1840.00   7620.00  \n",
       "75%       47.68    -122.12           2360.00  10091.00  \n",
       "max       47.78    -121.31           6210.00 871200.00  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#descriptive statistics summary of all Numerical Features or Variables .\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observation 1b)\n",
    "We Observe from the above table in the House Price/\"price\" Column or Variable that the max value is 7.7 million and minimum value is 75k . Let's see if they are Normally Distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9630729f50>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot normal distribution of House Price \n",
    "y = df['price']\n",
    "plt.figure(figsize=(11,11)); plt.title('Normal')\n",
    "sns.distplot(y, kde=False, fit=st.norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 4.102171\n",
      "Kurtosis: 35.801755\n"
     ]
    }
   ],
   "source": [
    "print(\"Skewness: %f\" % df['price'].skew())\n",
    "print(\"Kurtosis: %f\" % df['price'].kurt())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observation 1c)\n",
    "Skewness is ameasure of Symmetry or lack thereof of the Distribution . We see from the above plot , that \"House Price\" doesn't follow normal distribution and has high skewness, deviates from the normal distribution and shows peakedness or high Kurtosis.So we need to Transform the Data before we run our ML Algorithms on it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# 2. Analysis and Ploting of House Price , Other Numerical ,Temporal Variables and Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We plot the histogram (count=y-axis vs feature x-axis)for all 17 features to understand how they are distributed and\n",
    "# whether they are Continuous or Discrete or Outliers\n",
    "features=df[['date','price', 'bedrooms', 'bathrooms', 'condition', 'floors', 'waterfront', 'view', 'review_score',\n",
    "    'basement_size', 'built', 'renovation', 'zipcode',\n",
    "    'latitude', 'longitude', 'living_room_size', 'lot_size']]\n",
    "\n",
    "h = features.hist(bins=25,figsize=(16,16),xlabelsize='10',ylabelsize='10',xrot=-15)\n",
    "sns.despine(left=True, bottom=True)\n",
    "[x.title.set_size(12) for x in h.ravel()];\n",
    "[x.yaxis.tick_left() for x in h.ravel()];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observation 2a)\n",
    "We observe from the above Histograms that all the Features are Skewed and not normally distrbuted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrete Variables Count: 6\n",
      "Continuous Variables Count: 8\n"
     ]
    }
   ],
   "source": [
    "# Year or Temporal feature (as per Observation d) above)\n",
    "year_feature = ['date','built','renovation']\n",
    "\n",
    "# Numerical variables are of 2 types : Continous variable and Discrete Variables . We define discrete feature to\n",
    "# have less than 25 unique values , otherwise continuous feature\n",
    "discrete_feature=[f for f in df.columns if len(df[f].unique())<25 and f not in \n",
    "                  year_feature]\n",
    "print(\"Discrete Variables Count: {}\".format(len(discrete_feature)))\n",
    "\n",
    "continuous_feature=[f for f in df.columns if len(df[f].unique())>=25 and f not in \n",
    "                  year_feature]\n",
    "print(\"Continuous Variables Count: {}\".format(len(continuous_feature)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bedrooms', 'floors', 'waterfront', 'view', 'condition', 'review_score']"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# discrete features\n",
    "discrete_feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['price',\n",
       " 'bathrooms',\n",
       " 'basement_size',\n",
       " 'zipcode',\n",
       " 'latitude',\n",
       " 'longitude',\n",
       " 'living_room_size',\n",
       " 'lot_size']"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# continuous features\n",
    "continuous_feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Median House Price vs Date of Sale')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Lets analyze the Temporal Date-time or Year Variables\n",
    "## We will check whether there is a relation between year the house is sold vs House Price\n",
    "\n",
    "df.groupby('date')['price'].median().plot()\n",
    "plt.xlabel('Year Sold')\n",
    "plt.ylabel('Median House Price')\n",
    "plt.title(\"Median House Price vs Date of Sale\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#we plot the Scatter Plot of House Price(price) vs Year or Temporal variable/feature \n",
    "for feature in year_feature:\n",
    "    data=df.copy()\n",
    "    plt.scatter(data[feature],data['price'])\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('price')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observation 2b)\n",
    "We observe that 'renovation' has a lot of Zeros and Outliers , so for missing values it needs to be populated my median. And to reduce the number features from 3 to 2 we need to do feature engineering in 3. (next section)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEcCAYAAADKlrO6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAUKElEQVR4nO3df7RdZX3n8fenCXTSaqWa1MEECLVRJ3YANaKOraAzrYlOpe2qlugqHQsGRnF0rWmHtDOrzixnOjj+0cIIjSlNGWaNZI0MbakGsxwtP1YxTiIiEig0AwoRleAvBrHFyHf+ODve05Nzc2/w7nvu5Xm/1jqLs5/nOed+72bnfO6zf5ydqkKS1K4fmnQBkqTJMggkqXEGgSQ1ziCQpMYZBJLUOINAkhpnEEhHKcn1SX590nVIcyVeRyBJbXNGIEmNMwikMZJsTnLNSNslSS5NckOS84bafyPJXUm+kWRnkpO69v+Q5L92z49J8u0k/6VbXpbkb5P8+Hz+XtI4BoE03tXA65L8GECSJcCbgA8ND0ryi8DvAL8MrABu7l4LcCNwZvf8pcBXgDO65VcAd1fVN/r7FaTZWZRBkGRbkoeS3DHL8W9KcmeSvUk+NPMr1Lqq+iJwK/CLXdNrgMeqatfI0POB/1xVd1XVQeD3gNO6WcGngDVJngW8CvhjYGWSpzEIhBvn4VeRZrQogwC4Elg/m4FJ1gC/Dbyyql4IvLvHuvTU8iFgY/f8zYzMBjonAZck+WaSbwJfBwKsrKrvAHsYfOi/isEH/y3AKzEItIAsyiCoqpsY/IP7viTPTfKxJJ9JcnOSF3RdbwMuOzQFr6qH5rlcLV4fBs5Msgr4JcYHwQPA+VV13NBjWVXd0vXfyGA28SJgd7f8WuB04KbefwNpFhZlEExjK/DOqnoJ8JvA5V3784DnJfmrJLuSzGomIVXVAeAG4E+A+6rqrjHDtgC/neSFAEmekeSNQ/03AucAd1bV4937nde934Eey5dmbemkC5gL3T7XfwJ8OMmh5h/u/rsUWMPgoN0q4OYkP11V35zvOrUofQi4Cvg34zqr6k+77W97d1zgW8DHGcwmYLAraBlTf/3fCfwtzga0gCzaC8qSrAY+UlU/3Z3ZcXdVHT9m3BZgV1Vd2S1/AthcVbvnsVxJWrCeEruGquoR4L5DU/IMnNp1/xnw6q59OYNdRfdOpFBJWoAWZRAkuZrBqXnPT7I/ybnAW4Bzk3wO2Auc1Q3fCXwtyZ3AXwK/VVVfm0TdkrQQLdpdQ5KkubEoZwSSpLmz6M4aWr58ea1evXrSZUjSovKZz3zm4apaMa5v0QXB6tWr2bNnz6TLkKRFJckXp+tz15AkNc4gkKTGGQSS1DiDQJIaZxBIUuMMAklqnEEgSY0zCCSpcQaBJDVu0V1ZLEnzafXmj066BL5w8et7ff/eZgRJtiV5KMkd0/S/Jcnt3eOWofsHSJLmUZ+7hq4EjnR/4PuAM6rqFOC9DO45LEmaZ73tGqqqm7rbSU7Xf8vQ4i4G9xOWJM2zhXKw+Fzg+uk6k2xKsifJngMHDsxjWZL01DfxIEjyagZBcNF0Y6pqa1Wtq6p1K1aM/TptSdKTNNGzhpKcAlwBbPA+wpI0GRObESQ5EbgW+LWqumdSdUhS63qbESS5GjgTWJ5kP/Ae4BiAqtoC/C7wLODyJAAHq2pdX/VIksbr86yhjTP0nwec19fPlyTNzsQPFkuSJssgkKTGGQSS1DiDQJIaZxBIUuMMAklqnEEgSY0zCCSpcQaBJDXOIJCkxhkEktQ4g0CSGmcQSFLjDAJJapxBIEmNMwgkqXEGgSQ1ziCQpMYZBJLUOINAkhpnEEhS4wwCSWqcQSBJjTMIJKlxBoEkNa63IEiyLclDSe6Ypj9JLk2yL8ntSV7cVy2SpOn1OSO4Elh/hP4NwJrusQn4wx5rkSRNo7cgqKqbgK8fYchZwFU1sAs4LsnxfdUjSRpvkscIVgIPDC3v79oOk2RTkj1J9hw4cGBeipOkVkwyCDKmrcYNrKqtVbWuqtatWLGi57IkqS2TDIL9wAlDy6uABydUiyQ1a5JBcB1wTnf20MuBb1XVlydYjyQ1aWlfb5zkauBMYHmS/cB7gGMAqmoLsAN4HbAPeAx4a1+1SJKm11sQVNXGGfoLeEdfP1+SNDteWSxJjTMIJKlxBoEkNc4gkKTGGQSS1DiDQJIaZxBIUuMMAklqnEEgSY0zCCSpcQaBJDXOIJCkxhkEktQ4g0CSGmcQSFLjDAJJapxBIEmNMwgkqXEGgSQ1ziCQpMYZBJLUOINAkhpnEEhS4wwCSWqcQSBJjes1CJKsT3J3kn1JNo/pf0aSv0jyuSR7k7y1z3okSYdb2tcbJ1kCXAb8HLAf2J3kuqq6c2jYO4A7q+oXkqwA7k7yP6rq8b7qkjSz1Zs/OukS+MLFr590Cc3oc0ZwOrCvqu7tPti3A2eNjCng6UkCPA34OnCwx5okSSP6DIKVwANDy/u7tmEfAP4R8CDweeBdVfXE6Bsl2ZRkT5I9Bw4c6KteSWpSn0GQMW01svxa4DbgOcBpwAeS/NhhL6raWlXrqmrdihUr5r5SSWpYn0GwHzhhaHkVg7/8h70VuLYG9gH3AS/osSZJ0og+g2A3sCbJyUmOBc4GrhsZcz/wTwGSPBt4PnBvjzVJkkb0dtZQVR1MciGwE1gCbKuqvUku6Pq3AO8FrkzyeQa7ki6qqof7qkmSdLjeggCgqnYAO0batgw9fxD4+T5rkCQdmVcWS1LjDAJJapxBIEmNMwgkqXEGgSQ1ziCQpMYZBJLUOINAkhpnEEhS4wwCSWrcrIMgyUlJ/ln3fFmSp/dXliRpvszqu4aSvA3YBDwTeC6Dr5TeQvfNoVq8vCWhpNnOCN4BvBJ4BKCq/gb4ib6KkiTNn9kGwd8N31A+yVIOv9uYJGkRmu3XUN+Y5HeAZUl+Dng78Bf9lSXNP3eTqVWznRFsBg4wuMH8+QzuMfDv+ipKkjR/ZjsjWMbgDmN/BJBkSdf2WF+FSZLmx2xnBJ9g8MF/yDLgf899OZKk+TbbIPgHVfXooYXu+Y/0U5IkaT7NNgi+neTFhxaSvAT4Tj8lSZLm02yPEbwb+HCSB7vl44Ff7ackSdJ8mlUQVNXuJC8Ang8E+Ouq+m6vlUmS5sURgyDJa6rqk0l+eaRrTRKq6toea5MkzYOZZgRnAJ8EfmFMXwEGgSQtckcMgqp6T5IfAq6vqv95tG+eZD1wCbAEuKKqLh4z5kzgD4BjgIer6oyj/TmSpCdvxrOGquoJ4MKjfePuorPLgA3AWmBjkrUjY44DLgfeUFUvBN54tD9HkvSDme3pox9P8ptJTkjyzEOPGV5zOrCvqu7tvrBuO3DWyJg3A9dW1f0AVfXQUVUvSfqBzfb00d9gcEzg7SPtP3mE16wEHhha3g+8bGTM84BjktwAPB24pKquGn2jJJsY3A+BE088cZYlS5JmY7ZBsJZBCPwMg0C4mcGNaY4kY9pGv7p6KfASBje4WQZ8Ksmuqrrn772oaiuwFWDdunV+/bUkzaHZBsF/Y3BTmku75Y1d25uO8Jr9wAlDy6uAB8eMebiqvs3g6uWbgFOBe5AkzYvZBsHzq+rUoeW/TPK5GV6zm8H1BicDXwLOZnBMYNifAx/obnRzLINdR78/y5okSXNgtkHw2SQvr6pdAEleBvzVkV5QVQeTXAjsZHD66Laq2pvkgq5/S1XdleRjwO3AEwxOMb3jyf4ykqSjN9sgeBlwTpL7u+UTgbuSfB6oqjpl3IuqageDm9gMt20ZWX4/8P6jqlqSNGdmGwTre61CkjQxs/3SuS/2XYgkaTJme0GZJOkpyiCQpMYZBJLUOINAkhpnEEhS4wwCSWqcQSBJjTMIJKlxBoEkNc4gkKTGGQSS1DiDQJIaZxBIUuMMAklqnEEgSY0zCCSpcQaBJDXOIJCkxhkEktQ4g0CSGmcQSFLjlk66gElYvfmjky6BL1z8+kmXIEmAMwJJal6vQZBkfZK7k+xLsvkI416a5HtJfqXPeiRJh+stCJIsAS4DNgBrgY1J1k4z7n3Azr5qkSRNr88ZwenAvqq6t6oeB7YDZ40Z907gfwEP9ViLJGkafQbBSuCBoeX9Xdv3JVkJ/BKw5UhvlGRTkj1J9hw4cGDOC5WklvUZBBnTViPLfwBcVFXfO9IbVdXWqlpXVetWrFgxZwVKkvo9fXQ/cMLQ8irgwZEx64DtSQCWA69LcrCq/qzHuiRJQ/oMgt3AmiQnA18CzgbePDygqk4+9DzJlcBHDAFJml+9BUFVHUxyIYOzgZYA26pqb5ILuv4jHheQJM2PXq8srqodwI6RtrEBUFX/os9aJEnjeWWxJDXOIJCkxhkEktQ4g0CSGmcQSFLjDAJJapxBIEmNMwgkqXEGgSQ1ziCQpMYZBJLUOINAkhpnEEhS4wwCSWqcQSBJjTMIJKlxBoEkNc4gkKTGGQSS1DiDQJIaZxBIUuMMAklqnEEgSY0zCCSpcQaBJDWu1yBIsj7J3Un2Jdk8pv8tSW7vHrckObXPeiRJh+stCJIsAS4DNgBrgY1J1o4Muw84o6pOAd4LbO2rHknSeH3OCE4H9lXVvVX1OLAdOGt4QFXdUlXf6BZ3Aat6rEeSNEafQbASeGBoeX/XNp1zgevHdSTZlGRPkj0HDhyYwxIlSX0GQca01diByasZBMFF4/qramtVrauqdStWrJjDEiVJS3t87/3ACUPLq4AHRwclOQW4AthQVV/rsR5J0hh9zgh2A2uSnJzkWOBs4LrhAUlOBK4Ffq2q7umxFknSNHqbEVTVwSQXAjuBJcC2qtqb5IKufwvwu8CzgMuTABysqnV91SRJOlyfu4aoqh3AjpG2LUPPzwPO67MGSdKReWWxJDXOIJCkxhkEktQ4g0CSGmcQSFLjDAJJapxBIEmNMwgkqXEGgSQ1ziCQpMYZBJLUOINAkhpnEEhS4wwCSWqcQSBJjTMIJKlxBoEkNc4gkKTGGQSS1DiDQJIaZxBIUuMMAklqnEEgSY0zCCSpcQaBJDWu1yBIsj7J3Un2Jdk8pj9JLu36b0/y4j7rkSQdrrcgSLIEuAzYAKwFNiZZOzJsA7Cme2wC/rCveiRJ4/U5Izgd2FdV91bV48B24KyRMWcBV9XALuC4JMf3WJMkacTSHt97JfDA0PJ+4GWzGLMS+PLwoCSbGMwYAB5NcvfclvqkLAcefrIvzvvmsJLJc11McV1McV1MWQjr4qTpOvoMgoxpqycxhqraCmydi6LmSpI9VbVu0nUsBK6LKa6LKa6LKQt9XfS5a2g/cMLQ8irgwScxRpLUoz6DYDewJsnJSY4FzgauGxlzHXBOd/bQy4FvVdWXR99IktSf3nYNVdXBJBcCO4ElwLaq2pvkgq5/C7ADeB2wD3gMeGtf9fRgQe2qmjDXxRTXxRTXxZQFvS5SddgueUlSQ7yyWJIaZxBIUuMMAklqnEEgSY3r84Kyp5Qkz2Zw1XMBD1bVVydckhYAt4sprospi21deNbQDJKcBmwBngF8qWteBXwTeHtV3Tqp2iZpsW3oc83tYorrYspiXRcGwQyS3AacX1WfHml/OfDBqjp1MpVNxmLd0Oea28UU18WUxbou3DU0sx8d/Z8KUFW7kvzoJAqasCuZfkP/E2BBbug9cLuY4rqYsijXhUEws+uTfBS4iqlvSj0BOAf42MSqmpxFuaH3wO1iiutiyqJcF+4amoUkGxjcO2Elg29M3Q9cV1U7JlrYBCS5FHgu4zf0+6rqwknVNt/cLqa4LqYsxnVhEOioLcYNXdL0DIIfQJJN3b0SpO9zu5jiupiykNeFF5T9YMbdWKdZ3Z3k5HYxzHUxZcGuCw8Wz0KSFzC1K6QY3Dznuqr64EQLW3gW7Ibeh267WAl8uqoeHer64oRKmpgkpwNVVbuTrAXWA3/tvxFIclVVnbOQ14VBMIMkFwEbge3A/+maVwFXJ9leVRdPrLiF5/FJFzBfkvwr4B3AXcAfJ3lXVf151/17LOAzROZakvcAG4ClST7O4N7kNwCbk7yoqv7TJOubT0lGb74V4NVJjgOoqjfMf1Uz8xjBDJLcA7ywqr470n4ssLeq1kymsoUnyf1VdeKk65gPST4PvKKqHk2yGrgG+O9VdUmSz1bViyZa4Dzq1sVpwA8DXwFWVdUjSZYxmC2dMtEC51GSW4E7gSsY7D0IcDWDOzRSVTdOrrrpOSOY2RPAczh8un9819eUJLdP1wU8ez5rmbAlh3YHVdUXkpwJXJPkJBrbRQYcrKrvAY8l+b9V9QhAVX0nSWv/RtYB7wL+LfBbVXVbku8s1AA4xCCY2buBTyT5G6bOmz8R+CmgmXPmhzwbeC3wjZH2ALfMfzkT85Ukp1XVbQDdzOCfA9uAfzzZ0ubd40l+pKoeA15yqDHJM2jsj6WqegL4/SQf7v77VRbB5+yCL3DSqupjSZ4HnM7fP29+d/dXUGs+Ajzt0AfgsCQ3zH85E3MOcHC4oaoOAuckWbAHBXvyqqr6O/j+B+EhxwC/PpmSJquq9gNvTPJ64JFJ1zMTjxFIUuO8jkCSGmcQSFLjDALpKCV5TpJrJl2HNFc8RiBJjXNGIB1BkvclefvQ8r9P8q+T3NEtL0ny/iS7k9ye5Pyu/fIkb+ie/2mSbd3zc5P8x0n8LtJ0DALpyLYDvzq0/CZg99DyucC3quqlwEuBtyU5GbgJ+NluzEpgbff8Z4Cbe61YOkoGgXQEVfVZ4Ce64wKnMriQ7v6hIT/P4NqB24BPA88C1jD4sP/Z7gvY7gS+muR44BW0deGdFgEvKJNmdg3wK8A/ZDBDGBbgnVW1c/RFSX6cwbdw3gQ8k8Fs4tGq+n/9lisdHYNAmtl24I+A5cAZDL5c7ZCdwL9M8smq+m53FfqXqurbwKcYfEXJaxjMFK7pHtKC4q4haQZVtRd4OoMP+C+PdF/BYNfPrd0B5A8y9QfWzcDSqtoH3MpgVuDxAS04nj4qSY1zRiBJjTMIJKlxBoEkNc4gkKTGGQSS1DiDQJIaZxBIUuP+P46f3v3Ysjj+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEcCAYAAAD6GqKbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAZQUlEQVR4nO3df7DddX3n8edLYjFqpfwILCZgaMlowa1Y0oCDs6viQly1uB3UuLZkt7TpKm51Z9cu2J3NVicdnJ2pu4yrlZWUwKoYWS2sFm02VF1/FIgKIiglKwhZEKKhKipo8L1/nM8dTi4nl0tyP+fkXp+PmTPne97f7+dzPp+5J3nd7697UlVIkjTXnjTpAUiSFiYDRpLUhQEjSerCgJEkdWHASJK6MGAkSV0YMNIBLMmLkuwYen1LkhfNsP01SdaOZXDS41g06QFImr2qOnFqOcl/Ao6vqt8eWv+ySYxLGsU9GElSFwaMtB+SHJPko0l2JvlukncneVKS/5DkW0nuT3JZkkPa9suTVJK1Se5K8p0kfzzU3+IklyZ5IMmtwG9Me787k7w0yWrgbcBrkzyY5Ka2/tNJfq8t7/M4pLlgwEj7KMlBwMeBbwHLgaXAFcC/aI8XA78MPB1497TmLwSeDZwO/Mckv9rq64FfaY8zgZHnU6rqk8CfAh+uqqdX1fNGbLY/45D2mwEj7btVwDOBt1bVD6vqoar6HPB64M+q6ptV9SBwAbAmyfA5zz+pqh9X1U3ATcBUQLwG2FBVu6rqbuCi/Rjf/oxD2m8GjLTvjgG+VVW7p9WfyWCvZsq3GFxQc9RQ7dtDyz9isHcx1fbuaW331f6MQ9pvBoy07+4Gjp22RwBwD/CsodfHAruB+2bR570Mgmu47d483p9C359xSPvNgJH23fUMAuHCJE9L8pQkpwEfAv5NkuOSPJ1Hz5VM39MZZTNwQZJDkywD/vUM294HLE+yt3/H+zMOab8ZMNI+qqpHgFcCxwN3ATuA1wIbgcuBzwJ3AA8xc1AM+xMGh7LuAP669bM3H2nP303y5RHr92cc0n6LXzgmSerBPRhJUhcGjCSpCwNGktSFASNJ6sK/ptwcccQRtXz58kkPQ5LmlS996Uvfqaolo9YZMM3y5cvZtm3bpIchSfNKkr3+tQkPkUmSujBgJEldGDCSpC4MGElSFwaMJKkLA0aS1IUBI0nqwoCRJHVhwEiSuvBOfkk6AC0//xNjfb87L3z5nPfpHowkqQsDRpLUhQEjSerCgJEkdWHASJK66BowSe5McnOSG5Nsa7XDkmxJcnt7PnRo+wuSbE9yW5Izh+ont362J7koSVr94CQfbvXrkiwfarO2vcftSdb2nKck6bHGsQfz4qo6qapWttfnA1uragWwtb0myQnAGuBEYDXwniQHtTbvBdYBK9pjdaufCzxQVccD7wLe2fo6DFgPnAKsAtYPB5kkqb9JHCI7C9jUljcBrxqqX1FVD1fVHcB2YFWSo4FnVNUXq6qAy6a1merrSuD0tndzJrClqnZV1QPAFh4NJUnSGPQOmAL+OsmXkqxrtaOq6l6A9nxkqy8F7h5qu6PVlrbl6fU92lTVbuB7wOEz9CVJGpPed/KfVlX3JDkS2JLkGzNsmxG1mqG+r20efcNB6K0DOPbYY2cYmiTpieq6B1NV97Tn+4GPMTgfcl877EV7vr9tvgM4Zqj5MuCeVl82or5HmySLgEOAXTP0NX18F1fVyqpauWTJkn2fqCTpMboFTJKnJfnFqWXgDOBrwNXA1FVda4Gr2vLVwJp2ZdhxDE7mX98Oo/0gyant/Mo509pM9XU2cG07T/Mp4Iwkh7aT+2e0miRpTHoeIjsK+Fi7ongR8MGq+mSSG4DNSc4F7gJeDVBVtyTZDNwK7AbOq6pHWl9vAC4FFgPXtAfAJcDlSbYz2HNZ0/raleQdwA1tu7dX1a6Oc5UkTdMtYKrqm8DzRtS/C5y+lzYbgA0j6tuA546oP0QLqBHrNgIbn9ioJUlzxTv5JUldGDCSpC4MGElSFwaMJKkLA0aS1IUBI0nqwoCRJHVhwEiSujBgJEldGDCSpC4MGElSFwaMJKkLA0aS1IUBI0nqovdXJi94y8//xFjf784LXz7W99P85udTk+QejCSpCwNGktSFASNJ6sJzMJLmLc8xHdjcg5EkdWHASJK6MGAkSV0YMJKkLgwYSVIXBowkqQsDRpLUhQEjSerCgJEkdWHASJK6MGAkSV0YMJKkLroHTJKDknwlycfb68OSbElye3s+dGjbC5JsT3JbkjOH6icnubmtuyhJWv3gJB9u9euSLB9qs7a9x+1J1vaepyRpT+PYg3kz8PWh1+cDW6tqBbC1vSbJCcAa4ERgNfCeJAe1Nu8F1gEr2mN1q58LPFBVxwPvAt7Z+joMWA+cAqwC1g8HmSSpv64Bk2QZ8HLg/UPls4BNbXkT8Kqh+hVV9XBV3QFsB1YlORp4RlV9saoKuGxam6m+rgROb3s3ZwJbqmpXVT0AbOHRUJIkjUHvPZj/AvwR8LOh2lFVdS9Aez6y1ZcCdw9tt6PVlrbl6fU92lTVbuB7wOEz9LWHJOuSbEuybefOnfsyP0nSXnQLmCSvAO6vqi/NtsmIWs1Q39c2jxaqLq6qlVW1csmSJbMcpiRpNnruwZwG/GaSO4ErgJck+R/Afe2wF+35/rb9DuCYofbLgHtafdmI+h5tkiwCDgF2zdCXJGlMugVMVV1QVcuqajmDk/fXVtVvA1cDU1d1rQWuastXA2valWHHMTiZf307jPaDJKe28yvnTGsz1dfZ7T0K+BRwRpJD28n9M1pNkjQmiybwnhcCm5OcC9wFvBqgqm5Jshm4FdgNnFdVj7Q2bwAuBRYD17QHwCXA5Um2M9hzWdP62pXkHcANbbu3V9Wu3hOTJD1qLAFTVZ8GPt2WvwucvpftNgAbRtS3Ac8dUX+IFlAj1m0ENu7rmCVJ+8c7+SVJXRgwkqQuDBhJUhcGjCSpCwNGktSFASNJ6sKAkSR1YcBIkrowYCRJXRgwkqQuDBhJUhcGjCSpCwNGktSFASNJ6sKAkSR1YcBIkrowYCRJXRgwkqQuDBhJUhcGjCSpCwNGktSFASNJ6sKAkSR1YcBIkrowYCRJXRgwkqQuDBhJUhcGjCSpCwNGktSFASNJ6qJbwCR5SpLrk9yU5JYkf9LqhyXZkuT29nzoUJsLkmxPcluSM4fqJye5ua27KEla/eAkH27165IsH2qztr3H7UnW9pqnJGm0nnswDwMvqarnAScBq5OcCpwPbK2qFcDW9pokJwBrgBOB1cB7khzU+novsA5Y0R6rW/1c4IGqOh54F/DO1tdhwHrgFGAVsH44yCRJ/XULmBp4sL18cnsUcBawqdU3Aa9qy2cBV1TVw1V1B7AdWJXkaOAZVfXFqirgsmltpvq6Eji97d2cCWypql1V9QCwhUdDSZI0BrMOmCTPSvLStrw4yS/Oos1BSW4E7mfwH/51wFFVdS9Aez6ybb4UuHuo+Y5WW9qWp9f3aFNVu4HvAYfP0Nf08a1Lsi3Jtp07dz7edCRJT8CsAibJ7zPYQ3hfKy0D/vLx2lXVI1V1Utt+VZLnzvQ2o7qYob6vbYbHd3FVrayqlUuWLJlhaJKkJ2q2ezDnAacB3weoqtt5dM/jcVXV3wOfZnCY6r522Iv2fH/bbAdwzFCzZcA9rb5sRH2PNkkWAYcAu2boS5I0JrMNmIer6idTL9p/5o/ZIxiWZEmSX2rLi4GXAt8ArgamrupaC1zVlq8G1rQrw45jcDL/+nYY7QdJTm3nV86Z1maqr7OBa9t5mk8BZyQ5tJ3cP6PVJEljsmiW230myduAxUn+CfBG4H89TpujgU3tSrAnAZur6uNJvghsTnIucBfwaoCquiXJZuBWYDdwXlU90vp6A3ApsBi4pj0ALgEuT7KdwZ7LmtbXriTvAG5o2729qnbNcq6SpDkw24A5n8ElwTcDfwD8FfD+mRpU1VeB54+ofxc4fS9tNgAbRtS3AY85f1NVD9ECasS6jcDGmcYoSepntgGzGNhYVf8dBleHtdqPeg1MkjS/zfYczFYGgTJlMfC/5344kqSFYrYB85ShmyZpy0/tMyRJ0kIw24D5YZJfn3qR5GTgx32GJElaCGZ7DuYtwEeSTN1LcjTw2j5DkiQtBLMKmKq6IclzgGczuEv+G1X1064jkyTNazMGTJKXVNW1SX5r2qoVSaiqj3YcmyRpHnu8PZh/DFwLvHLEugIMGEnSSDMGTFWtT/Ik4Jqq2jymMUljs/z8T4z1/e688OVjfT9pkh73HExV/SzJmwAD5ueQ/wFL2lezvUx5S5J/l+SY9pXHh7VvjZQkaaTZXqb8uwzOubxxWv2X53Y4kqSFYrYBcwKDcHkhg6D5P8Cf9xqUJGn+m23AbGLwZWMXtdeva7XX9BiUJGn+m23APLuqnjf0+m+S3NRjQJKkhWG2J/m/kuTUqRdJTgE+32dIkqSFYLZ7MKcA5yS5q70+Fvh6kpuBqqpf6zI6SdK8NduAWd11FJKkBWe2f+zyW70HIklaWGZ7DkaSpCfEgJEkdWHASJK6MGAkSV0YMJKkLgwYSVIXBowkqQsDRpLUhQEjSerCgJEkdWHASJK6MGAkSV10C5gkxyT5myRfT3JLkje3+mFJtiS5vT0fOtTmgiTbk9yW5Myh+slJbm7rLkqSVj84yYdb/boky4farG3vcXuStb3mKUkarecezG7g31bVrwKnAuclOQE4H9haVSuAre01bd0a4EQGXw/wniQHtb7eC6wDVrTH1NcHnAs8UFXHA+8C3tn6OgxYz+B7bFYB64eDTJLUX7eAqap7q+rLbfkHwNeBpcBZwKa22SbgVW35LOCKqnq4qu4AtgOrkhwNPKOqvlhVBVw2rc1UX1cCp7e9mzOBLVW1q6oeALbgd9pI0liN5RxMO3T1fOA64KiquhcGIQQc2TZbCtw91GxHqy1ty9Pre7Spqt3A94DDZ+hr+rjWJdmWZNvOnTv3fYKSpMfoHjBJng78T+AtVfX9mTYdUasZ6vva5tFC1cVVtbKqVi5ZsmSGoUmSnqiuAZPkyQzC5QNV9dFWvq8d9qI939/qO4BjhpovA+5p9WUj6nu0SbIIOATYNUNfkqQx6XkVWYBLgK9X1Z8NrboamLqqay1w1VB9Tbsy7DgGJ/Ovb4fRfpDk1NbnOdPaTPV1NnBtO0/zKeCMJIe2k/tntJokaUwWdez7NOB3gJuT3NhqbwMuBDYnORe4C3g1QFXdkmQzcCuDK9DOq6pHWrs3AJcCi4Fr2gMGAXZ5ku0M9lzWtL52JXkHcEPb7u1VtavXRCVJj9UtYKrqc4w+FwJw+l7abAA2jKhvA547ov4QLaBGrNsIbJzteCVJc8s7+SVJXRgwkqQuDBhJUhcGjCSpCwNGktSFASNJ6sKAkSR1YcBIkrowYCRJXRgwkqQuDBhJUhcGjCSpCwNGktSFASNJ6sKAkSR1YcBIkrowYCRJXRgwkqQuDBhJUhcGjCSpCwNGktSFASNJ6sKAkSR1YcBIkrowYCRJXRgwkqQuDBhJUhcGjCSpCwNGktSFASNJ6qJbwCTZmOT+JF8bqh2WZEuS29vzoUPrLkiyPcltSc4cqp+c5Oa27qIkafWDk3y41a9Lsnyozdr2HrcnWdtrjpKkveu5B3MpsHpa7Xxga1WtALa21yQ5AVgDnNjavCfJQa3Ne4F1wIr2mOrzXOCBqjoeeBfwztbXYcB64BRgFbB+OMgkSePRLWCq6rPArmnls4BNbXkT8Kqh+hVV9XBV3QFsB1YlORp4RlV9saoKuGxam6m+rgROb3s3ZwJbqmpXVT0AbOGxQSdJ6mzc52COqqp7Adrzka2+FLh7aLsdrba0LU+v79GmqnYD3wMOn6Gvx0iyLsm2JNt27ty5H9OSJE13oJzkz4hazVDf1zZ7FqsurqqVVbVyyZIlsxqoJGl2xh0w97XDXrTn+1t9B3DM0HbLgHtafdmI+h5tkiwCDmFwSG5vfUmSxmjcAXM1MHVV11rgqqH6mnZl2HEMTuZf3w6j/SDJqe38yjnT2kz1dTZwbTtP8yngjCSHtpP7Z7SaJGmMFvXqOMmHgBcBRyTZweDKrguBzUnOBe4CXg1QVbck2QzcCuwGzquqR1pXb2BwRdpi4Jr2ALgEuDzJdgZ7LmtaX7uSvAO4oW339qqafrGBJKmzbgFTVa/by6rT97L9BmDDiPo24Lkj6g/RAmrEuo3AxlkPVpI05w6Uk/ySpAXGgJEkdWHASJK6MGAkSV0YMJKkLgwYSVIXBowkqQsDRpLUhQEjSerCgJEkdWHASJK6MGAkSV0YMJKkLgwYSVIXBowkqQsDRpLUhQEjSerCgJEkdWHASJK6MGAkSV0YMJKkLgwYSVIXBowkqQsDRpLUhQEjSerCgJEkdWHASJK6MGAkSV0YMJKkLgwYSVIXBowkqYsFHTBJVie5Lcn2JOdPejyS9PNkwQZMkoOA/wa8DDgBeF2SEyY7Kkn6+bFgAwZYBWyvqm9W1U+AK4CzJjwmSfq5kaqa9Bi6SHI2sLqqfq+9/h3glKp609A264B17eWzgdvGOMQjgO+M8f3GzfnNb85v/hr33J5VVUtGrVg0xkGMW0bU9kjTqroYuHg8w9lTkm1VtXIS7z0Ozm9+c37z14E0t4V8iGwHcMzQ62XAPRMaiyT93FnIAXMDsCLJcUl+AVgDXD3hMUnSz40Fe4isqnYneRPwKeAgYGNV3TLhYQ2byKG5MXJ+85vzm78OmLkt2JP8kqTJWsiHyCRJE2TASJK6MGAkSV0YMJKkLhbsVWQHoiRHAUsZ3PB5T1XdN+Eh6QlYyD+/hTw3cH6T4lVkY5DkJODPgUOA/9fKy4C/B95YVV+e1Njm0oH6Id9fC/nnt5DnBs5v0vMzYMYgyY3AH1TVddPqpwLvq6rnTWZkc+NA/5Dvr4X881vIcwPnN+n5eYhsPJ42/QMAUFV/m+RpkxjQHLuUvX/I/wKY1/+IWdg/v4U8N3B+E2XAjMc1ST4BXAbc3WrHAOcAn5zYqObOAf0hnwML+ee3kOcGzm+iPEQ2JklexuD7aJYy+EvPO4Crq+qvJjqwOZDkIuBXGP0hv2P4KxLmqwX+81uwcwPnN0kGjObEgfwhlzQZ3gczYe1Lz+a9qrqmqv5VVb2yql7Rlhd8uCyUn98oC3lu4PzGwYCZvFFfjLZgHAgf8s4W8s9vIc8NnF93nuQfkyTP4dFDSMXgy8+urqr3TXRg/U38Qz4X2s9vKXBdVT04tOpbExrSnEmyCqiquiHJCcBq4BsL9bOZ5LKqOmchzi/JC4FVwNcOhPkZMGOQ5N8DrwOuAK5v5WXAh5JcUVUXTmxw/f1k0gPYX0n+EDgP+DpwSZI3V9VVbfWfcgBcrbOvkqwHXgYsSrIFOAX4NHB+kudX1YZJjm9/JZn+JYMBXpzklwCq6jfHP6q5k+T6qlrVln+fwef0Y8D6JL8+6f9bPMk/Bkn+Djixqn46rf4LwC1VtWIyI+svyV1Vdeykx7E/ktwMvKCqHkyyHLgSuLyq/muSr1TV8yc6wP3Q5nYScDDwbWBZVX0/yWIGe2u/NtEB7qckXwZuBd7P4MhBgA8x+IZbquozkxvd/hv+/CW5AfinVbWz3R7wt1X1Dyc5PvdgxuNnwDN57OGUo9u6eS3JV/e2CjhqnGPp5KCpw2JVdWeSFwFXJnkW8/8Q4O6qegT4UZL/W1XfB6iqHyeZ959NYCXwZuCPgbdW1Y1Jfjzfg2XIk5IcyuB8eqpqJ0BV/TDJ7skOzYAZl7cAW5PczqP3iRwLHA/M+3tEGITImcAD0+oBvjD+4cy5byc5qapuBGh7Mq8ANgIT/Q1xDvwkyVOr6kfAyVPFJIewAH75qaqfAe9K8pH2fB8L6/+9Q4AvMfi3Vkn+QVV9O8nTOQB++fEQ2ZgkeRKDk2/D94nc0H57nNeSXAL8RVV9bsS6D1bVP5/AsOZMkmUMftP/9oh1p1XV5ycwrDmR5OCqenhE/Qjg6Kq6eQLD6ibJy4HTquptkx5LT0meChxVVXdMdBwGjCSpB++DkSR1YcBIkrowYKQDWJJLk5zdlt/fboQkydumbbcQLqbQAuM5GOkAluRS4ONVdeW0+oNV9fTJjEqaHfdgpDmU5JwkX01yU5LLkzwrydZW25rk2LbdpUkuSvKFJN8c2ktJkncnubV9z8eRQ31/OsnKJBcCi5PcmOQDbd2DQ+3/c5KvJbk5yWtb/UWt/ZVJvpHkA0kmfhmrFraFdD24NFFJTmRwQ99pVfWdJIcBm4DLqmpTkt8FLgJe1ZocDbwQeA5wNYO/EPDPgGczuL/mKAZ3oW8cfp+qOj/Jm6rqpBHD+C0Gd+Y/DzgCuCHJZ9u65wMnMvg7eJ8HTgMec2m5NFfcg5HmzkuAK6vqOwBVtQt4AfDBtv5yBoEy5S+r6mdVdSuP/sWDfwR8qKoeqap7gGuf4BheONT+PuAzwG+0dddX1Y528+GNwPIn2Lf0hBgw0twJg793NZPh9cM3OGYv2+zLGPZm+P0ewSMY6syAkebOVuA1SQ4HaIfIvkD7w4rA63n8Q1KfBdYkOSjJ0cCL97LdT5M8eS/tX9vaL2GwR3T9iO2k7vwNRpojVXVLkg3AZ5I8AnwF+ENgY5K3AjuBf/k43XyMwaG2m4G/Y3CIa5SLga8m+XJVvX5a+xcANzHYE/qj9repnrOv85L2lZcpS5K68BCZJKkLA0aS1IUBI0nqwoCRJHVhwEiSujBgJEldGDCSpC7+PzKZZo2yNp7xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEkCAYAAAASMydbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAeeElEQVR4nO3dfbxVZZ338c9XMEXzARQNQcOSMnXKlNApK8sGKCu9Z3Si7kYsim6zpu5XTWEz87LRmHSaO2estCxJtClFS6UHR1HDcdIUfERFg3wCcZQEHyq10O/9x7pObg6b4wH3dQ7n+H2/Xvu1177WutZv7Q3nfM9a19pryTYRERGdtll/b0BERAxOCZiIiKgiARMREVUkYCIioooETEREVJGAiYiIKhIwERFRRQImogJJ35T0j/29HRH9SfmiZURE1JA9mIj1kDS0v7ehv+UziBciARPRQtK9kj4v6Vbgd5IOknSNpEcl3SLp4LLcFEkLu/X9v5LmlumzJH2pZd67Jd1c1nONpNeW9g9J+nHLckslzWl5vUzSvj1srySdIulhSY9JulXSPmXeMEn/T9J9Zd5/SxpW5r1X0u1le+ZLek0Pn8FQSQe2+xwiemQ7jzzyKA/gXuBmYFdgNPAI8C6aP8b+orweCWwFPAGMa+m7AJhSps8CvlSm9wMeBg4AhgBTS50tgFcAj5b1jwLuAx4o/V4BrAY262F7JwE3ANsDAl4DjCrzvgHML+9jCPDGUvNVwO/K+9kc+BywFHhJm89gWE+fQ3//e+WxaT+yBxOxrlNtLwM+CPzM9s9sP2t7HrAQeJft3wMXA+8HkDQO2BOY22Z9HwW+Zfs628/Yng08DRxo+26aoNoXeCtwKfCApD3L66ttP9vDtv4R2KbUlu3Fth+UtBnwYeBTth8oda+x/TTwPuCntufZ/iPwrzRB8sbun4HtJ3v6HDboU40XnQRMxLqWleeXA0eWw0KPSnoUOIhmTwPg+5SAAT4AXFSCp7uXA5/ptp5dgV3K/KuAg4G3lOn5NOHy1vJ6vWxfCXydZm/lIUlnSNoW2BHYEvh1m2670Owpda3j2fKeR7f5DHrzOUS0lYCJWFfXqZXLgHNsb9/y2Nr2SWX+ZcCOZYzk/TSB084yYGa39Wxl+wdlflfAvLlMX0UvAwbA9qm29wf2pjn89XfAb4CngFe26bKCJjSAZhyHJvAeaPMZ9OZziGgrAROxft8D3iNpkqQhkraUdLCkMQC21wAXAF8BRgDz1rOebwP/R9IBZVB+a0mHStqmzL8KeBswzPZy4GpgMrADcFNPGyjpDWW9m9OMqzwFPFP2SmYBX5W0S9n+P5e0BTAHOFTSIaXfZ2gO2V2zMZ9DxPokYCLWo4zDHAZ8AVhJ85f837H2z833gXcA55fAabeehTTjMF+nGbRfChzdMv9XwG9pggXbjwN3A7+w/czzbOa2NAG2muaw1yM0YyoAnwUW0Zx8sAo4meaEgbtoxlW+RrOn8x7gPbb/8AI+h4h15IuWERFRRf4CiYiIKvIt3YhNnKQ3A5e0m2f7pX28ORG9lkNkERFRRQ6RRUREFTlEVuy4444eO3Zsf29GRMSAcsMNN/zG9sh28xIwxdixY1m4cOHzLxgREX8i6b71zcshsoiIqCIBExERVSRgIiKiigRMRERUkYCJiIgqEjAREVFFAiYiIqpIwERERBX5ouUmaOyMn25Uv3tPOrTDWxIRsfGyBxMREVUkYCIioooETEREVJGAiYiIKhIwERFRRQImIiKqSMBEREQVCZiIiKgiARMREVUkYCIioooETEREVJGAiYiIKhIwERFRRQImIiKqqBowku6VtEjSzZIWlrYRkuZJWlKeh7csf5ykpZLukjSppX3/sp6lkk6VpNK+haTzSvt1ksa29JlaaiyRNLXm+4yIiHX1xR7M22zva3t8eT0DuML2OOCK8hpJewFTgL2BycBpkoaUPqcD04Fx5TG5tE8DVtveAzgFOLmsawRwPHAAMAE4vjXIIiKivv644dhhwMFlejYwH/h8aT/X9tPAPZKWAhMk3Qtsa/taAElnA4cDl5Q+XyzrugD4etm7mQTMs72q9JlHE0o/qPzeIiI2WX19M8PaezAGLpN0g6TppW1n2w8ClOedSvtoYFlL3+WlbXSZ7t6+Vh/ba4DHgB16WNdaJE2XtFDSwpUrV270m4yIiHXV3oN5k+0VknYC5km6s4dl1abNPbRvbJ/nGuwzgDMAxo8fv878iIjYeFX3YGyvKM8PAxfSjIc8JGkUQHl+uCy+HNi1pfsYYEVpH9Omfa0+koYC2wGrelhXRET0kWoBI2lrSdt0TQMTgduAuUDXWV1TgYvL9FxgSjkzbHeawfzry2G0JyQdWMZXjurWp2tdRwBX2jZwKTBR0vAyuD+xtEVERB+peYhsZ+DCckbxUOD7tv9T0gJgjqRpwP3AkQC2b5c0B7gDWAMca/uZsq5jgLOAYTSD+5eU9jOBc8oJAatozkLD9ipJJwILynIndA34R0RE36gWMLbvBl7Xpv0R4JD19JkJzGzTvhDYp037U5SAajNvFjBrw7Y6IiI6Jd/kj4iIKhIwERFRRQImIiKqSMBEREQVCZiIiKiiP65FFhGxydqY63Vt7LW6BrvswURERBUJmIiIqCKHyKLPL+EdES8O2YOJiIgqEjAREVFFDpFFxAbJIdXorQRM9Ln8guqsfJ6xqUrAxKDX17+A8wu/s/J5DlwZg4mIiCoSMBERUUUCJiIiqsgYTEREPxns40vZg4mIiCoSMBERUUUCJiIiqkjAREREFQmYiIioIgETERFVJGAiIqKKBExERFSRgImIiCoSMBERUUX1gJE0RNJNkn5SXo+QNE/SkvI8vGXZ4yQtlXSXpEkt7ftLWlTmnSpJpX0LSeeV9uskjW3pM7XUWCJpau33GRERa+uLPZhPAYtbXs8ArrA9DriivEbSXsAUYG9gMnCapCGlz+nAdGBceUwu7dOA1bb3AE4BTi7rGgEcDxwATACObw2yiIior2rASBoDHAp8p6X5MGB2mZ4NHN7Sfq7tp23fAywFJkgaBWxr+1rbBs7u1qdrXRcAh5S9m0nAPNurbK8G5vFcKEVERB+ovQfzb8DngGdb2na2/SBAed6ptI8GlrUst7y0jS7T3dvX6mN7DfAYsEMP64qIiD5SLWAkvRt42PYNve3Sps09tG9sn9ZtnC5poaSFK1eu7OVmRkREb9Tcg3kT8F5J9wLnAm+X9D3goXLYi/L8cFl+ObBrS/8xwIrSPqZN+1p9JA0FtgNW9bCutdg+w/Z42+NHjhy58e80IiLWUS1gbB9ne4ztsTSD91fa/iAwF+g6q2sqcHGZngtMKWeG7U4zmH99OYz2hKQDy/jKUd36dK3riFLDwKXAREnDy+D+xNIWERF9pD/uaHkSMEfSNOB+4EgA27dLmgPcAawBjrX9TOlzDHAWMAy4pDwAzgTOkbSUZs9lSlnXKkknAgvKcifYXlX7jUVExHP6JGBszwfml+lHgEPWs9xMYGab9oXAPm3an6IEVJt5s4BZG7vNERHxwuSb/BERUUUCJiIiqkjAREREFQmYiIioIgETERFVJGAiIqKKBExERFSRgImIiCoSMBERUUUCJiIiqkjAREREFf1xscsBZ+yMn25Uv3tPOrTDWxIRMXBkDyYiIqpIwERERBUJmIiIqCIBExERVSRgIiKiigRMRERUkYCJiIgqEjAREVFFAiYiIqpIwERERBUJmIiIqCIBExERVSRgIiKiigRMRERUkYCJiIgqeh0wkl4u6R1lepikbeptVkREDHS9ChhJHwUuAL5VmsYAF9XaqIiIGPh6uwdzLPAm4HEA20uAnXrqIGlLSddLukXS7ZL+qbSPkDRP0pLyPLylz3GSlkq6S9Kklvb9JS0q806VpNK+haTzSvt1ksa29JlaaiyRNLWX7zMiIjqktwHztO0/dL2QNBTw8/UB3m77dcC+wGRJBwIzgCtsjwOuKK+RtBcwBdgbmAycJmlIWdfpwHRgXHlMLu3TgNW29wBOAU4u6xoBHA8cAEwAjm8NsoiIqK+3AXOVpC8AwyT9BXA+8OOeOrjx2/Jy8/IwcBgwu7TPBg4v04cB59p+2vY9wFJggqRRwLa2r7Vt4OxufbrWdQFwSNm7mQTMs73K9mpgHs+FUkRE9IHeBswMYCWwCPgY8DPgH56vk6Qhkm4GHqb5hX8dsLPtBwHKc9ehttHAspbuy0vb6DLdvX2tPrbXAI8BO/SwroiI6CNDe7ncMGCW7W9DExyl7fc9dbL9DLCvpO2BCyXt08PiareKHto3ts9zBaXpNIfe2G233XrYtIiI2FC93YO5giZQugwDLu9tEduPAvNpDlM9VA57UZ4fLostB3Zt6TYGWFHax7RpX6tPGRfaDljVw7q6b9cZtsfbHj9y5Mjevp2IiOiF3gbMli3jKZTprXrqIGlk2XNB0jDgHcCdwFyg66yuqcDFZXouMKWcGbY7zWD+9eUw2hOSDizjK0d169O1riOAK8s4zaXAREnDy+D+xNIWERF9pLeHyH4naT/bN0Jz2jDw5PP0GQXMLofTNgPm2P6JpGuBOZKmAfcDRwLYvl3SHOAOYA1wbDnEBnAMcBbNntMl5QFwJnCOpKU0ey5TyrpWSToRWFCWO8H2ql6+14iI6IDeBsyngfMldR1mGgW8r6cOtm8FXt+m/RHgkPX0mQnMbNO+EFhn/Mb2U5SAajNvFjCrp22MiIh6ehUwthdI2hN4Nc0A+p22/1h1yyIiYkDrMWAkvd32lZL+stuscZKw/aOK2xYREQPY8+3BvBW4EnhPm3kGEjAREdFWjwFj+3hJmwGX2J7TR9sUERGDwPOepmz7WeATfbAtERExiPT2ezDzJH1W0q7lasgjygUlIyIi2urtacofphlz+Xi39ld0dnMiImKw6G3A7EUTLgfRBM3VwDdrbVRERAx8vQ2Y2TQ3Gzu1vH5/afvrGhsVEREDX28D5tXlxmFdfi7plhobFBERg0NvB/lvKnejBEDSAcAv6mxSREQMBr3dgzkAOErS/eX1bsBiSYtobl752ipbFxERA1ZvAya3G46IiA3S24td3ld7QyIiYnDp7RhMRETEBknAREREFQmYiIioIgETERFVJGAiIqKKBExERFSRgImIiCoSMBERUUUCJiIiqkjAREREFQmYiIioIgETERFVJGAiIqKKBExERFSRgImIiCqqBYykXSX9XNJiSbdL+lRpHyFpnqQl5Xl4S5/jJC2VdJekSS3t+0taVOadKkmlfQtJ55X26ySNbekztdRYImlqrfcZERHt1dyDWQN8xvZrgAOBYyXtBcwArrA9DriivKbMmwLsTXMHzdMkDSnrOh2YDowrj647bE4DVtveAzgFOLmsawRwPM2tnicAx7cGWURE1FctYGw/aPvGMv0EsBgYDRwGzC6LzQYOL9OHAefaftr2PcBSYIKkUcC2tq+1beDsbn261nUBcEjZu5kEzLO9yvZqYB657XNERJ/qkzGYcujq9cB1wM62H4QmhICdymKjgWUt3ZaXttFlunv7Wn1srwEeA3boYV0REdFHqgeMpJcCPwQ+bfvxnhZt0+Ye2je2T+u2TZe0UNLClStX9rBpERGxoaoGjKTNacLlP2z/qDQ/VA57UZ4fLu3LgV1buo8BVpT2MW3a1+ojaSiwHbCqh3WtxfYZtsfbHj9y5MiNfZsREdFGzbPIBJwJLLb91ZZZc4Gus7qmAhe3tE8pZ4btTjOYf305jPaEpAPLOo/q1qdrXUcAV5ZxmkuBiZKGl8H9iaUtIiL6yNCK634T8DfAIkk3l7YvACcBcyRNA+4HjgSwfbukOcAdNGegHWv7mdLvGOAsYBhwSXlAE2DnSFpKs+cypaxrlaQTgQVluRNsr6r1RiMiYl3VAsb2f9N+LATgkPX0mQnMbNO+ENinTftTlIBqM28WMKu32xsREZ2Vb/JHREQVCZiIiKgiARMREVUkYCIioooETEREVJGAiYiIKhIwERFRRQImIiKqSMBEREQVCZiIiKgiARMREVUkYCIioooETEREVJGAiYiIKhIwERFRRQImIiKqSMBEREQVCZiIiKgiARMREVUkYCIioooETEREVJGAiYiIKhIwERFRRQImIiKqSMBEREQVCZiIiKgiARMREVUkYCIioooETEREVFEtYCTNkvSwpNta2kZImidpSXke3jLvOElLJd0laVJL+/6SFpV5p0pSad9C0nml/TpJY1v6TC01lkiaWus9RkTE+tXcgzkLmNytbQZwhe1xwBXlNZL2AqYAe5c+p0kaUvqcDkwHxpVH1zqnAatt7wGcApxc1jUCOB44AJgAHN8aZBER0TeqBYzt/wJWdWs+DJhdpmcDh7e0n2v7adv3AEuBCZJGAdvavta2gbO79ela1wXAIWXvZhIwz/Yq26uBeawbdBERUVlfj8HsbPtBgPK8U2kfDSxrWW55aRtdpru3r9XH9hrgMWCHHtYVERF9aFMZ5FebNvfQvrF91i4qTZe0UNLClStX9mpDIyKid/o6YB4qh70ozw+X9uXAri3LjQFWlPYxbdrX6iNpKLAdzSG59a1rHbbPsD3e9viRI0e+gLcVERHd9XXAzAW6zuqaClzc0j6lnBm2O81g/vXlMNoTkg4s4ytHdevTta4jgCvLOM2lwERJw8vg/sTSFhERfWhorRVL+gFwMLCjpOU0Z3adBMyRNA24HzgSwPbtkuYAdwBrgGNtP1NWdQzNGWnDgEvKA+BM4BxJS2n2XKaUda2SdCKwoCx3gu3uJxtERERl1QLG9vvXM+uQ9Sw/E5jZpn0hsE+b9qcoAdVm3ixgVq83NiIiOm5TGeSPiIhBJgETERFVJGAiIqKKBExERFSRgImIiCoSMBERUUUCJiIiqkjAREREFQmYiIioIgETERFVJGAiIqKKBExERFSRgImIiCoSMBERUUUCJiIiqkjAREREFQmYiIioIgETERFVJGAiIqKKBExERFSRgImIiCoSMBERUUUCJiIiqkjAREREFQmYiIioIgETERFVJGAiIqKKBExERFSRgImIiCoGdcBImizpLklLJc3o7+2JiHgxGbQBI2kI8A3gncBewPsl7dW/WxUR8eIxaAMGmAAstX237T8A5wKH9fM2RUS8aAzmgBkNLGt5vby0RUREH5Dt/t6GKiQdCUyy/ZHy+m+ACbY/2bLMdGB6eflq4K6NKLUj8JsXuLmpl3qpl3r9WeuF1Hu57ZHtZgx9YduzSVsO7NryegywonUB22cAZ7yQIpIW2h7/QtaReqmXeqnXn7Vq1RvMh8gWAOMk7S7pJcAUYG4/b1NExIvGoN2Dsb1G0ieAS4EhwCzbt/fzZkVEvGgM2oABsP0z4GeVy7ygQ2ypl3qpl3qbQK0q9QbtIH9ERPSvwTwGExER/SgBExERVSRgIiKiikE9yD9YSNqZ5ioEBlbYfij1Uq+/a/VHvRhYMsi/kfriB0vSvsA3ge2AB0rzGOBR4OO2b0y91OvrWv1Rr6XunjTXFPzTzx4w1/bi1NsEa9nOYwMewL7AL4HFwOXlcWdp26/DtW4GDmjTfiBwS4X3lnoDtN5gfm8t6/58qTsD+GB5zOhqS71Nr1bH/xMM9kcf/9JY0sO8pRXeW+oN0HqD+b21rPdXwOZt2l/S0/akXv/VyhjMhtva9nXdG23/UtLWHa51iaSfAmfz3JWhdwWOAv6zw7VSb2DXG8zvrcuzwC7Afd3aR5V5qbeJ1coYzAaSdCrwStr/YN1j+xMdrvdOnjtOKpqLeM51c5WCjku9gVtvML+3Um8y8HVgCc/97O0G7AF8wnZHg20w1+urWgmYjdDXP1gR0ZC0Gc3NBFt/9hbYfib1Nr1aCZgBStJ0N7cbSL3U22Rq9Ue92HTli5YdVG5g1mfl+rBW6g3seoP5vTUFpZ+k3qZXK3swHSTpY7a/1eF1TgBse4GkvYDJwJ19dThO0tm2j+qjWgfR7LLfZvuyCus/AFhs+3FJw2hOy9wPuAP4Z9uPdbje3wIX2l72vAu/8Fpd9zxaYftySR8A3khzOv0Ztv9YoeYrgf9FMwa5huZ4/g86/Tn2cltG2X4w9TatWgmYDpL0Idvf7eD6jgfeSXPFhXnAAcB84B3ApbZndqpWqdf9hmwC3gZcCWD7vR2ud73tCWX6o8CxwIXARODHtk/qcL3bgde5uVfQGcDvgQuAQ0r7X3a43mPA74BfAz8Azre9spM1Wmr9B83/k61ovuz4UuBHNO8N20d3uN7fAu8BrgLeRXP6/mqawPm47fmdrBcDVKfP5X4xP4D7O7y+RTQ3S9sKeBzYtrQPA26tsP03At8DDgbeWp4fLNNvrVDvppbpBcDIMr01sKhCvcWt77XbvJtrvD+aw9ATgTOBlTSn8E4FtulwrVvL81DgIWBIea1K/1cWtdTYCphfpndr/XftcM3tgJNovtj8SHksLm3b16jZw7ZcUmGd2wJfBs4BPtBt3mkdrvUy4HTgG8AOwBfLv+kcYFSn6mQMZgNJunU9j0XAzh0ut8b2M7Z/D/za9uMAtp+kznn444EbgL8HHnPzV+iTtq+yfVWFeptJGi5pB5q96ZUAtn9Hc8il026T9KEyfYuk8QCSXgV0/BASzaHNZ21fZnsazfcOTqM5zHl3h2ttVg6TbUPzC3+70r4FsHmHa3Xp+h7dFqUutu+vWG8OzV7SwbZ3sL0DzR72auD8TheTtN96HvvTXNGj075L8wfBD4Epkn4oaYsy78AO1zqL5tDwMuDnwJPAocDVNJcA6oh80XLD7QxMovlP3UrANR2u9QdJW5WA2f9PhaTtqBAwtp8FTpF0fnl+iLr/R7ajCTQBlvQy2/8j6aXUGSj+CPDvkv4B+A1wraRlND9kH6lQb6334GYcZC4wt4wBddKZNH/ZD6H5A+F8SXfT/GI6t8O1AL4DLJD0S+AtwMkAkkYCqyrUAxhr++TWBtv/A5ws6cMV6i2gOQTY7v/i9hXqvdL2X5XpiyT9PXClpI4emi52tv01AEkfb/lcvyZpWqeKJGA23E+Al9q+ufsMSfM7XOsttp+GP/3y77I5zWGWKmwvB46UdCjNobladcauZ9azNMfyO13vMeBoSdsAr6D5/7/c9a4A/L4etuXJThayfYqk88r0Ckln04zVfdv29Z2sVWr8u6TLgdcAX7V9Z2lfSRM4Ndwn6XPA7K5/s3LR2aN57suCnbQY+JjtJd1nlD9MOm0LSZt1/azbnilpOfBfNGNqndR69OrsbvOGdKpIBvkjYkCQNJzmzL/DgJ1K80M0e4Un2e5+VOGF1juCZizwrjbzDrd9UYfr/Qtwme3Lu7VPBr5me1wHa50A/Ivt33Zr34PmszyiI3USMBEx0HX6DM4Xc71O1krARMSAJ+l+27ul3qZVK2MwETEgSLp1fbPo/Bmcg7peX9VKwETEQNGXZ3AO9np9UisBExEDRV+ewTnY6/VJrYzBREREFfkmf0REVJGAiYiIKhIwERFRRQImojJJu0i6oL+3I6KvZZA/YgNJEs3PTo0rWvcbSUNt17iKdbxIZQ8mohckjZW0WNJpNPfN+UdJC8qtGv6pLHOypI+39PmipM+UvreVtiGSvtLS92Ol/bSuq+ZKulDSrDI9TdKX1rNNW0v6qaRbJN0m6X2l/Q2Srint10vaRtKWkr4raZGkmyS9rSx7tKTzJf0YuKysc1bZvpskHVbtQ41BL9+Diei9VwMfAi4CjqC5vbNoLr//FprL4v8bzT1fAP6a5t4vrX/ITaO5184byr0+fiHpMpor5r6Z5sKNo4FRZfmDWP/l9ifT3CL5UGhu41DuCXMe8D43t9neluZeH58CsP1nkvakCZNXlfX8OfBa26sk/TNwpe0PS9oeuF7S5eUePREbJHswEb13n+1f0tyhciLNHStvBPYExtm+CdipjLm8DlhdbsDVaiJwlKSbgeto7iY4juZGT2+WtBfNjaAekjSK5pf/+r5ZvQh4R9lzenO5HcGrgQdtLwCw/Xg57HUQzZ0SKZfWvw/oCph5trvu4TIRmFG2bz6wJc1dKiM2WPZgInqv6694AV+2/a02y1xAs3fzMtrveQj4pO1L15nRXI5+Ms3ezAiaPaDf2n6i3cbY/lW5u+K7gC+XPaGLgHYDqz3dwK1170TAX7W7RH3EhsoeTMSGuxT4cLnzJpJGS+q6P8m5wBSakGl35tilwDGSNi99XyVp6zLvWuDTNAFzNfDZ8tyWpF2A39v+HvCvwH40d7XcRdIbyjLbSBpa1vm/u2rS7JW0C5FLgU+WExmQ9Prn/zgi2sseTMQGsn2ZpNfQ3HIZ4LfAB4GHbd9e7pj5gO0H23T/DjAWuLH8El8JHF7mXQ1MtL1U0n00ezHrDRjgz4CvSHoW+CNwjO0/lMH+r6m5LfOTNHe2PA34pqRFwBrgaNtPl+1vdSLNONKtZfvuBd7d288molVOU46IiCpyiCwiIqrIIbKITZykHYAr2sw6xPYjfb09Eb2VQ2QREVFFDpFFREQVCZiIiKgiARMREVUkYCIioooETEREVPH/AfuYdVr3PL6LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#we plot the median House Price(price) vs Dicrete features variable \n",
    "for feature in discrete_feature:\n",
    "    data=df.copy()\n",
    "    data.groupby(feature)['price'].median().plot.bar()\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('price')\n",
    "    plt.title(feature)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observation 2c)\n",
    "We observe from above plots , a Linear Relationship between median price and Discrete Features except waterfront , view, condition  which have a lot of zero outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEWCAYAAABhffzLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de3gc5X0v8O9X8jrIhCI7mAQLCwMlpjXiKmLnuE0Ch8Y8hBCVSxMHN9eG3EhzVY4JbjBPnOJz3DbnJLRJSeOQFOID5qJASAo5gZTGxW5sbGNcoARjbGQSG4wN2ALL0u/8sbPyajXv7uxoZ3Yu38/z+PHq3dHOrEb67Tu/ed/fSzODiIjkR0uzD0BEROKlwC8ikjMK/CIiOaPALyKSMwr8IiI5o8AvIpIzCvySeiS3kjyvQa9lJH+/Ea8lklQK/JJbJH9J8i+afRwicVPgFwmJ5IRmH4NIGAr8khVnk/xPki+S/D7Jw0hOJvkTkru89p+QPBYASH4dwB8DuJ7kKySvL3ut80g+6X3P35Ok9z0fIrmK5DdI7gawmOSRJH/o7eMZkotItnjbt3hfP0Nyp7fdkd5zM7y00odJbvf29QmSZ5N8hOSe8mMi+fsk/5XkXpLPk7wlrh+sZI8Cv2TF5QDmATgRwJsBLELx9/v7AI4D0AlgAMD1AGBmVwP4NwBXmtnrzezKste6EMDZAE4D8Gfe65bMBrAFwNEAvg7gWwCOBHACgLcD+ACAD3vbfsj7d473/OtL+694vZMAvBfA/wZwNYDzAMwC8Gck3+5t9zUA9wGYDOBYb78ioSjwS1Zcb2bbzWw3igF5vpm9YGa3m9l+M3vZa3979ZcBACw1sz1mtg3AAwBOL3tuh5l9y8wOAjiAYsC+ysxeNrOtAP4WwJ97214O4O/MbIuZvQLgKgDvq0gRfc3MXjWz+wDsA7DCzHaaWT+KH0xneNsNovgBNs3b/lf1/oBEShT4JSu2lz1+BsA0kpNI/qOXankJwIMA2km21nit35Y93o9iT91vP0cBmOjtr3zfHd7jaT7PTQDwxrK235U9HvD5urTvLwMggP8guZnkR2q8BxEnBX7JiulljzsB7ADwRQAzAcw2s98D8DbveXr/hylNW/49z+NQT7x83/3e4x0+zx3E6OAebKdmvzWzj5nZNAAfB/APGnYqYSnwS1Z8muSxJKcA+AqAWwAcgWKveY/Xfk3F9/wOxdx7KGY2BOBWAF8neQTJ4wB8AcBN3iYrAHye5PEkXw/grwHc4qWJ6kLystKNaQAvovgBNBT22CXfFPglK36E4s3PLd6/JSjeLG1DsWe+GsC/VHzP/wFwqTei5psh9/sZFHPzWwD8yjuO5d5zywH8M4oppqcBvOptH8bZANaQfAXAXQA+a2ZPh3wtyTlqIRYRkXxRj19EJGcU+EVEckaBX0QkZxT4RURyJhVFpo466iibMWNGsw9DRCRV1q1b97yZTa1sjyzwk1yOYs2TnWZ2Sln7ZwBcieJElnvM7Mu1XmvGjBlYu3ZtVIcqIpJJJJ/xa48y1XMjgPMrDuIcAO8BcKqZzQLwNxHuX0REfEQW+M3sQQC7K5o/iWIBrNe8bXZGtX8REfEX983dNwP4Y5JrvNriZ7s2JHkFybUk1+7atSvGQxQRyba4A/8EFOuJzwHQC+DW0iIXlczsBjPrNrPuqVPH3JsQEZGQ4g78zwK4w4r+A8AwiqVtRUQkJnEP5+wDcC6AX5J8M4q1zJ+P+RhERBKvb30/lt37BHbsGcC09jb0zpuJnjM6an9jAFEO51wB4B0AjiL5LIolcZcDWE7yURRXL/qgqUqciMgofev7cdUdmzAwWKy83b9nAFfdsQkAGhL8Iwv8Zjbf8dSCqPYpIpIFy+59YiTolwwMDmHZvU80JPCrZIOISMLs2DNQV3u9UlGyQUTyJ8ocd9JNa29Dv0+Qn9be1pDXV+AXkcSJOsedROUfdO2TCii0EIPDh26BthVa0TtvZkP2pVSPiCROtRx3FpU+6Pr3DMAAvLh/ECDQ3lYAAXS0t+G6i7uSP6pHRCSsqHPcSeP3QTc4ZDj8dROw4Zp3Nnx/6vGLSOK4ctmNynEnTdwfdAr8IpI4vfNmoq3QOqqtkTnupIn7g06BX0QSp+eMDlx3cRc62tsiyXEnTdwfdMrxi0gi9ZzRkdlAX6n0PuMavqrALyKSAHF+0CnVIyKSM+rxi0juLerbhBVrtmPIDK0k5s+ejiU9Xc0+rMgo8ItIri3q24SbVm8b+XrIbOTrrAZ/BX4RyYSwtX1WrNnu2/6jNdvwwOO7MlkrSIFfRFJvPLV9hhxLggwbRgqlZa1WkAK/iKSeq7bPV+54BF+8dWPV3H0r6Qz+la/XqHr4zaZRPSKSeq7SBvsHh0eCeil3v6hv06ht5s+ePu79pI0Cv4ikXj2lDSpz+kt6urBgTidaSQDFK4C2gn9ozEqtIKV6RCT1eufNHJXjr8YvrbOkp2tUCqjyngGQrVpBCvwiknp+JQ927B2AX+q+1LOv9/WCjOpJy6phtAA3NZqtu7vb1q5d2+zDEEm1tASlRqkcn18y98Qp2PrCQMN/Dq6rhGYWlyO5zsy6K9uV4xfJgcoVnkrDE/vW9zf70CLjl7ufe+IUPLxtbyQ/hzStGqbAL5IDaQpKjbSkpwtPXXcBti59V/H/FwYi+zmkadUw5fhFciBNQSlK9fwc6k2NTWtvG5nwVdmeNOrxi+RA3pYydAn6cwiTGkvTqmEK/CI5kKagFKXeeTNRaBk9qqfQwjE/hzCpsTStGqZUj0gOxL3CU6JVjub0Gd0ZNjWWllXDFPhFciKpQSnOYabL7n0Cg0Ojh7APDtmYGjxpyteHoVSPiDRN3/p+9K7cOCqX3rtyY2TDTIP25LOeGlOPX0RG1Op9V3s+TM998V2bMThc0QMfNiy+a3Mkvf7DCi0YGBz2bS+X9dSYAr+IAPB637dtHEmF9O8ZQO9tGwEUA2G1mvcAQtXD3zMw6GyPIgX02sGxQb9ae1Yp1SMiAIBr797sm/++9u7NAKqPdIliglgUM42HHRVqKtuzPtNZPX4RAQC8uN+/911qDzPSpfScq/c+eVLBd78thPODxNXrD3KF4Fp0pbJwW7UPsiyke9TjF5FA2icVnO3VJkZV6z1f8+5ZKLRWjKtvpbNn7jfSBgjeQ3ctulLZnvWZzpEFfpLLSe4k+WhZ22KS/SQ3eP8uiGr/IkH0re/H3KX34/iF92Du0vszcykfRnubI7B77a5CvmbAOSdP9X3unJOn1uw9L7v0tFGTnpZdepqzdHIr6XvOgqaa/Aq3LZjTOWY5RtcHWQuZid+VyMoyk3wbgFcA/NDMTvHaFgN4xcz+pp7XUllmiUISy+g2k6uMcSkwHr/wHvhFC8I97r3D0V6ydem7fNtnLLzH+T1thdYx58y1AAsBPO3YRzV+vxt+x5H035XYyzKb2YMAdkf1+iLjlbeKlbWubu582L8HW2qvluqplhpxLXtSavc7rsmOfbly/661VQ4rtIS6oqssv+B3BZLm35Vm3Ny9kuQHAKwF8EUze9FvI5JXALgCADo7O2M8PMmLpOdxGzmcsTRRqjRmvjRRCjg03HLfAf/eban9NUfv97XBoaozXV09fsPYnvWhYaD+mQhX7t+VuBgYHB7Zf9AhpiXlM52Pd1yBJOV3pV5x39z9NoATAZwO4DkAf+va0MxuMLNuM+ueOtU/fygyHkmuWNno4YTVJkoFtd9n4lOpvXfeTFTUPkMLUXOmq+uqy2+SVSMMDA7h2rs3130VkOTflTBiDfxm9jszGzKzYQDfBfCWOPcvUi7J0/IbnYaqNlGqEdY+s3tMb3zYiu2TCv5hZlKhpWr+3097W8F3FFDtVXQPeXH/YN0fqL3zZvruNwm/K2HEGvhJHlP25Z8CeNS1rUjUklxG1xUQ6w2UjeTKo5PAijXbfZ9ztYc1a9oRY7NA5koMBRP4A9Vnv2kVWY6f5AoA7wBwFMlnAVwD4B0kT0fxR7YVwMej2r9IEEmtWEn6561dwbeWwye2+ubwD5/Y6rO1v2rDOYccUXDIDPsH/Z9zpY6qWb3lxTETsAaHzTkxiwgWn2vl6pfd+4RvqiytE7oiC/xmNt+n+XtR7U8kS6oF2UpBbgIXWlsAjA38xfZgXEMzO9rb8Nu9rzpnxPq1h+V6rSEz32Gel5zVgQce3zXys9n32kHf9FatXH3SBwLUSzN3RVIs6E3gvY5cvqvdT7U8d9AZsZUmttZ3CeO64mlvK/im7Zb0dGHVwnPx9NJ3YdXCc7H4olmh7utk7eauavWIJFB7W8G3Z1o5uzZoTZkjHa93pGO2rstQRRG30tdLerrw9K5XsOqpQ1N35p44BUt6unwnhZW0thAYGtuLL7QAIEcVjSvdxD3gs/3g0HCgtF3Ycsu982b6TvbTzV0RaZjFF80a88fZ4rWXC5qCcM1ArTYzdcwx3bUZlVn5Ya+9b30/Ht62d9RzD2/bW3O0jGvY5uAwfG+m+gV9wD0HoVGSPBAgDPX4RRKKLRw1Y4mVA+URfInARtShrzYkNIpZ0H43U6vxu9cBjO7dn3PyVNy+rr/udQNKz6c10FdSj18kJvUUhLv27s0Yqgh0Q8OHauOXJGUuQrXhpycdfbjvc672sCrvdfSu3Ije20Yv63jz6m25KtPhosAvEoO+9f34wq0bRgWhL9y6wRn8a9XGL0lDCuLT55xUV3tYlQF9cNjGLCzjumZI6+icsJTqEYnBV+54xHdm61fueGTcQTrpKQhXWYh6ykVELa2jc8JS4BeJQbU6N3FYMKfTWXI5alGXi6hX5aSu8aTGolgXOI7XVqpHJIS0LeDSfdyUutrr5QokcQaYynsdhRaOmXfQVmjF5XM6G5Iai3Jd3qjX/FXgF6lT0hbiDvIh1Ltyg+/3utrr9X7HlcP753TCUaPN2R5W5b2OZZedNmZ1r+su7gr8YVfr5xrleg5RrxWhVI/kWpjL6SQtxN23vh+9t20cuYnZv2cAvbeNrrMPeOPifTQq03TPI88526Ped4nrXkd5m7v+f/3bRVnGIeoSEerxS26F7bknqW7LtXdvHjNyZXBo7LDPqAUdhdRsQXvSQbaLsoxD1CUiFPglt8JeTof5o/SZe1W1Pai0BFyXeqqDVtMRMCAG/dAOsl2Ucyiinp+hwC+5FbbnHuaP0jXptMZk1Mz70zP9U2NvPGKib/tJRx8+roAY9EM7yHZRzqGIen6GcvySW+2TCr49Y9ei4iVhCn1VK2mcZw88vsu3fefLB3zbd+x5Fddd3BV6mKOr2NqMN7ThxKt+iiEr1vafc8Jk7N53oGZRtijnUET52gr8klv11LyvVO8f5Yw3+Af+GW/Id+CvthC7n30Hhpw/+yC1enrnzRzzwTHjDW2jqooOmWHVU7sx98Qp2PrCQCTj6JtNgV9yqxE16oN6aMvuutobzbUS1ThvMSSG3yic3pUbAWLUiKer7tiE6y4u1ugvOfGqn/q+5uotL+Kp6y6I/uCbQDl+ya04F9dodo7ftZus3GLwu1HvV6vH7+Z9tVW9skqBX3JrPCMn0jZzd7LjvoWrPW3qWYS+8uZ9q2NZL1d7FijVI7kVdjWmvvX9+PwtG0Z6y/17BvD5WzaMes2kedWx4IqrvZGqLfT+6uCQ3wJcVfnl8iuWLqiq8opu/uzpvnWMai0bmWbq8YvU6cu3bfRbHApf9mbMJpFrpStXe0P37Vgda+DAUKhU0xdXjq6x/8WVGwMH/dIaweWW9HRhwZzOkR5+K4kFczqxpKcrxNGlg3r8kltBp+9Xci3/52rPO9dHyzAQ6iaD3wI1gTk2XdLTlelAX0mBX3IrSTV3JB6Dw4bFd22OrNxxWijwS24lqeaOxGfPwODIWgD1rLmbJcrxS265ZujWmrkb1xqyEo88rrmrHr+k1nhXKAo7c3f/AcdqWo52ST6/q7woV8BqNvX4JZX61vf7ju6oZzx92CUBlSJqLr8ROK6aR+1thVGFzlzVQCuv8pK22E6jqccvqXT1nZt8R3dcfeemyHtl0xwF1/K2YHez+I3A6Vvfj96VGzFY9jtRaCEuPO2YUYXghh2Xc5XNWb/xr8AvsWnkpbPfhKBq7Y00aaL/hbKrXWJSMdF2yAy3/Hr7qFo9LpX1mbJ+VafAL7EIO2Y+iZ7cua+udmmsRX2bsGLN9pESyvNnT8cDj+8aU5dn2IDhgHMr/OrxZ/mqTl0UiUXUi0dLfty0ettIAbUhM9y0eltdtXoq+dVninoFrGZTj18A+PeiGjmTsdGXzoUW/8W6C+rKSA2HT2xF+6SJVVOOYes4pYUCv2BR36ZRRapKvSgADQv+jb50dpWYiaH0jCRUW6F1zFWln0Jry6h6/C5RroDVbOofCVas2V5XexilCorlWojMXDpLc7WSuOSsjlHDPF32DAymqqR2FCIL/CSXk9xJ8lGf575E0kgeFdX+Jbg4FqJY+8zuMRUUh63YHobrzzq7FdSlmjknTMbt6/pH5f6ryer4/KCi7PHfCOD8ykaS0wH8CYCxBbClKeJYiKLRVxVZX1EqLyaFuCnjN4Fr6wsDgdI8fsYzyCBtC/KURJbjN7MHSc7weeobAL4M4MdR7VvqE8dCFHlc3k5qY4jOhd8EruMX3jOu4wgzyCDNQ5RjzfGTvAhAv5nVXLGC5BUk15Jcu2vXrlqbyzjEsRBFHpe3k9oaNeFuvOPrw3x/mocoxzaqh+QkAFcDeGeQ7c3sBgA3AEB3d7e6hRGLeiGKPC5vJ403sdW/o9A7b+ao3jdQvN/jFzgq28OOz0/z7N44h3OeCOB4ABu9y7tjATxM8i1m9tsYj0OaoPShEuVcAcm+wSGrWvqjvN01qctQLNg23vH5aZ7dG1vgN7NNAI4ufU1yK4BuM3s+rmMQt8u/+xBWPXVohM3cE6fg5o+9taH7yNvydtJ4bYUWZ169UntbwbfSakd7W6Bx/LX4XWWkZXZvlMM5VwB4CMBMks+S/GhU+5LxqQz6ALDqqd24/LsPNemIRPztHxz2zasvvmvzmDLKL7920Pc1zjl5akOOpeeMDlx3cdeoss/XXdyV+Bu7QLSjeubXeH5GVPuW+lQG/VrtIknj17N3LcJeXqZ5vNI6u1czd0UkV9Jw8zVqCvwiknr1TARLw83XqKlIm4ik3usKrTBwVP6/0ErAMGpVrrZCK845eSrmLr0/k1U3gwr8MUnyOJLneY/bSB4R3WGJiAS3Z//gmButyy49DcsuO21U2yVndeD2df25r9UTqMdP8mMArgAwBcXx+McC+A6A/x7doYmIBDOtvc15o7W8be7S+wOvpdvIpUKTJmiP/9MA5gJ4CQDM7EmUjckXEWmmoEM0g862LdXhyeqVQdDA/5qZHSh9QXICVAhRRBIi6BBN143dyvY01+EJImjg/1eSXwHQRvJPAKwEcHd0hyUiElzQIZpB19JNcx2eIIIG/oUAdgHYBODjAH4KYFFUByUiUo+gQzSDzrYNemWQVkGHc7YBWG5m3wUAkq1e2/6oDkxExE/l2rr11scJMts2bB2etNwQDhr4fwHgPACveF+3AbgPwH+L4qDyKi2/NCLNdN3FXb5/J438+/Gr9lnr9dK0MEvQwH+YmZWCPszsFa++vjRImn5pRJrJr8cexd9PvXV4qt0QTtrfcNAc/z6SZ5a+IHkWgGzc5UiIrI8iEGmEFseCbUn4+0nTDeGgPf7PAVhJcof39TEA3hvNIeVTmn5pRJrFUXDTueiKqz0KaVqYJVCP38x+DeBkAJ8E8CkAf2Bm66I8sLzJ+igCET/1rrjsWqI5CWs6Bx0qmgRVAz/Jc73/LwbwbgBvBnASgHd7bdIgrpmHjVo0QiSJ6p0Fao5vGHI84WqPQpoWZqmV6nk7gPtRDPqVDMAdDT+inHLNPGzkohEiWdXhSLN0xHzFnJaFWaoGfjO7hmQLgJ+Z2a0xHVMuKccvaUb698Zd7SWudXHrleb1b5uhZo7fzIYBXBnDseSacvySZpfP7qyrvWTxRbPGBKEwq0O50ixAsSLn8Qvvwdyl92emyNp4Bf0Z/5zkl0hOJzml9C/SI8uZNN0YEqnUfdyUMTdq6bXX0trKql8H1XNGB1YtPBdPL30XVi08FwAyXWFzPIIO5/wIijn9T1W0n9DYw8mvMDMFRZLi6js3jblRa177pEIL9g8Oj/meSYUWLLv3CQwOjf7Oyq/DStOEqrgFDfx/iGLQ/yMUz+e/obgQizRQWm4MiVTad2DI2X74xFbf50hGeg9L983cgqZ6fgDgDwB8E8C3vMc/iOqgRCQ7qn0ouO5huZI9rg8RP7pv5hY08M80s78wswe8f1cAUPJZRMbFNU/FlexxfYj40X0zt6CpnvUk55jZagAgORvAqugOK59UnVPy5icbn4vstXXfzC1o4J8N4AMkt3lfdwJ4jOQmAGZmp0ZydDnSt74fvSs3YtArRtK/ZwC9KzcCyE51Tn2wSaVGjOGvRvfN/AUN/OdHehSCxXdtHgn6JYPDhsV3bc7EL27f+n703rZxZMRG/54B9N6WrQ828fd7r2vFS6+NTdG42pshb52SQIHfzJ6J+kDyztXzibpHFJdr797sO2zv2ruz8cEmbvsOjB3KWWqf2EocaNDwzbDyuBZGmElyInV7cb//B5irXbKjWgG1yqvcZkhCLf+4BU31iIiEEraOT73CpmvyON5fPX6Jhasseozl0qVJ2ib4h5m2CS0Nq5dfuodUXp6h97aNgcoz5HG8v3r8EgtXzy7GcunSJAM+5RpK7ZfP6cRNq7f5Pu+yqG8TVqzZjiEztJKYP3s67nnkudD3kPJY2VM9/oRYMMe/iqGrPW1cddHjrpcu8avWo17S04UFczpHev6tZM3f+ZtWbxu5bzBkhptWbxvXPaQ0LaDSKOrxJ8SSnmIJ2cqeTKk97fLYq5Ki3nkz8blbNvi2A8Xf/crf83qvAsarGeP9mzmENLLAT3I5gAsB7DSzU7y2rwF4D4BhADsBfMjMdrhfJV/8/gCyQrMo8+vvH3jS2e46/41aoKW9rTDu14hCs4eQRtnjvxHA9QB+WNa2zMz+CgBI/iWArwL4RITHkCpZn0SiWZT59OTOfXW1A8UFWspnsgNAoYUYMoPfCFASmECO2X7xRbPCH3iEml0yOrIcv5k9CGB3RdtLZV8ejvrXWs6sUg9Ai0ZIGk10LJ7iaq+l54wOLLvstFF592WXnYb3V1npy2/7pHY0mj2ENPYcP8mvA/gAgL0Azqmy3RUArgCAzs5s3OCsptk9AJHxcE3EGs8ELb8rxNLXrnthaflbmeZYHD6uIaSxB34zuxrA1SSvQnEt32sc290A4AYA6O7uzvyVgd8vQbV2kSSJc7huFu6FNXuwQzNH9fwIwD1wBH7Jnqzfw8izVtK3NEMriddNoHPpxbxq9mCHWAM/yZPMrHSL/yIAj8e5f2meZo9ikGjNOWEyVj2127f9333aAffErrxo5mCHyD5ySa4A8BCAmSSfJflRAEtJPkryEQDvBPDZqPYvyZLHQlh5snnHy8729kn+Qypd7RK9yHr8Zjbfp/l7Ue1Pkq3ZoxgkWtXKirvG0qtcR/PkN8kmscpjISwp2uv4UHC1S/QU+CUWrkW1Xe2SLodPbHW2h/3Q71vfj7lL78fxC+/B3KX3a05LAynwSyweeHxXXe2SLoVW/1BSaG1B77yZaCuM/mCoNXQxzIRGfVAEp8AvsVCOP9uq5fjDVL+sdzCAZr7XR9U5JRbNnqko0ao2jh+of+hivR0FzXyvj3r8EoveeTNRaBldt6XQQpVlzohq6+qGUe99AV1R1iezPX7NEk2gynpdWnYxMzocV3SlhXb8Vs2qVnah3pIGuqKsTyZ7/Mr3Jc+ye5/wXRpPE7iyodoN3EV9m3xXzVrUt8n5ej1ndOCSszpGrcx1yVnudFGYG8h5lsnAr1miyaNL8WyrdgN3xZrtvt/jageKnbfb1/WP+rC4fV2/s/Pm2j8AjfTxkclUj4JM8uhSPPtcN3DD5P/D3Kyt3L/qQ7llssevWaLJo0vx9JvsqK3jai8ppWuCtgONKVOuK3+3TAZ+BZnkCTOWW5Ll1YogWqu9ZP7s6XW1A+E+LCrpyt8tk4G/3htDIlKbq4xyrfLKS3q6sGBO56i/xwVzOquO6mnE8FBd+btlMvDXe2NIoqeRVsnn6ks3YtRt93FT8KYjDwMBvOnIw9B93JSq23c4grOr3Y+u/N0yGfiV20senZPkc/WlS+3VCrFVE+ZDvxFBW+lFN43qkVjonCRfrUlYxUJsY/P5pQJtrkmTYUfoAONfmrCZq1wlWSYD/5FtBd+iUUc6FoSQ6Gk4Z/LVmi1bra5+taGTYT/0FbSjk8lUj+vGfx0DAmI3ocX/4FztaaN8a/LVSo0c5lgc/bBCS9VevW6yJk8me/x79jtKxDrak+DgsH+G1dWeNo26dJdoVetlv3bQf/TOaweHq/bqv/He0+uquyPRy2TgV1ohmXTpnm6uPsiwue8PTGtv04d+AmUy8Ndb2U9EaqtWc7/W35w+9JMlkzl+DeMSabxqM3D1N5cumezxA+phiDTakp4urNnyAp7cuW+k7aSjDx+Zgau/ufTIZI9fRBpvUd+mUUEfAJ7cua9qXX1JJgV+kRSbe6J/6QNX+3iEqasvyaTAL5Ji//ncy4Ha2x2TF13tfhq9rq40jwK/SIq96JibUtl+4WnH+G7navfTiFLJkgwK/CI58MDju+pq9xOmrr4kU2ZH9YjIIY0oklcavbNizXYMmaGVxPzZ06vW1ZdkUuAXyYFGzWZf0tOlQJ8BSvWIpNgbj5gYqF1F8qScevwiKTah1X8RlMr2oPVyXDX1JVsU+BOiWh0UEZd6cve1ZtZWq6mv4J8tSvUkxAlTJ9XVLgI0dkFxLY+ZH5EFfpLLSe4k+WhZ2zKSj5N8hOSdJNuj2n/abNm1v652EaCxuXstj5kfUfb4bwRwfkXbzwGcYmanAvgvAFdFuP9U0axICaORVTG1UlZ+RJbjN7MHSc6oaLuv7MvVAC6Nav9poxy/hNWoqphaxyI/mnlz9yMAbnE9SfIKAFcAQGdnZ1zH1DRzTvW41CsAAAkDSURBVJiMVU/t9m0XaYRaI3a0UlZ+NCXwk7wawEEAN7u2MbMbANwAAN3d3ZnPd2ze4V9sy9UuUo+gI3ZUUz8fYh/VQ/KDAC4EcLmZEtglewYcC8Q72iVdXBm7uDJ5GrEj5WIN/CTPB/A/AFxkZhquIrlx+Wz/dKWrvdE0YkfKRTmccwWAhwDMJPksyY8CuB7AEQB+TnIDye9EtX+RJFnS04UFczpHbta3klgwp9NZ96YR9fPLacSOlItyVM98n+bvRbU/kaSrp8DZ4otmoXflRgwOH8qGFlqIxRfNCrVvjdiRcirZILlVaAEGh/3bq5k8qeC7AMrkSeF6434aPcJGI3aknAJ/QkwqtGC/TxSaVCsKSWgHfYJ+tfaSd516DG5avc23vZEaPcJGI3akRFElISZO8K+y6GqX8Qub927EalYizaTAnxB7HcM2Xe0yfmHr3GiEjKSdAn9CaNRFfRqx8HfPGR245KyOUSNtLjmrdjpE50rSToE/IbRCUn0asfB33/p+3L6uf6RG0pAZbl/Xj771/VW/T+dK0k6BPyHC9j7zqt5x8X7CzmbVuZK006iehHD1PruPm6KA4jDehb/9Fh+v1l6icyVppx5/QqiWSvzC3ifQuZK0y2yPP22LRmukSP3Ge47DLn6jcyVpl8kef6kEbf+eARgOlaCtddOumTRSpD6NOMcdjp+tq71E50rSLpOBP42X4hopUp9GnOOwP3OdK0m7TAb+NF6Ka6RIfRpxjsOuV9vIdW5FmiGTOf5p7W2+IzOSfCmukSL1adQ5Dlu/RnVvJM0y2eNP46V4GtNTzZTGcyySFJns8aexBG0a01PNlMZzLJIUmQz8QPouxdOYnmq2tJ1jkaTIZKonjZS6EJG4ZLbHnzZKXYhIXBT4E0SpCxGJg1I9IiI5o8AvIpIzSvUkSNoKy4lIOinwJ0Sp6FhpElep6BgABX8RaSilehJCM3dFJC4K/AmhmbsiEhcF/oRQjXcRiYsCf0I0c+Zu2AVJRCSdFPgTopk13lUuQiRfNKonQZo1c1flIkTyRYFfAKhchEieKNUjIpIzCvwiIjmjwC8ikjMK/CIiOaPALyKSMzSzZh9DTSR3AXim2cdR5igAzzf7ICKi95ZOem/pFPV7O87MplY2piLwJw3JtWbW3ezjiILeWzrpvaVTs96bUj0iIjmjwC8ikjMK/OHc0OwDiJDeWzrpvaVTU96bcvwiIjmjHr+ISM4o8IuI5IwCfwAkp5D8Ocknvf8nO7bbSnITyQ0k18Z9nPUgeT7JJ0j+huRCn+dJ8pve84+QPLMZxxlGgPf2DpJ7vfO0geRXm3Gc9SK5nOROko86nk/zOav13lJ5zgCA5HSSD5B8jORmkp/12Sbec2dm+lfjH4D/BWCh93ghgP/p2G4rgKOafbwB3k8rgKcAnABgIoCNAP6wYpsLAPwMAAHMAbCm2cfdwPf2DgA/afaxhnhvbwNwJoBHHc+n8pwFfG+pPGfesR8D4Ezv8REA/qvZf2/q8QfzHgA/8B7/AEBPE4+lEd4C4DdmtsXMDgD4vyi+x3LvAfBDK1oNoJ3kMXEfaAhB3lsqmdmDAHZX2SSt5yzIe0stM3vOzB72Hr8M4DEAlYtfxHruFPiDeaOZPQcUTyKAox3bGYD7SK4jeUVsR1e/DgDby75+FmN/EYNsk0RBj/utJDeS/BnJWfEcWuTSes6CSv05IzkDwBkA1lQ8Feu50wpcHpL/D8CbfJ66uo6XmWtmO0geDeDnJB/3ejJJQ5+2ynG9QbZJoiDH/TCKNUxeIXkBgD4AJ0V+ZNFL6zkLIvXnjOTrAdwO4HNm9lLl0z7fEtm5U4/fY2bnmdkpPv9+DOB3pcsu7/+djtfY4f2/E8CdKKYdkuhZANPLvj4WwI4Q2yRRzeM2s5fM7BXv8U8BFEgeFd8hRiat56ymtJ8zkgUUg/7NZnaHzyaxnjsF/mDuAvBB7/EHAfy4cgOSh5M8ovQYwDsB+I5QSIBfAziJ5PEkJwJ4H4rvsdxdAD7gjTaYA2BvKd2VcDXfG8k3kaT3+C0o/h28EPuRNl5az1lNaT5n3nF/D8BjZvZ3js1iPXdK9QSzFMCtJD8KYBuAywCA5DQA/2RmFwB4I4A7vd/NCQB+ZGb/0qTjrcrMDpK8EsC9KI6CWW5mm0l+wnv+OwB+iuJIg98A2A/gw8063noEfG+XAvgkyYMABgC8z7yhFUlGcgWKo1uOIvksgGsAFIB0nzMg0HtL5TnzzAXw5wA2kdzgtX0FQCfQnHOnkg0iIjmjVI+ISM4o8IuI5IwCv4hIzijwi4jkjAK/iEjOKPBLZpGc4ar26Nj+Q94Q3dLXW9M0SUgkKAV+kUM+BGBarY3KkdRcGEkdBX7Jugkkf+DVOL+N5CSSXyX5a5KPkrzBmy15KYBuADd79d7bvO//DMmHWVxn4WQAILnY+777APyQ5HEkf+Ht4xckO73tXO03kvy2V6N9C8m3s1iP/jGSN3rbtHrbPert+/Ox/+QksxT4JetmArjBzE4F8BKATwG43szONrNTALQBuNDMbgOwFsDlZna6mQ143/+8mZ0J4NsAvlT2umcBeI+ZvR/A9SiW1D0VwM0Avult42oHgMkAzgXweQB3A/gGgFkAukieDuB0AB1evaguAN9v4M9Eck6BX7Juu5mt8h7fBOCPAJxDcg3JTSgG32olfksFtdYBmFHWflfZh8NbAfzIe/zP3j6qtQPA3V7JgU0Afmdmm8xsGMBmbz9bAJxA8lskz0fxQ0ukIRT4Jesqa5IYgH8AcKnXk/4ugMOqfP9r3v9DGF3bal8d+/RrL73ucNnj0tcTzOxFAKcB+CWATwP4pyr7E6mLAr9kXSfJt3qP5wP4lff4ea8++qVl276M4tJ49fp3FKuAAsDlZftwtdfkjSZqMbPbAfwVissSijSERiRI1j0G4IMk/xHAkyjm6iejmGLZimIZ55IbAXyH5ACKaZqg/hLAcpK9AHbhUGVFV3sQHQC+T7LUObuqju8VqUrVOUVEckapHhGRnFHgFxHJGQV+EZGcUeAXEckZBX4RkZxR4BcRyRkFfhGRnPn/ELQPjqh88esAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#we plot the Scatter Plot of House Price(price) vs Continuous Features (except Longitude)\n",
    "continuous_feature1 = continuous_feature\n",
    "#remove price from continuous feature\n",
    "continuous_feature1.remove('price')\n",
    "#remove longitude from continuous feature as it has negative value and throwing an error\n",
    "continuous_feature1.remove('longitude')\n",
    "\n",
    "for feature in continuous_feature1:\n",
    "    data=df.copy()\n",
    "    data[feature]=np.log(data[feature])\n",
    "    data['price']=np.log(data['price'])\n",
    "    plt.scatter(data[feature],data['price'])\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('price')\n",
    "    plt.title(feature)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#taking the scatter plot of 'price' vs 'longitude'\n",
    "data=df.copy()\n",
    "data['longitude']=np.log(data[feature].abs())\n",
    "data['price']=np.log(data['price'])\n",
    "plt.scatter(data['longitude'],data['price'])\n",
    "plt.xlabel('longitude')\n",
    "plt.ylabel('price')\n",
    "plt.title('longitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEJCAYAAAB2T0usAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAZ9klEQVR4nO3df5hV1X3v8feHGZgR/BWjEn8gaGLNoE+idipKSQqXoDERTRPzRCL1RylITIcaYqpxUmuTThpyo61oEpRLo6lmmmiiV22M8NCZW4nRBBARmaQaFUEp/mgUUJgww7d/7D3cw2GGORvOjznweT3Pfs7ea6+9z/fAzHzPWmvvtRURmJnZ/m1QpQMwM7PKczIwMzMnAzMzczIwMzOcDMzMDCcDMzPDycDMzHAysCog6UVJH6l0HAORpOMkbZZUU+lYrLo5GZhViKTxktbtzTki4qWIODAiuosVl+2fnAzMzMzJwKrGH0laLel3kr4nqV7SuyQ9JOm1tPwhScf2HCDpMknPS9ok6QVJF+fs+3NJHelxj0gambMvJF0p6dn02K9Jeq+kX0jaKOlHkobk1D9P0gpJb0p6TNIHcva9KOlqSSslvSXph2nsw4CHgaPTbp7Nko7u68NLOkPS0vT9N0i6KS0flcZbK+msnHNtlrRV0otpvUGSrpX0W0lvpJ/hsCL939i+ICK8eBnQC/AisAoYARwG/Bz4e+DdwKeAocBBwD3A/ekxw4CNwEnp9lHAyen6J4DngAagFvgK8FjO+wXwAHAwcDLQCSwGTgAOAVYDl6Z1TwdeBcYANcClabx1ObH/Ejg6jb0DmJnuGw+sK/Df4BfAn6XrBwJnpuuj0nhr8+oPBtqBf0i3rwIeB44F6oDbgNZK/996GTiLWwZWLW6NiLUR8d9ACzAlIt6IiB9HxDsRsSkt/5OcY7YDp0g6ICLWR8QzafkVJH8kOyKiC/g6cGpu6wCYExEb02NWAQsj4vmIeIvkG/1pab3pwG0R8UREdEfEnSTJ48ycc82NiFfS2B8ETt2Dz78NeJ+kwyNic0Q83k/9ucDbQHPOZ26OiHUR0QncAFwoqXYPYrF9kJOBVYu1OetrSLpXhkq6TdIaSRuB/wAOlVQTEW8DnwFmAusl/Zuk96fHjwRuTrt13gT+GxBwTM57bMhZ39LL9oE55/piz7nS840gaQn0+K+c9Xdyjs1iGvAHwK8l/UrSeX1VlHQFSavjsxGxPSfO+3Ji7AC6geF7EIvtg5wMrFqMyFk/DngF+CJwEjAmIg4GPpzuF0BEPBIRk0i6iH4NzE/3rwWuiIhDc5YDIuKxPYhrLdCSd66hEdFawLEFzx8fEc9GxBTgSGAOcG867rATSR8CvgZckLZicuM8Ny/O+oh4udAYbN/mZGDV4vOSjk0HPa8DfkgyTrAFeDMt/9ueypKGSzo//YPZCWwm+SYMMA/4sqST07qHSPr0HsY1H5gpaYwSwyR9XNJBBRy7AXi3pEP6qyhpqqQj0m/6b6bF3Xl1RpD8u1wSEf+Zd4p5QEtPV5ikIyRdUECMtp9wMrBq8QNgIfB8uvw98E/AAcDrJIOjP8upP4ik5fAKSTfQnwBXAkTEfSTfrv817V5aBZy7J0FFxFKScYNbgd+RDExfVuCxvwZagefT7ps+ryYCPgo8I2kzcDNwUURszaszEXgPSauh54qinnGSm0kGxRdK2kTy7zWmkDht/6AIP+nMzGx/55aBmZk5GZgNFJIezrtprGe5rtKx2b7P3URmZkZV3nBy+OGHx6hRoyodhlmv3n77bYYN2+WqT7OKW7Zs2esRcURv+6oyGYwaNYqlS5dWOgyzXrW3tzN+/PhKh2G2C0lr+trnMQMzM3MyMDMzJwMzM6MMyUDSFyQ9I2mVpFZJ9Xn7x6fzvK9Il+tLHZOZme2spAPIko4BZgGjI2KLpB8BFwF35FV9NCL6nIXRzMxKqxzdRLXAAem86UNJ5oox2+e0trZyyimnMHHiRE455RRaWwuZuNRsYChpyyAiXpb0LeAlktklF0bEwl6qniXpKZJEcXXOQ0jMqkJrayvNzc0sWLCA7u5uampqmDZtGgBTpkypcHRm/St1N9G7gAuA40mm3b1H0tSIuCun2nJgZERslvQx4H7gxF7ONQOYATB8+HDa29tLGbpZJtdddx2zZs1CElu3buXAAw+kqamJ6667jqOOOqrS4Zn1q6TTUaRzxH80Iqal25eQPLv1yt0c8yLQGBGv91WnsbExfNOZDSQ1NTVs3bqVwYMH77jpbNu2bdTX19Pd3d3/CczKQNKyiGjsbV+pxwxeAs5MH08okvnWO/KCe0+6D0lnpDG9UeK4zIqqoaGBJUuW7FS2ZMkSGhoaKhSRWTYlTQYR8QRwL0lX0NPp+90uaaakmWm1C4FV6ZjBXJKHdnj2PKsqzc3NTJs2jba2Nrq6umhra2PatGk0Nzf3f7DZAFCVs5a6m8gGotbWVlpaWujo6KChoYHm5mYPHtuAsrtuIicDsyLzRHU2UFVyzMDMzKqAk4GZmTkZmJmZk4GZmeFkYGZmOBmYmRlOBmZmhpOBWdF4CmurZiWdtdRsf+EprK3auWVgVgQtLS0sWLCACRMmUFtby4QJE1iwYAEtLS2VDs2sIE4GZkXQ0dHBuHHjdiobN24cHR0dfRxhNrA4GZgVgaewtmrnZGBWBJ7C2qqdB5DNiqBnkLipqWnHFNYtLS0ePLaq4SmszYrMU1jbQOUprM3MbLecDMzMzMnAzMycDMzMDCcDMzPDycDMzHAyMDMznAzMiqapqYn6+nomTJhAfX09TU1NlQ7JrGC+A9msCJqampg3bx5z5sxh9OjRrF69mmuuuQaAW265pcLRmfXPLQOzIpg/fz5z5sxh9uzZ1NfXM3v2bObMmcP8+fMrHZpZQZwMzIqgs7OTmTNn7lQ2c+ZMOjs7KxSRWTZOBmZFUFdXx7x583YqmzdvHnV1dRWKyCwbjxmYFcH06dN3jBGMHj2am266iWuuuWaX1oLZQOVZS82K5JxzzmHRokVEBJKYNGkSjzzySKXDMtvBs5aalVhrayvPPvssixcvZtGiRSxevJhnn32W1tbWSodmVhAnA7MiaGlpYcGCBUyYMIHa2lomTJjAggULaGlpqXRoZgVxMjArgo6ODsaNG7dT2bhx4+jo6KhQRGbZOBmYFUFDQwNLlizZqWzJkiU0NDRUKCKzbJwMzIqgubmZadOm0dbWRldXF21tbUybNo3m5uZKh2ZWEF9aalYEPQ++b2pqoqOjg4aGBlpaWnaUmw10vrTUrMja29sZP358pcMw20VFLy2V9AVJz0haJalVUn3efkmaK+k5SSslnV7qmMxKwbOWWjUraTeRpGOAWcDoiNgi6UfARcAdOdXOBU5MlzHAd9NXs6rhWUut2pVjALkWOEBSLTAUeCVv/wXA9yPxOHCopKPKEJdZ0XjWUqt2JW0ZRMTLkr4FvARsARZGxMK8ascAa3O216Vl63MrSZoBzAAYPnw47e3tpQrbLLPOzk5Gjx5Ne3s7mzdvpr29ndGjR9PZ2emfVasKpe4mehfJN//jgTeBeyRNjYi7cqv1cuguo9oRcTtwOyQDyB6gs4Gkrq6O1atXM3v27B0DyDfddBN1dXUeTLaqUOpLSz8CvBARrwFI+gkwFshNBuuAETnbx7JrV5LZgDZ9+nS+9KUv8c1vfpNXX32VI488ktdee40rr7yy0qGZFaTUYwYvAWdKGipJwEQg//78B4BL0quKzgTeioj1+ScyG8jGjh3LkCFD2LBhAxHBhg0bGDJkCGPHjq10aGYFKfl9BpL+DvgM0AU8CfwFcDlARMxLk8StwEeBd4DLI2K3NxH4PgMbaEaMGEF3dzd333033d3d1NTUcPHFF1NTU8PatWv7P4FZGezuPgPfdGZWBJJYuHAhkyZN2jFmsGjRIs4++2yq8XfM9k27SwaejsKsSK699lrOOeecHQ+3Oe200yodklnBPFGdWRHU1dWxfPlyJk+ezH333cfkyZNZvny5n4FsVcMtA7Mi6OzspL6+npUrV/LJT36SkSNHUl9fz9atWysdmllB3DIwK5KpU6eyfv16IoL169czderUSodkVjAPIJsVgSQGDRrE9u3bd5T1bFfj75jtmyo6a6nZ/iA/EQBs376dQYP8K2bVwT+pZkWQnwj6KzcbaJwMzIpEEjfeeCMPP/wwN954I8n9lGbVwVcTmRXRtddey7Zt2xg8eHClQzHLxC0DsyKJCGpra5FEbW2tB46tqrhlYFZEW7Zs2enVrFq4ZWBWJPljBB4zsGriloFZEdTWJr9KXV1dO8pqamoqFY5ZZm4ZmBVBXV0dXV1dO+4rGDRoEF1dXZ6byKqGk4FZEbz99tvA/7+voOe1p9xsoCs4GaRPIpsq6fp0+zhJZ5QuNLPq09Nd1PNqVi2ytAy+A5wFTEm3NwHfLnpEZlVs+vTpPPjgg0yfPr3SoZhlUvBEdZKWR8Tpkp6MiNPSsqci4oMljbAXnqjOBpqeiepqamp23HTW3d3tiepsQCnWk862SaoBIj3pEYAnXjFL5c5D1JMIzKpFlm6iucB9wJGSWoAlwNdLEpVZlcofQDarFgW3DCLibknLgImAgE9EREfJIjMzs7LJcjXRAqA+Ir4dEbdGRIekG0oXmpmZlUuWbqJzgDskXZJTdn6R4zEzswrIkgxeBT4MfFrStyXVknQXmZlZlcuSDBQRGyNiMvAa8P+AQ0oTlpmZlVOWZPBAz0pE3AD8A/BikeMxM7MKKDgZRMTf5m0/FBH/q/ghmZlZufV7aamkJRExTtIm0hvOenYBEREHlyw6MzMri36TQUSMS18PKn04ZmZWCVnuM3ivpLp0fbykWZIOLV1oZmZWLlkGkH8MdEt6H7AAOB74QUmiMjOzssqSDLZHRBfwp8A/RcQXgKNKE5aZmZVTlmSwTdIU4FLgobRscPFDMjOzcsuSDC4nebhNS0S8IOl44K7ShGVmZuWUZdbS1cCsnO0XgG/0bEv6cUR8qrjhmZlZOWRpGfTnhCKey8zMyqiYycDP9jMzq1LFTAa7kHSSpBU5y0ZJV+XVGS/prZw615cyJjMz21WWZyD3Z5fprCPiN8CpAOnzk18meXRmvkcj4rwixmJmZhlkuQP5r/opu6afU0wEfhsRawp9TzMzK48sLYNLgZvzyi7rKYuIhf0cfxHQ2se+syQ9BbwCXB0Rz+RXkDQDmAEwfPhw2tvbCw7crJL8s2rVQBG7H/dNbzT7LDAOeDRn10FAd0R8pN83kYaQ/KE/OSI25O07mOTu5s2SPgbcHBEn7u58jY2NsXTp0v7e1qxspL4f+tff75hZuUhaFhGNve0rpGXwGLAeOBy4Mad8E7CywBjOBZbnJwKAiNiYs/5TSd+RdHhEvF7guc3MbC8VMoX1GmANyd3He2oKfXQRSXoPsCEiQtIZJOMYb+zFe5mZWUYFjxlI+iQwBziS5Mqhgh5uI2koMAm4IqdsJsnB84ALgc9J6gK2ABeF29VmZmXV75jBjorSc8DkiOgobUj985iBDTQeM7BqsLsxgyw3nW0YCInAzMyKL8ulpUsl/RC4H+jsKYyInxQ9KjMzK6ssyeBg4B3g7JyyAJwMzMyqXJYprC8vZSBmZlY5Waaj+ANJiyWtSrc/IOkrpQvNzMzKJcsA8nzgy8A2gIhYSTLFhJmZVbksyWBoRPwyr6yrmMGYmVllZEkGr0t6L+lDbCRdSDJNhZmZVbksVxN9HrgdeL+kl4EXgKklicrMzMoqy9VEzwMfkTQMGBQRm0oXlpmZlVOWuYkOBS4BRgG1PbffR8SskkRmZmZlk6Wb6KfA48DTwPbShGNmZpWQJRnUR8TskkViZmYVk+Vqon+RNF3SUZIO61lKFpmZmZVNlpbB74H/DTSTXl6avp5Q7KDMzKy8siSD2cD7/DhKM7N9T5ZuomdIZi01M7N9TJaWQTewQlIbOz/PwJeWmplVuSzJ4P50MTOzfUyWO5DvlHQAcFxE/KaEMZmZWZlleZ7BZGAF8LN0+1RJD5QqMDMzK58sA8g3AGcAbwJExArg+BLEZGZmZZYlGXRFxFt5ZdFrTTMzqypZBpBXSfosUCPpRGAW8FhpwjIzs3LK0jJoAk4muay0FdgIXFWKoMzMrLyyXE30DslUFM2SaoBhEbG1ZJGZmVnZZLma6AeSDk4fbvMM8BtJXypdaGZmVi5ZuolGR8RG4BMkzzY4DvizkkRlZmZllSUZDJY0mCQZ/N+I2IavJjIz2ydkSQa3AS8Cw4D/kDSSZBDZzMyqXJYB5LnA3JyiNZImFD8kMzMrtyz3GSDp4ySXl9bnFH+1qBGZmVnZZbmaaB7wGZL7DQR8GhhZorjMzKyMsowZjI2IS4DfRcTfAWcBI0oTlpmZlVOWZLAlfX1H0tHANjxRnZnZPiHLmMFDkg4FvgksS8v+T/FDMjOzcsuSDL4FfA74EPAL4FHgu6UIyszMyitLN9GdJFcSzQVuARqA7+/uAEknSVqRs2yUdFVeHUmaK+k5SSslnZ71Q5iZ2d7J0jI4KSI+mLPdJump3R2QPh7zVIB0cruXgfvyqp0LnJguY0haG2MyxGVmZnspS8vgSUln9mxIGgP8PMPxE4HfRsSavPILgO9H4nHgUElHZTivmZntpX5bBpKeJpmDaDBwiaSX0u2RwOoM73URyXMQ8h0DrM3ZXpeWrc+LYwYwA2D48OG0t7dneGuzyvHPqlWDQrqJztvbN5E0BDgf+HJvu3sp22UCvIi4HbgdoLGxMcaPH7+3YZmVhX9WrRr0mwx66dbZE+cCyyNiQy/71rHzzWvHAq8U4T3NzKxAWcYM9sYUeu8iAniApPtJ6ZjEWxGxvo+6ZmZWApkmqtsTkoYCk4ArcspmAkTEPJIH5XwMeA54B7i81DGZmdnOSp4M0mcnvzuvbF7OegCfL3UcZmbWt3J1E5mZ2QDmZGBmZk4GZmbmZGBmZjgZmJkZTgZmZoaTgZmZ4WRgZmY4GZiZGU4GZmaGk4GZmeFkYGZmOBmYmRlOBmZmhpOBmZnhZGBmZjgZmJkZTgZmZoaTgZmZ4WRgZmY4GZiZGU4GZmaGk4GZmeFkYGZmOBmYmRlOBmZmhpOBmZnhZGBmZjgZmJkZTgZmZoaTgZmZ4WRgZmY4GZiZGU4GZmaGk4GZmeFkYGZmOBmYmRllSAaSDpV0r6RfS+qQdFbe/vGS3pK0Il2uL3VMZma2s9oyvMfNwM8i4kJJQ4ChvdR5NCLOK0MsZmbWi5ImA0kHAx8GLgOIiN8Dvy/le5qZWXalbhmcALwGfE/SB4FlwF9FxNt59c6S9BTwCnB1RDyTfyJJM4AZAMOHD6e9vb2kgZsVi39WrRooIkp3cqkReBz444h4QtLNwMaI+JucOgcD2yNis6SPATdHxIm7O29jY2MsXbq0ZHGbZSWpz32l/B0zy0LSsoho7G1fqQeQ1wHrIuKJdPte4PTcChGxMSI2p+s/BQZLOrzEcZmZWY6SJoOI+C9graST0qKJwOrcOpLeo/RrlaQz0pjeKGVcZma2s3JcTdQE3J1eSfQ8cLmkmQARMQ+4EPicpC5gC3BRuF1tZlZWJU8GEbECyO+jmpez/1bg1lLHYWZmffMdyGZm5mRgZmZOBmZmhpOBmZnhZGBmZjgZmJkZTgZmZoaTgZmZ4WRgZmY4GZiZGeWZm8hsQBh17b9lPmbNnL1/AN/uprfuMfKahzKf98VvfHxPwjHrlZOB7Tf26I/nNwqfM7G3P/qec9GqhbuJzIokIogI2tradqybVQsnAzMzczIwMzMnAzMzw8nAzMxwMjAzM5wMzMwMJwMzMwNUjddCS3oNWFPpOMz6cDjweqWDMOvFyIg4orcdVZkMzAYySUsjorHScZhl4W4iMzNzMjAzMycDs1K4vdIBmGXlMQMzM3PLwMzMnAzMzAwnAzMzw8nAqpykUZJWVTqOLNKYP7uHxz5W7HjMwMnArBJGAXuUDCJibHFDMUs4Gdi+oFbSnZJWSrpX0lBJ10v6laRVkm5X+oBiSbMkrU7r/mtaNkzSP6f1n5R0QVp+maT7JT0o6QVJfylpdlrncUmHpfXeK+lnkpZJelTS+9PyOyTNlfSYpOclXZjG+w3gQ5JWSPpCbx9I0smSfpnWWSnpxLR8c/r61XTfCkkvS/peWj4157jbJNWU7p/d9ik9z2r14qUaF5Jv2QH8cbr9z8DVwGE5df4FmJyuvwLUpeuHpq9fB6b2lAH/CQwDLgOeAw4CjgDeAmam9f4RuCpdXwycmK6PAf49Xb8DuIfkS9do4Lm0fDzwUD+f6xbg4nR9CHBAur45r94hwErgD4EG4EFgcLrvO8Allf4/8lIdS+1eZRKzgWFtRPw8Xb8LmAW8IOmvgaHAYcAzJH8oVwJ3S7ofuD895mzgfElXp9v1wHHpeltEbAI2SXorPQfA08AHJB0IjAXuSRsfAHU5sd0fEduB1ZKGZ/hMvwCaJR0L/CQins2vkLZ27gb+MSKWSfpLkqTwqzSWA4BXM7yn7cecDGxfkH/nZJB8K26MiLWSbiD5Aw/wceDDwPnA30g6GRDwqYj4Te5JJI0BOnOKtudsbyf5/RkEvBkRp/YRW+7x6qPOrh8o4geSnkjjfUTSX0TEv+dVuwFYFxHfyzn/nRHx5ULfx6yHxwxsX3CcpLPS9SnAknT99fSb+4UAkgYBIyKiDfhrki6hA4FHgKaccYXTCn3jiNhI0gr5dHqsJH2wn8M2kXQ99UnSCcDzETEXeAD4QN7+84BJJK2gHouBCyUdmdY5TNLIQj+L7d+cDGxf0AFcKmklSZfQd4H5JF059wO/SuvVAHdJehp4kqR75U3ga8BgYGV6merXMr7/xcA0SU+RdEdd0E/9lUCXpKf6GkAGPgOskrQCeD/w/bz9XwSOBnoGi78aEauBrwAL03+LRcBRGT+L7ac8N5GZmbllYGZmHkA2qyhJ5wBz8opfiIg/rUQ8tv9yN5GZmbmbyMzMnAzMzAwnAzMzw8nAzMyA/wE0CbtuthAX9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#we plot the Boxplot of all continuous features to  see which features have outliers\n",
    "\n",
    "for feature in continuous_feature1:\n",
    "    data=df.copy()\n",
    "    data[feature]=np.log(data[feature])\n",
    "    data.boxplot(column=feature)\n",
    "    plt.ylabel(feature)\n",
    "    plt.title(feature)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#taking the box plot of 'longitude'\n",
    "data=df.copy()\n",
    "data['longitude']=np.log(data['longitude'].abs())\n",
    "data.boxplot(column='longitude')\n",
    "plt.ylabel('longitude')\n",
    "plt.title('longitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observation 2d)\n",
    "We observe from series of scatterplots,boxplots above and histogram (beginning plot of all variables) of Continuous Features  that there is essentially a Linear Relationship between price and Continuous Features with many outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Missing Values and Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bedrooms 2.69 % missing values\n",
      "bathrooms 2.6 % missing values\n",
      "floors 2.82 % missing values\n",
      "waterfront 2.84 % missing values\n",
      "view 2.66 % missing values\n",
      "review_score 5.6 % missing values\n",
      "basement_size 2.55 % missing values\n",
      "built 2.8 % missing values\n",
      "renovation 2.66 % missing values\n",
      "zipcode 2.66 % missing values\n",
      "latitude 2.68 % missing values\n",
      "longitude 2.63 % missing values\n",
      "living_room_size 2.75 % missing values\n",
      "lot_size 2.66 % missing values\n"
     ]
    }
   ],
   "source": [
    "## In Missing Values we check the percentage of nan values present in each feature (all are numerical features)\n",
    "\n",
    "data=df.copy()\n",
    "## Step 1 make the list of features which has missing values\n",
    "features_with_na=[features for features in df.columns if data[features].isnull().sum()>1]\n",
    "## Step 2 print the feature name and the percentage of missing values\n",
    "for feature in features_with_na:\n",
    "    print(feature, np.round(data[feature].isnull().mean()*100, 2),  '% missing values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from Observation 2a)-d) we conclude that we will fill all the nan or null values with median\n",
    "df = df.fillna(df.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Temporal Variables (Date Time Variables) Feature Engineering as per Observation 2b)\n",
    "for feature in ['built','renovation']:\n",
    "    df[feature]=df['date']-df[feature]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We Drop the date Column as we have already performed Feature Engineering on it \n",
    "df = df.drop('date',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 21896 entries, 0 to 23153\n",
      "Data columns (total 16 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   price             21896 non-null  float64\n",
      " 1   bedrooms          21896 non-null  float64\n",
      " 2   bathrooms         21896 non-null  float64\n",
      " 3   floors            21896 non-null  float64\n",
      " 4   waterfront        21896 non-null  float64\n",
      " 5   view              21896 non-null  float64\n",
      " 6   condition         21896 non-null  float64\n",
      " 7   review_score      21896 non-null  float64\n",
      " 8   basement_size     21896 non-null  float64\n",
      " 9   built             21896 non-null  float64\n",
      " 10  renovation        21896 non-null  float64\n",
      " 11  zipcode           21896 non-null  float64\n",
      " 12  latitude          21896 non-null  float64\n",
      " 13  longitude         21896 non-null  float64\n",
      " 14  living_room_size  21896 non-null  float64\n",
      " 15  lot_size          21896 non-null  float64\n",
      "dtypes: float64(16)\n",
      "memory usage: 2.8 MB\n"
     ]
    }
   ],
   "source": [
    "#display information : number of rows and columns, Column or  Feature name \n",
    "#the total memory usage, the data type of each column, and the number of non-Null elements.\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>review_score</th>\n",
       "      <th>basement_size</th>\n",
       "      <th>built</th>\n",
       "      <th>renovation</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>living_room_size</th>\n",
       "      <th>lot_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>221900.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>59.00</td>\n",
       "      <td>2014.00</td>\n",
       "      <td>98178.00</td>\n",
       "      <td>47.51</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>1340.00</td>\n",
       "      <td>5650.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>538000.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>63.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>98125.00</td>\n",
       "      <td>47.72</td>\n",
       "      <td>-122.32</td>\n",
       "      <td>1690.00</td>\n",
       "      <td>7639.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180000.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>82.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>98028.00</td>\n",
       "      <td>47.74</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>2720.00</td>\n",
       "      <td>8062.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>604000.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>910.00</td>\n",
       "      <td>49.00</td>\n",
       "      <td>2014.00</td>\n",
       "      <td>98136.00</td>\n",
       "      <td>47.52</td>\n",
       "      <td>-122.39</td>\n",
       "      <td>1360.00</td>\n",
       "      <td>5000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>510000.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>98074.00</td>\n",
       "      <td>47.62</td>\n",
       "      <td>-122.05</td>\n",
       "      <td>1800.00</td>\n",
       "      <td>7503.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1225000.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1530.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>2014.00</td>\n",
       "      <td>98053.00</td>\n",
       "      <td>47.66</td>\n",
       "      <td>-122.00</td>\n",
       "      <td>4760.00</td>\n",
       "      <td>101930.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>257500.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>2014.00</td>\n",
       "      <td>98003.00</td>\n",
       "      <td>47.31</td>\n",
       "      <td>-122.33</td>\n",
       "      <td>2238.00</td>\n",
       "      <td>6819.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>291850.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.50</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>0.00</td>\n",
       "      <td>52.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>98198.00</td>\n",
       "      <td>47.41</td>\n",
       "      <td>-122.31</td>\n",
       "      <td>1650.00</td>\n",
       "      <td>9711.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>229500.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</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>730.00</td>\n",
       "      <td>55.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>98146.00</td>\n",
       "      <td>47.51</td>\n",
       "      <td>-122.34</td>\n",
       "      <td>1780.00</td>\n",
       "      <td>8113.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>323000.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>98038.00</td>\n",
       "      <td>47.37</td>\n",
       "      <td>-122.03</td>\n",
       "      <td>2390.00</td>\n",
       "      <td>7570.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>662500.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.50</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>1700.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>98007.00</td>\n",
       "      <td>47.60</td>\n",
       "      <td>-122.14</td>\n",
       "      <td>2210.00</td>\n",
       "      <td>8925.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>468000.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>72.00</td>\n",
       "      <td>2014.00</td>\n",
       "      <td>98115.00</td>\n",
       "      <td>47.69</td>\n",
       "      <td>-122.29</td>\n",
       "      <td>1330.00</td>\n",
       "      <td>6000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>400000.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>2014.00</td>\n",
       "      <td>98074.00</td>\n",
       "      <td>47.61</td>\n",
       "      <td>-122.05</td>\n",
       "      <td>1370.00</td>\n",
       "      <td>10208.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>530000.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>115.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>98107.00</td>\n",
       "      <td>47.67</td>\n",
       "      <td>-122.39</td>\n",
       "      <td>1360.00</td>\n",
       "      <td>4850.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>650000.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>970.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>98126.00</td>\n",
       "      <td>47.57</td>\n",
       "      <td>-122.38</td>\n",
       "      <td>2140.00</td>\n",
       "      <td>4000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>395000.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>2014.00</td>\n",
       "      <td>98019.00</td>\n",
       "      <td>47.73</td>\n",
       "      <td>-121.96</td>\n",
       "      <td>1890.00</td>\n",
       "      <td>14018.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>485000.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98.00</td>\n",
       "      <td>2014.00</td>\n",
       "      <td>98103.00</td>\n",
       "      <td>47.66</td>\n",
       "      <td>-122.34</td>\n",
       "      <td>1610.00</td>\n",
       "      <td>4300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>189000.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>93.00</td>\n",
       "      <td>2014.00</td>\n",
       "      <td>98002.00</td>\n",
       "      <td>47.31</td>\n",
       "      <td>-122.21</td>\n",
       "      <td>1060.00</td>\n",
       "      <td>5095.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>230000.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>98003.00</td>\n",
       "      <td>47.33</td>\n",
       "      <td>-122.31</td>\n",
       "      <td>1280.00</td>\n",
       "      <td>8850.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>385000.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>760.00</td>\n",
       "      <td>67.00</td>\n",
       "      <td>2014.00</td>\n",
       "      <td>98133.00</td>\n",
       "      <td>47.70</td>\n",
       "      <td>-122.34</td>\n",
       "      <td>1400.00</td>\n",
       "      <td>4980.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        price  bedrooms  bathrooms  floors  waterfront  view  condition  \\\n",
       "0   221900.00      3.00       1.00    1.00        0.00  0.00       3.00   \n",
       "1   538000.00      3.00       2.25    2.00        0.00  0.00       3.00   \n",
       "2   180000.00      2.00       1.00    1.00        0.00  0.00       3.00   \n",
       "3   604000.00      4.00       3.00    1.00        0.00  0.00       5.00   \n",
       "4   510000.00      3.00       2.00    1.00        0.00  0.00       3.00   \n",
       "5  1225000.00      4.00       4.50    1.00        0.00  0.00       3.00   \n",
       "6   257500.00      3.00       2.25    2.00        0.00  0.00       3.00   \n",
       "7   291850.00      3.00       1.50    1.00        0.00  0.00       3.00   \n",
       "8   229500.00      3.00       1.00    1.00        0.00  0.00       3.00   \n",
       "9   323000.00      3.00       2.50    2.00        0.00  0.00       3.00   \n",
       "10  662500.00      3.00       2.50    1.00        0.00  0.00       3.00   \n",
       "11  468000.00      2.00       1.00    1.00        0.00  0.00       4.00   \n",
       "13  400000.00      3.00       1.75    1.00        0.00  0.00       4.00   \n",
       "14  530000.00      5.00       2.00    1.50        0.00  0.00       3.00   \n",
       "15  650000.00      4.00       3.00    2.00        0.00  3.00       3.00   \n",
       "16  395000.00      3.00       2.00    2.00        0.00  0.00       3.00   \n",
       "17  485000.00      4.00       1.00    1.50        0.00  0.00       4.00   \n",
       "18  189000.00      2.00       1.00    1.00        0.00  0.00       4.00   \n",
       "19  230000.00      3.00       1.00    1.00        0.00  0.00       4.00   \n",
       "20  385000.00      4.00       1.75    1.00        0.00  0.00       4.00   \n",
       "\n",
       "    review_score  basement_size  built  renovation  zipcode  latitude  \\\n",
       "0          10.00           0.00  59.00     2014.00 98178.00     47.51   \n",
       "1           7.00         400.00  63.00       23.00 98125.00     47.72   \n",
       "2           8.00           0.00  82.00     2015.00 98028.00     47.74   \n",
       "3           6.00         910.00  49.00     2014.00 98136.00     47.52   \n",
       "4           6.00           0.00  28.00     2015.00 98074.00     47.62   \n",
       "5           8.00        1530.00  13.00     2014.00 98053.00     47.66   \n",
       "6           7.00           0.00  19.00     2014.00 98003.00     47.31   \n",
       "7           7.00           0.00  52.00     2015.00 98198.00     47.41   \n",
       "8           7.00         730.00  55.00     2015.00 98146.00     47.51   \n",
       "9           7.00           0.00  12.00     2015.00 98038.00     47.37   \n",
       "10          7.00        1700.00  50.00     2015.00 98007.00     47.60   \n",
       "11          6.00         300.00  72.00     2014.00 98115.00     47.69   \n",
       "13          7.00           0.00  37.00     2014.00 98074.00     47.61   \n",
       "14          9.00           0.00 115.00     2015.00 98107.00     47.67   \n",
       "15          7.00         970.00  36.00     2015.00 98126.00     47.57   \n",
       "16          7.00           0.00  20.00     2014.00 98019.00     47.73   \n",
       "17          9.00           0.00  98.00     2014.00 98103.00     47.66   \n",
       "18          8.00           0.00  93.00     2014.00 98002.00     47.31   \n",
       "19          8.00           0.00  46.00     2015.00 98003.00     47.33   \n",
       "20          9.00         760.00  67.00     2014.00 98133.00     47.70   \n",
       "\n",
       "    longitude  living_room_size  lot_size  \n",
       "0     -122.26           1340.00   5650.00  \n",
       "1     -122.32           1690.00   7639.00  \n",
       "2     -122.23           2720.00   8062.00  \n",
       "3     -122.39           1360.00   5000.00  \n",
       "4     -122.05           1800.00   7503.00  \n",
       "5     -122.00           4760.00 101930.00  \n",
       "6     -122.33           2238.00   6819.00  \n",
       "7     -122.31           1650.00   9711.00  \n",
       "8     -122.34           1780.00   8113.00  \n",
       "9     -122.03           2390.00   7570.00  \n",
       "10    -122.14           2210.00   8925.00  \n",
       "11    -122.29           1330.00   6000.00  \n",
       "13    -122.05           1370.00  10208.00  \n",
       "14    -122.39           1360.00   4850.00  \n",
       "15    -122.38           2140.00   4000.00  \n",
       "16    -121.96           1890.00  14018.00  \n",
       "17    -122.34           1610.00   4300.00  \n",
       "18    -122.21           1060.00   5095.00  \n",
       "19    -122.31           1280.00   8850.00  \n",
       "20    -122.34           1400.00   4980.00  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Regression Algorithms to Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train(=Independent variables) and label(=target variable) \n",
    "label = df['price']\n",
    "train = df.drop(['price'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train and test split\n",
    "x_train , x_test , y_train , y_test = train_test_split(train , label , test_size = 0.15,random_state =2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model - Multiple Linear Regression\n",
    "mlr = LinearRegression()\n",
    "# Run fit fuction\n",
    "mlr.fit(x_train,y_train)\n",
    "#R Squared Score\n",
    "mlr_score=mlr.score(x_test,y_test)\n",
    "#Run Predict on test set\n",
    "pred_mlr = mlr.predict(x_test)\n",
    "#get explained variance (measure of Accuracy of Prediction)\n",
    "expl_mlr = explained_variance_score(pred_mlr,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model - ensemble - Gradient Boosting \n",
    "gbm = ensemble.GradientBoostingRegressor(n_estimators = 400, max_depth = 5, min_samples_split = 2,\n",
    "          learning_rate = 0.1, loss = 'ls')\n",
    "#run fit\n",
    "gbm.fit(x_train, y_train)\n",
    "#R Squared Score\n",
    "gbm_score = gbm.score(x_test,y_test)\n",
    "#Run Predict on test set\n",
    "pred_gbm = gbm.predict(x_test)\n",
    "#get explained variance (measure of Accuracy of Prediction)\n",
    "expl_gbm = explained_variance_score(pred_gbm,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decision Tree Algorthm\n",
    "tr_regressor = DecisionTreeRegressor(random_state=0)\n",
    "#run fit function\n",
    "tr_regressor.fit(x_train,y_train)\n",
    "#R Squared Score\n",
    "decision_score=tr_regressor.score(x_test,y_test)\n",
    "#Run Predict on test set\n",
    "pred_tr = tr_regressor.predict(x_test)\n",
    "#get explained variance (measure of Accuracy of Prediction)\n",
    "expl_tr = explained_variance_score(pred_tr,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Classification Algorithm to Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random Forest - Classification Algorithm\n",
    "rf_classifier= RandomForestRegressor(random_state=0)\n",
    "#run fit function\n",
    "rf_classifier.fit(x_train,y_train)\n",
    "#R Squared Score\n",
    "rf_score = rf_classifier.score(x_test,y_test)\n",
    "#Run Predict on test set\n",
    "rf_pred = rf_classifier.predict(x_test)\n",
    "#get explained variance (measure of Accuracy of Prediction)\n",
    "expl_rf = explained_variance_score(rf_pred,y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>R-Squared Score</th>\n",
       "      <th>Explained Variance Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gradient Boosting Method</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random forest</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Multiple Linear Regression</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model  R-Squared Score  Explained Variance Score\n",
       "1    Gradient Boosting Method             0.81                      0.76\n",
       "3               Random forest             0.78                      0.70\n",
       "2               Decision Tree             0.60                      0.55\n",
       "0  Multiple Linear Regression             0.57                      0.19"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_score =pd.DataFrame({'Model':['Multiple Linear Regression','Gradient Boosting Method','Decision Tree','Random forest'],\n",
    "                            'R-Squared Score':[mlr_score,gbm_score,decision_score,rf_score],\n",
    "                            'Explained Variance Score':[expl_mlr,expl_gbm,expl_tr,expl_rf]\n",
    "                           })\n",
    "models_score.sort_values(by='R-Squared Score',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}