Toronto Home Prices Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.rcParams[\"figure.figsize\"] = (20,10)\n",
    "from sklearn.model_selection import train_test_split\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>title</th>\n",
       "      <th>final_price</th>\n",
       "      <th>list_price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft</th>\n",
       "      <th>parking</th>\n",
       "      <th>description</th>\n",
       "      <th>mls</th>\n",
       "      <th>...</th>\n",
       "      <th>full_address</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>city_district</th>\n",
       "      <th>mean_district_income</th>\n",
       "      <th>district_code</th>\n",
       "      <th>final_price_transformed</th>\n",
       "      <th>final_price_log</th>\n",
       "      <th>bedrooms_ag</th>\n",
       "      <th>bedrooms_bg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1303 - 38 Grenville St, Toronto (C4461599) | Z...</td>\n",
       "      <td>855000</td>\n",
       "      <td>870000</td>\n",
       "      <td>2 + 1 beds</td>\n",
       "      <td>2</td>\n",
       "      <td>850.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Luxurious And Spacious Murano Tower. 2+1, 2 Ba...</td>\n",
       "      <td>C4461599</td>\n",
       "      <td>...</td>\n",
       "      <td>38 Grenville St, Toronto , Ontario, Canada</td>\n",
       "      <td>43.661896</td>\n",
       "      <td>-79.385748</td>\n",
       "      <td>Bay Street Corridor</td>\n",
       "      <td>56526</td>\n",
       "      <td>76</td>\n",
       "      <td>2.391687</td>\n",
       "      <td>13.658857</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2 Cabot Crt, Toronto (W4502992) | Zoocasa</td>\n",
       "      <td>885000</td>\n",
       "      <td>898000</td>\n",
       "      <td>3 beds</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>Fantastic Opportunity To Live Within The Histo...</td>\n",
       "      <td>W4502992</td>\n",
       "      <td>...</td>\n",
       "      <td>2 Cabot Crt, Toronto , Ontario, Canada</td>\n",
       "      <td>43.646982</td>\n",
       "      <td>-79.530356</td>\n",
       "      <td>Islington-City Centre West</td>\n",
       "      <td>52787</td>\n",
       "      <td>14</td>\n",
       "      <td>2.391803</td>\n",
       "      <td>13.693343</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1504 - 30 Roehampton Ave, Toronto (C4511330) |...</td>\n",
       "      <td>550000</td>\n",
       "      <td>549900</td>\n",
       "      <td>1 beds</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bright Sunfilled Spacious 1 Bdr Unit; Floor To...</td>\n",
       "      <td>C4511330</td>\n",
       "      <td>...</td>\n",
       "      <td>30 Roehampton Ave, Toronto , Ontario, Canada</td>\n",
       "      <td>43.708472</td>\n",
       "      <td>-79.397498</td>\n",
       "      <td>Mount Pleasant West</td>\n",
       "      <td>57039</td>\n",
       "      <td>104</td>\n",
       "      <td>2.390053</td>\n",
       "      <td>13.217674</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>514 - 65 East Liberty St, Toronto (C4515763) |...</td>\n",
       "      <td>665000</td>\n",
       "      <td>600000</td>\n",
       "      <td>1 + 1 beds</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Rare Loft-Like Condo In Liberty Village W/ 18'...</td>\n",
       "      <td>C4515763</td>\n",
       "      <td>...</td>\n",
       "      <td>65 East Liberty St, Toronto , Ontario, Canada</td>\n",
       "      <td>43.638399</td>\n",
       "      <td>-79.414448</td>\n",
       "      <td>Niagara</td>\n",
       "      <td>70623</td>\n",
       "      <td>82</td>\n",
       "      <td>2.390793</td>\n",
       "      <td>13.407542</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>61 Twelfth St, Toronto (W4519375) | Zoocasa</td>\n",
       "      <td>825513</td>\n",
       "      <td>839000</td>\n",
       "      <td>2 beds</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Location! Location! Location. Your Cottage In ...</td>\n",
       "      <td>W4519375</td>\n",
       "      <td>...</td>\n",
       "      <td>61 Twelfth St, Toronto , Ontario, Canada</td>\n",
       "      <td>43.597096</td>\n",
       "      <td>-79.509612</td>\n",
       "      <td>New Toronto</td>\n",
       "      <td>44101</td>\n",
       "      <td>18</td>\n",
       "      <td>2.391568</td>\n",
       "      <td>13.623760</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                                              title  final_price  \\\n",
       "0      0  1303 - 38 Grenville St, Toronto (C4461599) | Z...       855000   \n",
       "1      1          2 Cabot Crt, Toronto (W4502992) | Zoocasa       885000   \n",
       "2      2  1504 - 30 Roehampton Ave, Toronto (C4511330) |...       550000   \n",
       "3      3  514 - 65 East Liberty St, Toronto (C4515763) |...       665000   \n",
       "4      4        61 Twelfth St, Toronto (W4519375) | Zoocasa       825513   \n",
       "\n",
       "   list_price    bedrooms  bathrooms   sqft  parking  \\\n",
       "0      870000  2 + 1 beds          2  850.0        1   \n",
       "1      898000      3 beds          2    NaN        6   \n",
       "2      549900      1 beds          1  550.0        0   \n",
       "3      600000  1 + 1 beds          1  650.0        1   \n",
       "4      839000      2 beds          2    NaN        1   \n",
       "\n",
       "                                         description       mls  ...  \\\n",
       "0  Luxurious And Spacious Murano Tower. 2+1, 2 Ba...  C4461599  ...   \n",
       "1  Fantastic Opportunity To Live Within The Histo...  W4502992  ...   \n",
       "2  Bright Sunfilled Spacious 1 Bdr Unit; Floor To...  C4511330  ...   \n",
       "3  Rare Loft-Like Condo In Liberty Village W/ 18'...  C4515763  ...   \n",
       "4  Location! Location! Location. Your Cottage In ...  W4519375  ...   \n",
       "\n",
       "                                     full_address        lat       long  \\\n",
       "0      38 Grenville St, Toronto , Ontario, Canada  43.661896 -79.385748   \n",
       "1          2 Cabot Crt, Toronto , Ontario, Canada  43.646982 -79.530356   \n",
       "2    30 Roehampton Ave, Toronto , Ontario, Canada  43.708472 -79.397498   \n",
       "3   65 East Liberty St, Toronto , Ontario, Canada  43.638399 -79.414448   \n",
       "4        61 Twelfth St, Toronto , Ontario, Canada  43.597096 -79.509612   \n",
       "\n",
       "                city_district  mean_district_income district_code  \\\n",
       "0         Bay Street Corridor                 56526            76   \n",
       "1  Islington-City Centre West                 52787            14   \n",
       "2         Mount Pleasant West                 57039           104   \n",
       "3                     Niagara                 70623            82   \n",
       "4                 New Toronto                 44101            18   \n",
       "\n",
       "   final_price_transformed  final_price_log  bedrooms_ag  bedrooms_bg  \n",
       "0                 2.391687        13.658857            2            1  \n",
       "1                 2.391803        13.693343            3            0  \n",
       "2                 2.390053        13.217674            1            0  \n",
       "3                 2.390793        13.407542            1            1  \n",
       "4                 2.391568        13.623760            2            0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 472,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(\"houses_edited.csv\")\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15234, 22)"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Att/Row/Twnhouse       467\n",
       "Co-Op Apt               24\n",
       "Co-Ownership Apt        27\n",
       "Comm Element Condo     181\n",
       "Condo Apt             7572\n",
       "Condo Townhouse       1062\n",
       "Detached              4361\n",
       "Link                    27\n",
       "Plex                    70\n",
       "Semi-Detached         1435\n",
       "Store W/Apt/Offc         8\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 474,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby('type')['type'].agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>final_price</th>\n",
       "      <th>list_price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft</th>\n",
       "      <th>type</th>\n",
       "      <th>city_district</th>\n",
       "      <th>bedrooms_ag</th>\n",
       "      <th>bedrooms_bg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>855000</td>\n",
       "      <td>870000</td>\n",
       "      <td>2 + 1 beds</td>\n",
       "      <td>2</td>\n",
       "      <td>850.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>Bay Street Corridor</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>885000</td>\n",
       "      <td>898000</td>\n",
       "      <td>3 beds</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Semi-Detached</td>\n",
       "      <td>Islington-City Centre West</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>550000</td>\n",
       "      <td>549900</td>\n",
       "      <td>1 beds</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>Mount Pleasant West</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>665000</td>\n",
       "      <td>600000</td>\n",
       "      <td>1 + 1 beds</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>Niagara</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>825513</td>\n",
       "      <td>839000</td>\n",
       "      <td>2 beds</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Detached</td>\n",
       "      <td>New Toronto</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  final_price  list_price    bedrooms  bathrooms   sqft  \\\n",
       "0      0       855000      870000  2 + 1 beds          2  850.0   \n",
       "1      1       885000      898000      3 beds          2    NaN   \n",
       "2      2       550000      549900      1 beds          1  550.0   \n",
       "3      3       665000      600000  1 + 1 beds          1  650.0   \n",
       "4      4       825513      839000      2 beds          2    NaN   \n",
       "\n",
       "            type               city_district  bedrooms_ag  bedrooms_bg  \n",
       "0      Condo Apt         Bay Street Corridor            2            1  \n",
       "1  Semi-Detached  Islington-City Centre West            3            0  \n",
       "2      Condo Apt         Mount Pleasant West            1            0  \n",
       "3      Condo Apt                     Niagara            1            1  \n",
       "4       Detached                 New Toronto            2            0  "
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df1.drop(['final_price_log','final_price_transformed','title','parking','lat','long','district_code','mean_district_income','district_code','full_link','full_address','mls','description'],axis = 'columns')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index               0\n",
       "final_price         0\n",
       "list_price          0\n",
       "bedrooms            0\n",
       "bathrooms           0\n",
       "sqft             4521\n",
       "type                0\n",
       "city_district       0\n",
       "bedrooms_ag         0\n",
       "bedrooms_bg         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 476,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index            0\n",
       "final_price      0\n",
       "list_price       0\n",
       "bedrooms         0\n",
       "bathrooms        0\n",
       "sqft             0\n",
       "type             0\n",
       "city_district    0\n",
       "bedrooms_ag      0\n",
       "bedrooms_bg      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2.dropna()\n",
    "df3.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10713, 10)"
      ]
     },
     "execution_count": 478,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2 + 1 beds', '1 beds', '1 + 1 beds', '0 beds', '4 beds', '3 beds',\n",
       "       '2 beds', '4 + 2 beds', '3 + 1 beds', '3 + 2 beds', '4 + 3 beds',\n",
       "       '4 + 1 beds', '5 beds', '2 + 2 beds', '5 + 2 beds', '6 + 2 beds',\n",
       "       '6 beds', '1 + 2 beds', '0 + 1 beds', '5 + 1 beds', '5 + 3 beds',\n",
       "       '2 + 3 beds', '6 + 1 beds', '8 + 3 beds', '3 + 3 beds',\n",
       "       '6 + 3 beds', '4 + 4 beds', '7 + 3 beds', '7 + 1 beds',\n",
       "       '9 + 3 beds', '9 beds', '9 + 2 beds', '7 beds', '4 + 6 beds',\n",
       "       '5 + 6 beds', '8 + 4 beds', '2 + 4 beds'], dtype=object)"
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['bedrooms'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  1,  5,  4,  3,  6,  7,  8,  9, 14, 10], dtype=int64)"
      ]
     },
     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['bathrooms'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Username\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df3['total_bedrooms'] = df3['bedrooms_ag'] + df3['bedrooms_bg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>final_price</th>\n",
       "      <th>list_price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft</th>\n",
       "      <th>type</th>\n",
       "      <th>city_district</th>\n",
       "      <th>bedrooms_ag</th>\n",
       "      <th>bedrooms_bg</th>\n",
       "      <th>total_bedrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>855000</td>\n",
       "      <td>870000</td>\n",
       "      <td>2 + 1 beds</td>\n",
       "      <td>2</td>\n",
       "      <td>850.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>Bay Street Corridor</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>550000</td>\n",
       "      <td>549900</td>\n",
       "      <td>1 beds</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>Mount Pleasant West</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>665000</td>\n",
       "      <td>600000</td>\n",
       "      <td>1 + 1 beds</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>Niagara</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>368000</td>\n",
       "      <td>289000</td>\n",
       "      <td>0 beds</td>\n",
       "      <td>1</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>New Toronto</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>2700000</td>\n",
       "      <td>2798000</td>\n",
       "      <td>4 beds</td>\n",
       "      <td>5</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>Detached</td>\n",
       "      <td>Lawrence Park South</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>200000</td>\n",
       "      <td>199000</td>\n",
       "      <td>3 beds</td>\n",
       "      <td>2</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>Condo Townhouse</td>\n",
       "      <td>Glenfield-Jane Heights</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>975000</td>\n",
       "      <td>954900</td>\n",
       "      <td>2 beds</td>\n",
       "      <td>2</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>Plex</td>\n",
       "      <td>Mount Pleasant East</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>269900</td>\n",
       "      <td>269900</td>\n",
       "      <td>1 beds</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>Malvern</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1530000</td>\n",
       "      <td>1399900</td>\n",
       "      <td>4 + 2 beds</td>\n",
       "      <td>5</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>Detached</td>\n",
       "      <td>Wexford/Maryvale</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>2250000</td>\n",
       "      <td>1990000</td>\n",
       "      <td>3 + 1 beds</td>\n",
       "      <td>4</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>Semi-Detached</td>\n",
       "      <td>Cabbagetown-South St. James Town</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  final_price  list_price    bedrooms  bathrooms    sqft  \\\n",
       "0       0       855000      870000  2 + 1 beds          2   850.0   \n",
       "2       2       550000      549900      1 beds          1   550.0   \n",
       "3       3       665000      600000  1 + 1 beds          1   650.0   \n",
       "5       5       368000      289000      0 beds          1   250.0   \n",
       "6       6      2700000     2798000      4 beds          5  2750.0   \n",
       "7       7       200000      199000      3 beds          2  1100.0   \n",
       "8       8       975000      954900      2 beds          2  1300.0   \n",
       "9       9       269900      269900      1 beds          1   650.0   \n",
       "11     11      1530000     1399900  4 + 2 beds          5  3250.0   \n",
       "12     12      2250000     1990000  3 + 1 beds          4  2750.0   \n",
       "\n",
       "               type                     city_district  bedrooms_ag  \\\n",
       "0         Condo Apt               Bay Street Corridor            2   \n",
       "2         Condo Apt               Mount Pleasant West            1   \n",
       "3         Condo Apt                           Niagara            1   \n",
       "5         Condo Apt                       New Toronto            0   \n",
       "6          Detached               Lawrence Park South            4   \n",
       "7   Condo Townhouse            Glenfield-Jane Heights            3   \n",
       "8              Plex               Mount Pleasant East            2   \n",
       "9         Condo Apt                           Malvern            1   \n",
       "11         Detached                  Wexford/Maryvale            4   \n",
       "12    Semi-Detached  Cabbagetown-South St. James Town            3   \n",
       "\n",
       "    bedrooms_bg  total_bedrooms  \n",
       "0             1               3  \n",
       "2             0               1  \n",
       "3             1               2  \n",
       "5             0               0  \n",
       "6             0               4  \n",
       "7             0               3  \n",
       "8             0               2  \n",
       "9             0               1  \n",
       "11            2               6  \n",
       "12            1               4  "
      ]
     },
     "execution_count": 482,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  1,  2,  0,  4,  6,  5,  7,  8, 11,  9, 10, 12], dtype=int64)"
      ]
     },
     "execution_count": 483,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['total_bedrooms'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>final_price</th>\n",
       "      <th>list_price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft</th>\n",
       "      <th>type</th>\n",
       "      <th>city_district</th>\n",
       "      <th>bedrooms_ag</th>\n",
       "      <th>bedrooms_bg</th>\n",
       "      <th>total_bedrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [index, final_price, list_price, bedrooms, bathrooms, sqft, type, city_district, bedrooms_ag, bedrooms_bg, total_bedrooms]\n",
       "Index: []"
      ]
     },
     "execution_count": 484,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[df3.total_bedrooms == 13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 850.,  550.,  650.,  250., 2750., 1100., 1300., 3250., 1750.,\n",
       "        750.,  900., 2374.,  950., 2250., 1900., 2124., 1500., 4250.,\n",
       "       1700., 4374.,  800., 2874., 3374., 2624.,  600., 3124., 1000.,\n",
       "       1200.])"
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.sqft.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_float(x):\n",
    "    try:\n",
    "        float(x)\n",
    "    except:\n",
    "        return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>final_price</th>\n",
       "      <th>list_price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft</th>\n",
       "      <th>type</th>\n",
       "      <th>city_district</th>\n",
       "      <th>bedrooms_ag</th>\n",
       "      <th>bedrooms_bg</th>\n",
       "      <th>total_bedrooms</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [index, final_price, list_price, bedrooms, bathrooms, sqft, type, city_district, bedrooms_ag, bedrooms_bg, total_bedrooms]\n",
       "Index: []"
      ]
     },
     "execution_count": 487,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[~df3['sqft'].apply(is_float)].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                               0\n",
       "final_price                    855000\n",
       "list_price                     870000\n",
       "bedrooms                   2 + 1 beds\n",
       "bathrooms                           2\n",
       "sqft                              850\n",
       "type                        Condo Apt\n",
       "city_district     Bay Street Corridor\n",
       "bedrooms_ag                         2\n",
       "bedrooms_bg                         1\n",
       "total_bedrooms                      3\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 488,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>final_price</th>\n",
       "      <th>list_price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft</th>\n",
       "      <th>type</th>\n",
       "      <th>city_district</th>\n",
       "      <th>bedrooms_ag</th>\n",
       "      <th>bedrooms_bg</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>855000</td>\n",
       "      <td>870000</td>\n",
       "      <td>2 + 1 beds</td>\n",
       "      <td>2</td>\n",
       "      <td>850.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>Bay Street Corridor</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1005.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>550000</td>\n",
       "      <td>549900</td>\n",
       "      <td>1 beds</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>Mount Pleasant West</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>665000</td>\n",
       "      <td>600000</td>\n",
       "      <td>1 + 1 beds</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>Niagara</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1023.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>368000</td>\n",
       "      <td>289000</td>\n",
       "      <td>0 beds</td>\n",
       "      <td>1</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>New Toronto</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1472.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>2700000</td>\n",
       "      <td>2798000</td>\n",
       "      <td>4 beds</td>\n",
       "      <td>5</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>Detached</td>\n",
       "      <td>Lawrence Park South</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>981.818182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  final_price  list_price    bedrooms  bathrooms    sqft       type  \\\n",
       "0      0       855000      870000  2 + 1 beds          2   850.0  Condo Apt   \n",
       "2      2       550000      549900      1 beds          1   550.0  Condo Apt   \n",
       "3      3       665000      600000  1 + 1 beds          1   650.0  Condo Apt   \n",
       "5      5       368000      289000      0 beds          1   250.0  Condo Apt   \n",
       "6      6      2700000     2798000      4 beds          5  2750.0   Detached   \n",
       "\n",
       "         city_district  bedrooms_ag  bedrooms_bg  total_bedrooms  \\\n",
       "0  Bay Street Corridor            2            1               3   \n",
       "2  Mount Pleasant West            1            0               1   \n",
       "3              Niagara            1            1               2   \n",
       "5          New Toronto            0            0               0   \n",
       "6  Lawrence Park South            4            0               4   \n",
       "\n",
       "   price_per_sqft  \n",
       "0     1005.882353  \n",
       "2     1000.000000  \n",
       "3     1023.076923  \n",
       "5     1472.000000  \n",
       "6      981.818182  "
      ]
     },
     "execution_count": 489,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df3.copy()\n",
    "df4['price_per_sqft'] = df4['final_price']/df4['sqft']\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "140"
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df4.city_district.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Username\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "city_district\n",
       "Waterfront Communities-The Island      1145\n",
       "Niagara                                 525\n",
       "Mimico (includes Humber Bay Shores)     470\n",
       "Willowdale East                         425\n",
       "Church-Yonge Corridor                   380\n",
       "Islington-City Centre West              370\n",
       "Bay Street Corridor                     276\n",
       "Mount Pleasant West                     226\n",
       "Bayview Village                         195\n",
       "Moss Park                               181\n",
       "Malvern                                 175\n",
       "L'Amoreaux                              160\n",
       "Annex                                   158\n",
       "High Park-Swansea                       139\n",
       "Banbury-Don Mills                       137\n",
       "Henry Farm                              135\n",
       "Agincourt South-Malvern West            134\n",
       "Woburn                                  121\n",
       "Dovercourt-Wallace Emerson-Junction     121\n",
       "Little Portugal                         121\n",
       "Willowdale West                         114\n",
       "South Riverdale                         112\n",
       "Bendale                                 112\n",
       "Rosedale-Moore Park                      99\n",
       "Downsview-Roding-CFB                     98\n",
       "West Humber-Clairville                   95\n",
       "York University Heights                  95\n",
       "Mount Olive-Silverstone-Jamestown        89\n",
       "Newtonbrook West                         88\n",
       "Dorset Park                              86\n",
       "                                       ... \n",
       "O'Connor-Parkview                        21\n",
       "Humber Summit                            21\n",
       "Weston-Pelham Park                       20\n",
       "Oakwood Village                          19\n",
       "Alderwood                                19\n",
       "Runnymede-Bloor West Village             19\n",
       "Danforth East York                       18\n",
       "Forest Hill North                        17\n",
       "Oakridge                                 16\n",
       "Palmerston-Little Italy                  15\n",
       "Humewood-Cedarvale                       15\n",
       "Keelesdale-Eglinton West                 14\n",
       "Maple Leaf                               14\n",
       "University                               14\n",
       "Highland Creek                           14\n",
       "Lambton Baby Point                       13\n",
       "Broadview North                          12\n",
       "Old East York                            12\n",
       "Bayview Woods-Steeles                    11\n",
       "Danforth                                 11\n",
       "Ionview                                  10\n",
       "Woodbine-Lumsden                          9\n",
       "Blake-Jones                               8\n",
       "Beechborough-Greenbrook                   8\n",
       "Playter Estates-Danforth                  8\n",
       "Rexdale-Kipling                           7\n",
       "North Riverdale                           7\n",
       "Thistletown-Beaumond Heights              7\n",
       "Caledonia-Fairbank                        5\n",
       "Rustic                                    4\n",
       "Name: city_district, Length: 140, dtype: int64"
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.location = df4.city_district.apply(lambda x: x.strip())\n",
    "location_stats = df4.groupby(\"city_district\")['city_district'].agg('count').sort_values(ascending = False)\n",
    "location_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(location_stats[location_stats <= 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city_district\n",
       "Ionview                         10\n",
       "Woodbine-Lumsden                 9\n",
       "Blake-Jones                      8\n",
       "Beechborough-Greenbrook          8\n",
       "Playter Estates-Danforth         8\n",
       "Rexdale-Kipling                  7\n",
       "North Riverdale                  7\n",
       "Thistletown-Beaumond Heights     7\n",
       "Caledonia-Fairbank               5\n",
       "Rustic                           4\n",
       "Name: city_district, dtype: int64"
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "less_than_ten = location_stats[location_stats <= 10]\n",
    "less_than_ten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "140"
      ]
     },
     "execution_count": 494,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df4.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "131"
      ]
     },
     "execution_count": 495,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.city_district = df4.city_district.apply(lambda x: 'Other' if x in less_than_ten else x)\n",
    "len(df4.city_district.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "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>index</th>\n",
       "      <th>final_price</th>\n",
       "      <th>list_price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft</th>\n",
       "      <th>type</th>\n",
       "      <th>city_district</th>\n",
       "      <th>bedrooms_ag</th>\n",
       "      <th>bedrooms_bg</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>855000</td>\n",
       "      <td>870000</td>\n",
       "      <td>2 + 1 beds</td>\n",
       "      <td>2</td>\n",
       "      <td>850.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>Bay Street Corridor</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1005.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>550000</td>\n",
       "      <td>549900</td>\n",
       "      <td>1 beds</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>Mount Pleasant West</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>665000</td>\n",
       "      <td>600000</td>\n",
       "      <td>1 + 1 beds</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>Niagara</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1023.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>368000</td>\n",
       "      <td>289000</td>\n",
       "      <td>0 beds</td>\n",
       "      <td>1</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>New Toronto</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1472.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>2700000</td>\n",
       "      <td>2798000</td>\n",
       "      <td>4 beds</td>\n",
       "      <td>5</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>Detached</td>\n",
       "      <td>Lawrence Park South</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>981.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>200000</td>\n",
       "      <td>199000</td>\n",
       "      <td>3 beds</td>\n",
       "      <td>2</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>Condo Townhouse</td>\n",
       "      <td>Glenfield-Jane Heights</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>181.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>975000</td>\n",
       "      <td>954900</td>\n",
       "      <td>2 beds</td>\n",
       "      <td>2</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>Plex</td>\n",
       "      <td>Mount Pleasant East</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>750.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>269900</td>\n",
       "      <td>269900</td>\n",
       "      <td>1 beds</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>Malvern</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>415.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1530000</td>\n",
       "      <td>1399900</td>\n",
       "      <td>4 + 2 beds</td>\n",
       "      <td>5</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>Detached</td>\n",
       "      <td>Wexford/Maryvale</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>470.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>2250000</td>\n",
       "      <td>1990000</td>\n",
       "      <td>3 + 1 beds</td>\n",
       "      <td>4</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>Semi-Detached</td>\n",
       "      <td>Cabbagetown-South St. James Town</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>818.181818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  final_price  list_price    bedrooms  bathrooms    sqft  \\\n",
       "0       0       855000      870000  2 + 1 beds          2   850.0   \n",
       "2       2       550000      549900      1 beds          1   550.0   \n",
       "3       3       665000      600000  1 + 1 beds          1   650.0   \n",
       "5       5       368000      289000      0 beds          1   250.0   \n",
       "6       6      2700000     2798000      4 beds          5  2750.0   \n",
       "7       7       200000      199000      3 beds          2  1100.0   \n",
       "8       8       975000      954900      2 beds          2  1300.0   \n",
       "9       9       269900      269900      1 beds          1   650.0   \n",
       "11     11      1530000     1399900  4 + 2 beds          5  3250.0   \n",
       "12     12      2250000     1990000  3 + 1 beds          4  2750.0   \n",
       "\n",
       "               type                     city_district  bedrooms_ag  \\\n",
       "0         Condo Apt               Bay Street Corridor            2   \n",
       "2         Condo Apt               Mount Pleasant West            1   \n",
       "3         Condo Apt                           Niagara            1   \n",
       "5         Condo Apt                       New Toronto            0   \n",
       "6          Detached               Lawrence Park South            4   \n",
       "7   Condo Townhouse            Glenfield-Jane Heights            3   \n",
       "8              Plex               Mount Pleasant East            2   \n",
       "9         Condo Apt                           Malvern            1   \n",
       "11         Detached                  Wexford/Maryvale            4   \n",
       "12    Semi-Detached  Cabbagetown-South St. James Town            3   \n",
       "\n",
       "    bedrooms_bg  total_bedrooms  price_per_sqft  \n",
       "0             1               3     1005.882353  \n",
       "2             0               1     1000.000000  \n",
       "3             1               2     1023.076923  \n",
       "5             0               0     1472.000000  \n",
       "6             0               4      981.818182  \n",
       "7             0               3      181.818182  \n",
       "8             0               2      750.000000  \n",
       "9             0               1      415.230769  \n",
       "11            2               6      470.769231  \n",
       "12            1               4      818.181818  "
      ]
     },
     "execution_count": 496,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "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>index</th>\n",
       "      <th>final_price</th>\n",
       "      <th>list_price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft</th>\n",
       "      <th>type</th>\n",
       "      <th>city_district</th>\n",
       "      <th>bedrooms_ag</th>\n",
       "      <th>bedrooms_bg</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>3088</td>\n",
       "      <td>691100</td>\n",
       "      <td>699900</td>\n",
       "      <td>3 + 3 beds</td>\n",
       "      <td>2</td>\n",
       "      <td>900.0</td>\n",
       "      <td>Detached</td>\n",
       "      <td>Birchcliffe-Cliffside</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>767.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3666</th>\n",
       "      <td>5483</td>\n",
       "      <td>552500</td>\n",
       "      <td>549000</td>\n",
       "      <td>1 + 1 beds</td>\n",
       "      <td>1</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>Waterfront Communities-The Island</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2210.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4538</th>\n",
       "      <td>6673</td>\n",
       "      <td>535000</td>\n",
       "      <td>539000</td>\n",
       "      <td>1 + 1 beds</td>\n",
       "      <td>1</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>Waterfront Communities-The Island</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2140.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11455</th>\n",
       "      <td>13698</td>\n",
       "      <td>500000</td>\n",
       "      <td>499000</td>\n",
       "      <td>1 + 1 beds</td>\n",
       "      <td>1</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>Niagara</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11626</th>\n",
       "      <td>13871</td>\n",
       "      <td>455000</td>\n",
       "      <td>459000</td>\n",
       "      <td>1 + 1 beds</td>\n",
       "      <td>1</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Condo Apt</td>\n",
       "      <td>Mimico (includes Humber Bay Shores)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1820.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  final_price  list_price    bedrooms  bathrooms   sqft  \\\n",
       "2034    3088       691100      699900  3 + 3 beds          2  900.0   \n",
       "3666    5483       552500      549000  1 + 1 beds          1  250.0   \n",
       "4538    6673       535000      539000  1 + 1 beds          1  250.0   \n",
       "11455  13698       500000      499000  1 + 1 beds          1  250.0   \n",
       "11626  13871       455000      459000  1 + 1 beds          1  250.0   \n",
       "\n",
       "            type                        city_district  bedrooms_ag  \\\n",
       "2034    Detached                Birchcliffe-Cliffside            3   \n",
       "3666   Condo Apt    Waterfront Communities-The Island            1   \n",
       "4538   Condo Apt    Waterfront Communities-The Island            1   \n",
       "11455  Condo Apt                              Niagara            1   \n",
       "11626  Condo Apt  Mimico (includes Humber Bay Shores)            1   \n",
       "\n",
       "       bedrooms_bg  total_bedrooms  price_per_sqft  \n",
       "2034             3               6      767.888889  \n",
       "3666             1               2     2210.000000  \n",
       "4538             1               2     2140.000000  \n",
       "11455            1               2     2000.000000  \n",
       "11626            1               2     1820.000000  "
      ]
     },
     "execution_count": 497,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4[df4.sqft/df4.total_bedrooms<160].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10713, 12)"
      ]
     },
     "execution_count": 498,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10704, 12)"
      ]
     },
     "execution_count": 499,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = df4[~(df4.sqft/df4.total_bedrooms<160)]\n",
    "df5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10704.000000\n",
       "mean       752.282753\n",
       "std        323.423726\n",
       "min        109.000000\n",
       "25%        520.981781\n",
       "50%        730.839161\n",
       "75%        909.090909\n",
       "max       2500.000000\n",
       "Name: price_per_sqft, dtype: float64"
      ]
     },
     "execution_count": 500,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.price_per_sqft.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8293, 12)"
      ]
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_pps_outliers(df):\n",
    "    df_out = pd.DataFrame()\n",
    "    for key, subdf in df.groupby('city_district'):\n",
    "        m = np.mean(subdf.price_per_sqft)\n",
    "        st = np.std(subdf.price_per_sqft)\n",
    "        reduced_df = subdf[(subdf.price_per_sqft > (m - st)) & (subdf.price_per_sqft <= (m + st))]\n",
    "        df_out = pd.concat([df_out, reduced_df], ignore_index = True)\n",
    "    return df_out\n",
    "\n",
    "df6 = remove_pps_outliers(df5)\n",
    "df6.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    8293.000000\n",
       "mean      735.007110\n",
       "std       231.735468\n",
       "min       215.789474\n",
       "25%       557.142857\n",
       "50%       754.545455\n",
       "75%       905.333333\n",
       "max      1505.335366\n",
       "Name: price_per_sqft, dtype: float64"
      ]
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.price_per_sqft.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_scatter_chart(df,city_district):\n",
    "    total_bedrooms_2 = df[(df.city_district == city_district) & (df.total_bedrooms == 2)]\n",
    "    total_bedrooms_3 = df[(df.city_district == city_district) & (df.total_bedrooms == 3)]\n",
    "    matplotlib.rcParams['figure.figsize'] = (15,10)\n",
    "    plt.scatter(total_bedrooms_2.sqft,total_bedrooms_2.final_price,color = 'blue',label = '2 beds', s = 50)\n",
    "    plt.scatter(total_bedrooms_3.sqft,total_bedrooms_3.final_price,marker = '+',color = 'green',label = '3 beds', s = 50)\n",
    "    plt.xlabel(\"Square Feet\")\n",
    "    plt.ylabel(\"Price\")\n",
    "    plt.title(city_district)\n",
    "    plt.legend()\n",
    "plot_scatter_chart(df6,\"Waterfront Communities-The Island\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5040, 12)"
      ]
     },
     "execution_count": 504,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_bedroom_outliers(df):\n",
    "    exclude_indices = np.array([])\n",
    "    for city_district, city_district_df in df.groupby('city_district'):\n",
    "        total_bedrooms_stats = {}\n",
    "        for total_bedrooms, total_bedrooms_df in city_district_df.groupby('total_bedrooms'):\n",
    "            total_bedrooms_stats[total_bedrooms] = {\n",
    "                'mean': np.mean(total_bedrooms_df.price_per_sqft),\n",
    "                'std': np.std(total_bedrooms_df.price_per_sqft),\n",
    "                'count': total_bedrooms_df.shape[0]\n",
    "            }\n",
    "        for total_bedrooms, total_bedrooms_df in city_district_df.groupby('total_bedrooms'):\n",
    "            stats = total_bedrooms_stats.get(total_bedrooms-1)\n",
    "            if stats and stats['count']>5:\n",
    "                exclude_indices = np.append(exclude_indices, total_bedrooms_df[total_bedrooms_df.price_per_sqft<(stats['mean'])].index.values)\n",
    "    return df.drop(exclude_indices,axis='index')\n",
    "df7 = remove_bedroom_outliers(df6)\n",
    "df7.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P7ncVAAAAM8eGPgAAAHQmXAIAANCZcAkAANAHg5cMZvCSwX6X8aARLgEAAOhMuNwP3X333TnllFPypCc9KU94whPy5je/ecpxg4ODmfwYlX3R9f0AAMDCYbfYWTIx3T28drjzuR7ykIfkM5/5TJYuXZrt27dn9erVeeELX5inPe1pnc8NAADMnN5lsFd+58oHtD0YeaFfzFzuh0opWdo+KHP79u3Zvn17SilTjv3rv/7rPOMZz8gJJ5yQq666KkkyNjaWV77ylXnqU5+aJz/5yfnoRz+aJLnrrrty5pln5sQTT8zQ0FDuuuuuJMmOHTuydu3anHDCCXniE5+Yiy66aBY+JQAAsD8xc7mf2rFjR57ylKdkZGQk55xzTk499dQpx42NjeXf/u3f8rnPfS6vfOUrc+211+b3f//387znPS8XX3xxtm7dmlNOOSXPf/7z8573vCcPfehD85WvfCVf+cpXcvLJJydJNm7cmO9+97u59tprkyRbt26dtc8JAADzSe/M5IO5unEuEC5n0ExOeS9evDgbN27M1q1b85KXvCTXXnttTjjhhAeMe9nLXpYkefazn50777wzW7duzac+9al87GMfy5/8yZ8kae7hvPHGG/O5z30u/+N//I8kyYknnpgTTzwxSfLYxz42N9xwQ37t134tL3rRi3LaaadNu24AAGB+six2P3fYYYdlcHAw//iP/zhl/+TlsqWU1Frz4Q9/OBs3bszGjRtz44035rjjjptyfJIcfvjh+fKXv5zBwcG8853vzKte9aoH/4MAAAD7NTOXM2imprxvvfXWHHjggTnssMNy11135Z/+6Z/yhje8Ycqx69aty3Of+9ysX78+hx56aA499NC84AUvyDve8Y684x3vSCkl11xzTZ785Cfn2c9+dj7wgQ/kuc99bq699tp85StfSZL84Ac/yEEHHZQzzjgjj3vc47J27drOnwEAABa6+bIcdoJwuR+6+eabc9ZZZ2XHjh0ZHx/PS1/60vzMz/zMlGMPP/zwPOMZz8idd96Ziy++OEny27/92zn33HNz4oknptaaY445Jh//+Mfzq7/6q3nFK16RE088MSeddFJOOeWUJMl3v/vdvOIVr8j4+HiS5IILLpidDwoAAOw3hMv90Iknnphrrrlmj+OGh4enbD/44IPznve8Z8r2yy+/fMr3fOlLX9qnGgEAgIVFuJwl823KGwAAoJcNfQAAAOhMuJymWmu/S5hT/DwAAGBhEy6nYcmSJbntttsEqlatNbfddluWLFnS71IAAIA+cc/lNBx11FHZvHlzbr311n6XMmcsWbIkRx11VL/LAAAA+kS4nIYDDzwwj3nMY/pdBgAAwJxhWSwAAACdCZcAAAB0JlwCAADQmXAJAABAZ8IlAAAAnQmXAAAAdCZcAgAA0JlwCQAAQGfCJQAAAJ0JlwAAAHQmXAIAANCZcAkAAEBnwiUAAACdCZcAAAB0JlwCAADQmXAJAABAZ8IlAAAAnQmXAAAAdCZcAgAA0JlwCQAAQGfCJQAAAJ0JlwAAAHQmXAIAANCZcAkAAEBnwiUAAACdCZcAAAB0JlwCAADQmXAJAABAZ8IlAAAAnc1YuCylLCmlXFVK+XIp5WullP+vbX9MKeULpZRNpZR1pZSD2vaHtMcjbf8xPed6Y9t+fSnlBT3tp7dtI6WU83va9/kaAAAATN9Mzlzek+R5tdYnJTkpyemllKcl+cMkF9VaVyb5YZKz2/FnJ/lhrXVFkovacSmlHJ/kzCRPSHJ6kneVUhaXUhYneWeSFyY5PsnL2rHZ12sAAADQzYyFy9rY1h4e2H7VJM9L8qG2/dIkP9t+/+L2OG3/T5VSStt+ea31nlrrt5KMJDml/Rqptd5Qa703yeVJXty+Z1+vAQAAQAczes9lO8O4Mcn3k3w6yTeTbK213tcO2Zxkefv98iQ3JUnbf0eSI3rbJ71nV+1HTOMak+t+dSllQyllw6233jq9Dw8AALCAzGi4rLXuqLWelOSoNDONx001rH2dagaxPojtu7vG/RtqfW+tdVWtddWRRx45xVsAAADoNSu7xdZatyYZTvK0JIeVUg5ou45KsqX9fnOSo5Ok7T80ye297ZPes6v2H0zjGgAAAHQwk7vFHllKOaz9/uAkz0/yjSSfTfLz7bCzkny0/f5j7XHa/s/UWmvbfma70+tjkqxMclWSLyZZ2e4Me1CaTX8+1r5nX68BAABABwfseci0PTLJpe2urouSfLDW+vFSyteTXF5KeUuSa5K8rx3/viTvL6WMpJlNPDNJaq1fK6V8MMnXk9yX5Jxa644kKaW8NsknkyxOcnGt9Wvtud6wL9cAAACgm2LibvdWrVpVN2zY0O8yAAAA+qKUcnWtddWexs3KPZcAAADMb8IlAAAAnQmXAAAAdCZcAgAA0JlwCQAAQGfCJQAAAJ0JlwAAAHQmXAIAANCZcAkAAEBnwiUAAACdCZcAAAB0JlwCAADQmXAJAABAZ8IlAAAAnQmXAAAAdCZcAgAA0JlwCQAAQGfCJQAAAJ0JlwAAAHQmXAIAANCZcAkAAEBnwiUAAACdCZcAAAB0JlwCAADQmXAJAABAZ8IlAAAAnQmXAAAAdCZcAgAA0JlwCQAAQGfCJQAAAJ0JlwAAAHQmXAIAANCZcAkAAEBnwiUAAACdCZcAAAB0JlwCAADQmXAJAABAZ8IlAAAAnQmXAAAAdCZcAgAA0JlwCQAAQGfCJQAAAJ0JlwAAAHQmXAIAANCZcAkAAEBnwiUAAACdCZcAAAB0JlwCAADQmXAJAABAZ8IlAAAAnQmXAAAAdCZcAgAA0JlwCQAAQGfCJQAAAJ0JlwAAAHQmXAIAANCZcAkAAEBnwiUAAACdCZcAAAB0JlwCAADQmXAJAABAZ8IlAAAAnQmXAAAAdCZcAgAA0JlwCQAAQGfCJQAAAJ0JlwAAAHQmXAIAANCZcAkAAEBnwiUAAACdCZcAAAB0JlwCAADQmXAJAABAZ8IlAAAAnQmXAAAAdCZcAgAA0JlwCQAAQGfCJQAAAJ0JlwAAAHQmXAIAANCZcAkAAEBnwiUAAACdzVi4LKUcXUr5bCnlG6WUr5VSfr1t/51SyndLKRvbrzU973ljKWWklHJ9KeUFPe2nt20jpZTze9ofU0r5QillUyllXSnloLb9Ie3xSNt/zJ6uAQAAwPTN5MzlfUleV2s9LsnTkpxTSjm+7buo1npS+3VFkrR9ZyZ5QpLTk7yrlLK4lLI4yTuTvDDJ8Ule1nOeP2zPtTLJD5Oc3bafneSHtdYVSS5qx+3yGjP3IwAAAFgYZixc1lpvrrV+qf1+NMk3kizfzVtenOTyWus9tdZvJRlJckr7NVJrvaHWem+Sy5O8uJRSkjwvyYfa91+a5Gd7znVp+/2HkvxUO35X1wAAAKCDWbnnsl2W+uQkX2ibXltK+Uop5eJSyuFt2/IkN/W8bXPbtqv2I5JsrbXeN6n9fudq++9ox+/qXJPrfXUpZUMpZcOtt966z58XAABgoZnxcFlKWZrkw0nOrbXemeTdSR6X5KQkNyd568TQKd5ep9E+nXPdv6HW99ZaV9VaVx155JFTvAUAAIBeMxouSykHpgmWH6i1/n2S1FpvqbXuqLWOJ/nf2bksdXOSo3veflSSLbtp/0GSw0opB0xqv9+52v5Dk9y+m3MBAADQwUzuFluSvC/JN2qtf9rT/sieYS9Jcm37/ceSnNnu9PqYJCuTXJXki0lWtjvDHpRmQ56P1Vprks8m+fn2/Wcl+WjPuc5qv//5JJ9px+/qGgAAAHRwwJ6HTNszk/zXJF8tpWxs234rzW6vJ6VZjvrtJP89SWqtXyulfDDJ19PsNHtOrXVHkpRSXpvkk0kWJ7m41vq19nxvSHJ5KeUtSa5JE2bTvr6/lDKSZsbyzD1dAwAAgOkrzYQeu7Jq1aq6YcOGfpcBAADQF6WUq2utq/Y0blZ2iwUAAGB+Ey4BAADoTLgEAACgM+ESAACAzoRLAAAAOhMuAQAA6Ey4BAAAoDPhEgAAgM6ESwAAADoTLgEAAOhMuAQAAKAz4RIAAIDOhEsAAAA6Ey4BAADoTLgEAACgM+ESAACAzoRLAAAAOhMuAQAA6Ey4BAAAoDPhEgAAgM6ESwAAADoTLgEAAOhMuAQAAKAz4RIAAIDOhEsAAAA6Ey4BAADoTLgEAACgM+ESAACAzoRLAAAAOhMuAQAA6Ey4BAAAoDPhEgAAgM6ESwAAADoTLgEAAOhMuAQAAKAz4RIAAIDOhEsAAAA6Ey4BAADoTLgEANiNwUsGM3jJYL/LAJjzhEsAAAA6Ey4BAADo7IB+FwAAMNf0LoO98jtXPqBteO3w7BYEsB8wcwkAAEBnZi4BACbpnZmcmLE0Wwmwe2YuAQAA6Ey4BACAecqjdJhNlsUCAOyG5bAAe8fMJQAAAJ2ZuQQAgHnEo3ToFzOXAAAAdGbmEgAA5hGP0qFfzFwCAOyG3TYB9o5wCQAAQGeWxQIAwDxlOSyzSbgEAJjEbpsA+86yWAAAADozcwkAMIndNgH2nZlLAAAAOhMuAQAA6MyyWACA3bAcFmDvmLkEAACgM+ESAACAzoRLAAAAOnPPJQAAzEOjo8m6dcmmTcnKlcnQULJsWb+rYj4TLgEAYJ5Zvz5ZsyYZH0/GxpKBgeS885IrrkhWr+53dcxXlsUCAMA8MjraBMvR0SZYJs3rRPu2bf2tj/lLuAQAgHlk3bpmxnIq4+NNP8wE4RIAAOaRTZt2zlhONjaWjIzMbj0sHMIlAADMIytXNvdYTmVgIFmxYnbrYeEQLgEAYB4ZGkoW7eJf+YsWNf0wE4RLAACYR5Yta3aFXbZs5wzmwMDO9qVL+1sf85dHkQAAwDyzenWyZUuzec/ISLMUdmhIsGRmCZcAADAPLV2anH12v6tgIbEsFgAAgM7MXALAPDM62iyF27Sp2TVyaKi51woAZpJwCQDzyPr1yZo1zYPSx8aaTTzOO6/ZxGP16n5XB8B8ZlksAMwTo6NNsBwd3fkA9bGxne3btvW3PgDmN+ESAOaJdeuaGcupjI83/QAwU4RLAJgnNm3aOWM52dhY8zgCAJgpwiUAzBNHH737/qOOmp06AFiYhEsAAAA6Ey4BYJ646abd92/ePDt1zDeDlwxm8JLBfpcBMOfNWLgspRxdSvlsKeUbpZSvlVJ+vW3/iVLKp0spm9rXw9v2Ukr5s1LKSCnlK6WUk3vOdVY7flMp5aye9qeUUr7avufPSillutcAgP3dypXNo0emMjCQrFgxu/UAsLDM5MzlfUleV2s9LsnTkpxTSjk+yflJ/rnWujLJP7fHSfLCJCvbr1cneXfSBMUkb05yapJTkrx5Iiy2Y17d877T2/Z9ugYAzAdDQ8miXfzNvmhR0w8AM+WAmTpxrfXmJDe334+WUr6RZHmSFycZbIddmmQ4yRva9stqrTXJv5dSDiulPLId++la6+1JUkr5dJLTSynDSQ6ptX6+bb8syc8m+cS+XqOtFQD2a8uWJVdc0TzTcny82SF2YKAJlldckSxd2u8K9x+9y2Cv/M6VD2gbXjs8uwUB7AdmLFz2KqUck+TJSb6Q5D9NhLla682llIe3w5Yn6b1bZHPbtrv2zVO0ZxrXuF+4LKW8Os3MZh796Efv24cFgD5avTrZsqV5puXISLMUdmhIsARg5s14uCylLE3y4STn1lrvbG+LnHLoFG11Gu27LWdv3lNrfW+S9ybJqlWr9nROAJhTli5Nzj6731Xs33pnJidmLM1WAuzejO4WW0o5ME2w/ECt9e/b5lva5a5pX7/ftm9O0vuErqOSbNlD+1FTtE/nGgAAAHQwk7vFliTvS/KNWuuf9nR9LMnEjq9nJfloT/vL2x1dn5bkjnZp6yeTnFZKObzdyOe0JJ9s+0ZLKU9rr/XySefal2sAAADQwUwui31mkv+a5KullI1t228luTDJB0spZye5MckvtH1XJFmTZCTJj5K8IklqrbeXUn4vyRfbcb87sblPkl9NckmSg9Ns5POJtn2frgEAsCuWwwLsndJsnMqurFq1qm7YsKHfZQDAXhsdbTb02bSpefbl0FCzkywATEcp5epa66o9jZuV3WIBgNmxfv0DH0Vy3nm4OiR2AAAgAElEQVTNo0hWr+53dQDMZzO6oQ8AMHtGR5tgOTraBMukeZ1o37atv/UBML/tU7gspQzMVCEAQDfr1jUzllMZH2/6AWCm7FW4LKU8o5Ty9STfaI+fVEp514xWBgDsk02bds5YTjY2loyMzG49ACwseztzeVGSFyS5LUlqrV9O8uyZKgoA2HcrVzb3WE5lYCBZsWJ26wFgYdnrZbG11psmNe14kGsBADoYGkoW7eJv9kWLmn4AmCl7Gy5vKqU8I0ktpRxUSnl92iWyAMDcsGxZsyvssmU7ZzAHBna2L13a3/oAmN/29lEkv5Lk7UmWJ9mc5FNJzpmpogCA6Vm9Otmypdm8Z2SkWQo7NCRYAjDz9ipc1lp/kOSXZrgWAOBBsHRpcvbZ/a4CgIVmb3eLvbSUcljP8eGllItnriwAAAD2J3t7z+WJtdatEwe11h8mefLMlAQAAMD+Zm/D5aJSyuETB6WUn8je368JAADAPLe3AfGtSf6tlPKh9vgXkvz+zJQEAADA/mZvN/S5rJSyIcnzkpQkP1dr/fqMVgYAAMB+Y7fhspRySK31znYZ7PeS/E1P30/UWm+f6QIBAACY+/Y0c/k3SX4mydVJak97aY8fO0N1AQAAsB/Zbbistf5MKaUkeU6t9cZZqgkAAID9zB53i6211iT/MAu1AAAAsJ/a20eR/Hsp5akzWgkAAAD7rb19FMlzk/xKKeXbScbS3nNZaz1xpgoDAABg/7G34fKFM1oFAAAA+7U9PYpkSZJfSbIiyVeTvK/Wet9sFAYAAMD+Y0/3XF6aZFWaYPnCJG+d8YoAAADY7+xpWezxtdYnJkkp5X1Jrpr5kgAAANjf7GnmcvvEN5bDAgAAsCt7mrl8Uinlzvb7kuTg9nhit9hDZrQ6AAAA9gu7DZe11sWzVQgAAAD7r719FAkAwIIzOpqsW5ds2pSsXJkMDSXLlvW7KoC5SbgEAJjC+vXJmjXJ+HgyNpYMDCTnnZdccUWyenW/qwOYe/a0oQ8AwIIzOtoEy9HRJlgmzetE+7Zt/a0PYC4SLgEAJlm3rpmxnMr4eNMPwP0JlwAAk2zatHPGcrKxsWRkZHbrAdgfCJcAAJOsXNncYzmVgYFkxYrZrQdgfyBcAgBMMjSULNrFv5IWLWr6Abg/4RIAYJJly5pdYZct2zmDOTCws33p0v7WBzAXeRQJAMAUVq9OtmxpNu8ZGWmWwg4NCZYAuyJcAgDswtKlyfsXDybHJhesHe53OQBzmmWxAAAAdCZcAgAA0JllsQAAkwxeMpgk2bEjWb/5yiTJsRcM5uEPTxYvToYtkQV4ADOXAABTuOOO5POf33k8MtIc33FH/2oCmMvMXAIATPJ/zhjO8uXJjtEkaweTJOOXDCdJvrks2Xa2XWMBJjNzCQAwybp1yfj41H3j400/APcnXAIATLJpUzI2NnXf2FizRBaA+7MsFgBgkpUrk4GBNmC2y2EnDAwkK1b0pSyAOc3MJQDAJENDyaJd/Ctp0aKmH4D7Ey4BACZZtiy58MKp+y680GY+AFMRLgEAJhkdTc4/f+q+889Ptm2b3XoA9gfCJQDAJPfbLXbt4I8fR5LYLRZgV2zoAwAwyf12i330+vv12S0WYGpmLgEAJpnYLXYqdosFmJqZSwCASS4tg7n7zCQ7kiza0TS2S2PvXpwMDQ33qTKAuUu4BACY5PPfXZ96dJLxnsZ2eWxdZLdYgKkIlwAAkyw9qEmPtSZ33ntHkuTgxUtz4IFJKf2sDGDuEi4BmDMGLxlMkgyvHe5rHbD1/K0//v6A323+ufSjN23d1XAAYkMfAAAAHgRmLgEAdmNiiSwAuydcAtBXE0thk+TK71z5gDZLZOm33iWyAOyaZbEAAAB0ZuYSgL7qnZm0oQ8A7L/MXAIAANCZcAkA89DgJYP3u3cVAGaaZbEAzBmWwwLA/svMJQAAAJ2ZuQSAecJjXQDoJzOXAAAAdGbmEgDmCY91AaCfzFwCAADQmZlLAJiHNn5vY79LAGCBES4BYB466REn9bsEABYYy2IBAADozMwlAMwTHkUCQD+ZuQQAAKAzM5cAME94FAkA/WTmEgAAgM6ESwAAADqzLBYA5iHLYQGYbWYuAQAA6Ey4BAAAoDPhEgAAgM6ESwAAADoTLgEAAOhsxsJlKeXiUsr3SynX9rT9Tinlu6WUje3Xmp6+N5ZSRkop15dSXtDTfnrbNlJKOb+n/TGllC+UUjaVUtaVUg5q2x/SHo+0/cfs6RoAAAB0M5Mzl5ckOX2K9otqrSe1X1ckSSnl+CRnJnlC+553lVIWl1IWJ3lnkhcmOT7Jy9qxSfKH7blWJvlhkrPb9rOT/LDWuiLJRe24XV7jQf7MAAAAC9KMhcta6+eS3L6Xw1+c5PJa6z211m8lGUlySvs1Umu9odZ6b5LLk7y4lFKSPC/Jh9r3X5rkZ3vOdWn7/YeS/FQ7flfXAAAAoKN+3HP52lLKV9pls4e3bcuT3NQzZnPbtqv2I5JsrbXeN6n9fudq++9ox+/qXA9QSnl1KWVDKWXDrbfeOr1PCQAAsIDMdrh8d5LHJTkpyc1J3tq2lynG1mm0T+dcD2ys9b211lW11lVHHnnkVEMAAADoMavhstZ6S611R611PMn/zs5lqZuTHN0z9KgkW3bT/oMkh5VSDpjUfr9ztf2Hplmeu6tzAQAA0NGshstSyiN7Dl+SZGIn2Y8lObPd6fUxSVYmuSrJF5OsbHeGPSjNhjwfq7XWJJ9N8vPt+89K8tGec53Vfv/zST7Tjt/VNQAAAOjogD0PmZ5Syt8mGUzysFLK5iRvTjJYSjkpzXLUbyf570lSa/1aKeWDSb6e5L4k59Rad7TneW2STyZZnOTiWuvX2ku8IcnlpZS3JLkmyfva9vcleX8pZSTNjOWZe7oGAAAA3ZRmUo9dWbVqVd2wYUO/ywAAAOiLUsrVtdZVexrXj91iAQAAmGeESwAAADoTLgEAAOhMuAQAAKAz4RIAAIDOZuxRJACwL0ZHk3Xrkk2bkpUrk6GhZNmyflcFAOwt4RKAvlu/PlmzJhkfT8bGkoGB5LzzkiuuSFav7nd1AMDesCwWgL4aHW2C5ehoEyyT5nWifdu2/tYHAOwd4RKAvlq3rpmxnMr4eNMPAMx9wiUAfbVp084Zy6wdbL5aY2PJyEg/qgIA9pVwCUBfrVzZ3GM5lYGBZMWK2a0HAJge4RKAvhoaShbt4m+jRYuafgBg7rNbLAB99V8+PJjH/V7y1a8mO46+Mkmy6JWDKSV53BOTpUuH+1ofALB3hEsA+u7QQ5OnPz1Zv7k5XrEiefjDk8WL+1sXALD3hEsA+mp47fCPvz/swsOSJNefPzz1YABgznLPJQAAAJ0JlwAAAHRmWSwAfTV4yeCPv7/jnjse0Na7bBYAmLvMXAIAANCZmUsA+mqqDX3MVgLA/sfMJQAAAJ0JlwAAAHRmWSwAfWVDHwCYH8xcAgAA0JmZSwD6yoY+ADA/CJcA9JVlsQAwP1gWCwAAQGdmLgHoq96ZyYkZS7OVALD/MXMJAABAZ8IlAAAAnVkWC8CcYTksAOy/hEsA5oTR0WTdumTTpmTlymRoKFm2rN9VAQB7S7gEoO/Wr0/WrEnGx5OxsWRgIDnvvOSKK5LVq/tdHQCwN9xzCdDR4CWD93suI/tmdLQJlqOjTbBMmteJ9m3b+lsfALB3hEsA+mrdumbGcirj400/ADD3CZcA9NWmTTtnLCcbG0tGRma3HgBgetxzCTANvctgr/zOlQ9os+vp3lu5srnHcqqAOTCQrFgx+zUBAPvOzCUAfTU0lCzaxd9GixY1/QDA3GfmEmAaemcmJ2YszVZOz7Jlza6wk3eLXbSoaV+6tN8VAgB7Q7gEoO9Wr062bGk27xkZaZbCDg0JlgCwPxEuAZgTli5Nzj6731UAANMlXAJ0ZDksAIBwCdDJ6GizlHPTpmbX06Gh5h5CAICFRrgEmKb165MXvjC5997m66CDkv/5P5NPfKK5hxAAYCHxKBKAaRgdTU47Ldm2Lbn3FweTtYO5997meKIdAGAhES4BpuHSS5O77pq67667mn4AgIVEuASYho9/fPf9//f/zk4dAABzhXsugc5GR5NVfz6Yu+5K3vTo4QWxqc1Vxw8mj2wPjrmyeV07uHPALcOzWxAAQJ+ZuQQ6Wb8+Wb68efD9TTcl557bHK9f3+/KZtYRR+y+/0Uvmp06AADmCjOXwLSNjiZr1jSvGW/axsaa1zVrki1bkqVL+1bejLryFcNZvrw9mJixvGT4x/1nXDDbFQEA9JdwCUzbqj8fzLZfSBMsJy0N3bYoWbduOGef3a/qZtYVVyRLliR33/3AviVLmnsu5+tnBwCYimWxwLSN/Sip41P31fHk61+f3Xpm06ZNUwfLpGkfGZndegAA+s3MJTBtP3XjcC67rD2YYmnobWfNdkWz5+ijd99/1FGzUwcAwFxh5hKYtsMP333/nja9AQBg/jBzCUzbCSfs/r7D44+f/Zpmy0039Rw8+oFb427ePHu1AADMBcIlMG1DQ8l557Xhsmc5bJIceGDTP1995LDBLHplMj6eZNGOprFdGrxoUbJixXC/SgMA6AvLYoFpW7as2TV12bJkYKBpGxjY2T5fH0OSJA9/eFLK1H2lzO9gDQAwFTOXQCerVzfPs1y3rtkhdcWKJljN52CZJF+9dWOWHLPzuZ5JkkdsTJIsGZj/nx8AYDLhEuhs6dKF+UzHAw5IDjkkufPe5vjgg5vlwLua0QQAmM+ES6Cz0dFm5nLTpmTlymbmctmyflc1s7aev/XH3x/wu81/Sn/0pq27Gg4AMO8Jl0An69cna9Y0G9uMjTX3XJ53XnPP5erV/a5u5o2OJgdlacbHk7/8y4URrAEApmJDH2DaRkebYDk6uvPew7Gxne3btvW3vpm2fn2yfHly113JPfck557bHK9/4JNJAADmPeESmLZ169pHcUxhfLzpn696g3Uu3JpcuHVBBWsAgMmES2DaNm2atFtqj7GxZvfY+ep+wXrt4I+fcZnM/2ANADAV4RKYtpUrdz7fcrKBgeaxJPPVQg7WAABTES6BaRsaShbt4r8iixY1/fPVkUd26wcAmG/sFgtM27Jlza6wk3eLXbSoaV+6tN8Vzpw/vnUwWdseHHNl89qzNPbqq4dntyAAgD4zcwn7gcMuPCyHXXhYv8uY0urVyfXXJ2eckZx6avN6/fXz/zEkd921+/4bbpidOgAA5gozl0An69cnp5/ePIrjvvuSq69OPvzh5B//cX4HzJfcMZzLLmsPJmYsLxn+cf+xZ812RQAA/WXmEpi20dHk+c9vlsPe98uDydrB3Hdfc/z858/vx3FccMHu+y+8cHbqAACYK8xcwhzVuwz2jnvueEDb1vO3znpNk73nPc2M5VTuuSd573uT886b3Zpmy6Melbzznck55zyw753vTB7xiNmvCQCgn8xcAtN2ySW77/+rv5qVMvrmNa9Jbr45OasO52nXDeess5rj17ym35UBAMw+M5cwR/XOTE7MWM6F2cpeI88aTJ7aHkyxY+qPPjc8yxXNvkc8Ys8hGwBgITBzCUzbsmW771+1anbqAACg/8xcAtP2hocP5zf+uD04v70ftGfH1FPfOuslAQDQJ8Il7Afm2nLYCbfe2q0fAID5w7JYYNpWrkwGBqbuGxhIVqyY3XoAAOgfM5fAtF1aBnP3mUl2JFnSPC5lYkOfuxcnQ0PDfaoMAIDZZuYSmLbFi5NHPnLqvkc+Mlm6dHbrAQCgf8xcAtP2N6cNZ/ny9mDiESTthj6bk3zvnOZRHQAAzH9mLoFpe+Mbd99//vmzUwcAAP0nXALTdt11u++//vrZqQMAgP6bsXBZSrm4lPL9Usq1PW0/UUr5dCllU/t6eNteSil/VkoZKaV8pZRycs97zmrHbyqlnNXT/pRSylfb9/xZKaVM9xrA9Pzn/9xzcMnw/Z5xmSTHHjub1QAA0E8zOXN5SZLTJ7Wdn+Sfa60rk/xze5wkL0yysv16dZJ3J01QTPLmJKcmOSXJmyfCYjvm1T3vO3061wCm74ILeg7WDu6877J14YWzWQ0AAP00Y+Gy1vq5JLdPan5xkkvb7y9N8rM97ZfVxr8nOayU8sgkL0jy6Vrr7bXWHyb5dJLT275Daq2fr7XWJJdNOte+XAOYpkc9KnnnO6fue+c7beYDALCQzPY9l/+p1npzkrSvD2/blye5qWfc5rZtd+2bp2ifzjUeoJTy6lLKhlLKhltvvXWfPiAsNK95TbJpU3LIIclBByVPf3pz/JrX9LsyAABm01x5FEmZoq1Oo30613hgY63vTfLeJFm1atWezgszasuWZlfW665r7nG84IJmxnAuGLxkMHfckXz1q8mOo69MknzhvsH85wuTJz4xuebXh/tbIAAAs2a2Zy5vmViK2r5+v23fnOTonnFHJdmyh/ajpmifzjVgznrXu5Lly5PLLkuuuqp5Xb68aZ8Lduxog+WOnW3j4zvbt23rX20AAMyu2Q6XH0sysePrWUk+2tP+8nZH16cluaNd0vrJJKeVUg5vN/I5Lckn277RUsrT2l1iXz7pXPtyDZiTtmxJzjln6r5zzkm+973ZrWcqZ9XhLLl8uNkldnxx89XuGrvk8uGsW9fnAgEAmDUztiy2lPK3SQaTPKyUsjnNrq8XJvlgKeXsJDcm+YV2+BVJ1iQZSfKjJK9Iklrr7aWU30vyxXbc79ZaJzYJ+tU0O9IenOQT7Vf29RosbIOXDCZJhtcO97WOqbz+9bvvf93rkg98YHZq2ZXXbj4s9/xae7Conb48/7AkyViSkZGtfakLAIDZN2Phstb6sl10/dQUY2uSKedoaq0XJ7l4ivYNSU6Yov22fb0GzEWf//zu+//932enjt0pU93J3OOoo3bfDwDA/DFXNvQBJjn44N33L1kyO3Xszh8fsTW/NjFz+ab2PycX9sxW/vmslwQAQJ8Ilyw4E0thk+TK71z5gLa5skT2CU9IvvGN9mDtYPN6yfCP+094wLz97Lvg5sFkbXswsSx2otYk1103PLsFAQDQN7O9oQ+wl5ZP+RTWnebCktM97Qb7pS/NTh0AAPSfmUsWnN6Zybm8oc8JJzRLX++++4F9S5Ykxx8/+zVNtvJfh3P11e1Bu5FP7+zq9qfOekkAAPSJcAlz1KVlMPf+YpLxJMc0y3cnlpzeuygZGhruU2U7bX7+YPLE9mDJHc1rz7LY4+vwLFcEAEC/WBbLgjU6mtx8c3LDDclf/mVzPNeM131rn20/+ZO77//t356dOgAA6D8zlyxI69cna9Yk4+PDGRtLzh1IzjsvueKKZPXqflfXePEPh7P+r9qDKTb0ee8JTc399N8OHM7Gv0nuvTcPqPGgg5Lh1cnjHtev6gAAmE1mLllwRkebYDk6moyNNW1jYzvb97RJzWx53/u69c+GTZvaYDmFe+9NRkZmtx4AAPpHuGTBWbcu2b596r7t25v+ueDWW3sOHrGx+erx/e/Pbj1TWbkyGRiYum9gIFmxYnbrAQCgf4RLFpxrr516B9akaf/612e3nl15+MN7Dr53UvO1q/4+GRpKFk38V+SS4fst2120qOkHAGBhEC5ZcPY04/e9781OHXvyi7+4+/5f/uXZqWN3li1r7lNdtmznDObAwM72pUv7Wx//f3v3HyxXWR5w/PskVwIkAUIJJCQUAtzCIEMxXDFo0DjV8EMr+ItonRIUJghYFQanoXYqg+0YtbZTO0HFkQJW8OJYhBEEgtOrZSqUAEGIBHPBACE/+A2XRBKSvP3jnJtsLrt7783uPWf35vuZ2dmz73N291nenF2e+77nPZIkScVxQR/tdtaubSxelB9snQPn5A8GXIok01NkOjXNng1r1mTTiXt7s6mw8+ZZWEqSJO1uLC612xlsZHL9+mLyGMz6QUZYf/hDuOyyYnIZzIQJcO65ZWchSZKkMllcarcTUXYGQ7N3dw9/fCF/UOVSJM8dUHRGkiRJUm0WlxoRfX3ZNMmVK7MVRefNy87DawXTpsGjj9aPt4J994UX+ovLASvF9sclSZKkVmFxqaa7++7sepHbtmXXjxw/Hi65JFvgZfbssrODM86Au+6qH28FtS6XMtS4JEmSVCRXi1VT9fVlhWVfX1ZYQnbf3/7aa+XmB/CRj9SPf/SjxeQxmJdeqnhQ5VIkO8UlSZKkkjlyqabq7s5GLKvZti2Ll73wy223wZ57Vr/W5Z57wq23lp8jwB/nzYGt+YMqq8WO/VlPwRlJkiRJtTlyqaZauXLHiOVAGzZkl6oo28qVFYXlOXN2Kthef701coQd142s5a1vLSYPSZIkaSgcuVRTdXbWHxU88sjicxqosxM6OmDLljfHOjpaI0eADzzXww035A+qrBZ76F8VnZEkSZJUmyOXaqrTT69eWELW/oEPFJtPNTNnVi8sIWt/+9uLzaeWAw+sH58ypZg8JEmSpKFw5FJN1Q7nM865Zg6ckz+oci7jggU93HNPsTlVs9MIapVLkRx+eHG5SJIkSYOxuFRT7XQ+4wCtcj7jxo314ytWFJPHYF55peLBgJViAV59tbhcJEmSpMFYXKqpDjmkfnz69GLyqGfyrT2sW5c/qHIu495Ti86ouu3nW9Zw/fVw2WXF5CJJkiQNxuJSu50TTsim59bS1VVcLvX0njwH+s//rDJ9d+OvewrOSJIkSarNBX3UVE8/XT++enUxedSz03TTXYgXZd996sdnzSomD0mSJGkoHLlUU3V2ZtdnrHaty/HjW+MyH088UT/eCueFAjz4hR6mTcsfVJm++621RWckSZIk1ebIpZpq3jwYU+Nf1ZgxWbxsHYP8SWWPPYrJYzAHHwyLF1ePLV7spUgkSZLUWiwu1VQTJ8J551WPnXceTJhQbD7VnHhiY/EiXXghrF0LBx0EE/eB+fOzxxdeWHZmkiRJ0s4ipVR2Di2tq6srLV26tOw02saaNeyYylnF2rXlj7hNvWzOjtVi+xfKWfWe7fHzx/Xw3e8Wn5ckSZLUiiLi/pTSoMteOnKpphrs0hgLFxaTRz3r19ePX311MXlIkiRJo4kL+qipVqyoH3/ssWLyqCf9R8+OB1UWynmjyGQkSZKkUcKRSzXV0UfXjx91VDF5DNmUZdlNkiRJUkMsLtVUX/ta/fiiRcXkIUmSJKlYFpdqqoMPhosvrh67+OLyF/N5k3XHZzdJkiRJDXG12EG4Wuzw9PXB5MmwadObY+PGwfPPl385kvj0nB0PqqwWyzU9eFhIkiRJGVeLVSm+973qhSVk7VddVWw+uyKi7AwkSZKk9uPI5SAcuRyeY4+F5cvrxx9+uLh8qhkzhh0jkwv3y+4Xvbw9HgHbthWflyRJktSKHLlUKfr66sdfe62YPOoZ7O8p/r1FkiRJGj6LSzXV/vvXj0+aVEwekiRJkorVUXYCGl1efrl+/JVXismjnjGfmbNj2uueeULnzNke3/vGnoIzkiRJktqfxWWb6euD7m5YuRI6O2HePJg4seysdnjmmfrx1auLyaMRYxzPlyRJkobN4rKN3H03nH56ttjMhg0wfjxccgncdhvMnl12dpktW+rH33ijmDzqiWt7YGv+oH/E8pqe7fHXPSokSZKkYXOMpk309WWFZV9fVlhCdt/f3goL5UB7LJYzblxjcUmSJElvZnHZJrq7a18eY9u2LK6hef/768fnzi0mD0mSJGk0cQJgm1i5cseI5UAbNkBvb7H5tLMrr4Sbb84fVEyHrYxLkiRJGh5HLttEZ2d2jmU148fDkUcWm087O/hgWLy4emzxYpgypdh8JEmSpNEgUiucBNfCurq60tKlS8tOg74+mDYtux9o4kRYswYmTCg+r4EiBt+nVf7JrVsHCxfCY4/BUUfBokUWlpIkSdJAEXF/SqlrsP2cFtsmJk7Mip+LLnpzbNGi1igs282UKXDNNWVnIUmSJI0OTottE3192ShbNQsXts5qsZIkSZJ2TxaXbaK7GzZtqh7btMnVYiVJkiSVy+KyTTz4IGzeXD22eTMsW1ZsPpIkSZJUyeKyTTz4YP34Aw8Uk4ckSZIkVWNx2SZqjVr2e+ONYvKQJEmSpGosLtvE9OmNxSVJkiRpJFlctoknn6wfX7WqkDQkSZIkqSqLyzbxxBONxSVJkiRpJFlctom99mosLkmSJEkjyeKyTUyd2lhckiRJkkaSxWWbeP31+vFNm4rJQ5IkSZKqsbhsE089VT8+2II/kiRJkjSSLC7bxMaN9eMbNhSThyRJkiRVY3Gpppo8uX78wAOLyUOSJElSsSwu1VS3314/vmRJMXlIkiRJKpbFpZpq5kw466zqsbPOguOOKzYfSZIkScWwuFTTdXfDQw/BEUfA+PHZ/UMPZe2SJEmSRqeOshPQ0IwZA9u21Y6PHVtcLkNx3HHQ21t2FpIkSZKK4shlm0ipfrxe4SlJkiRJI83isk0MNjLZ4Ri0JEmSpBJZXLaJPfZoLC5JkiRJI8nisk1s3lw/vmlTMXlIkiRJUjUWl21i69bG4pIkSZI0kiwu28RgC/oMFpckSZKkkWRx2SYiGotLkiRJ0kiyuJQkSZIkNczisk047VWSJElSK7O4bBODXedyjD0pSZIkqUSWJG3i0EPrx2fMKCYPSZIkSarG4rJNXHBBY3FJkiRJGkkWl23i/PNh3LjqsXHjYMGCYvORJEmSpEoWl21i4kS46y4YPx46OrK2jo7s8V13wYQJ5eYnSZIkaffWUXYCGrrZs2HdOujuht5eOPJImDfPwlKSJElS+UopLiNiFdAHbAW2pJS6ImJ/oBs4DFgFnJVSeikiAvg34HRgI3BOSumB/HXmA3+fv+w/ppSuzdtPAK4B9gJuA76QUkq13mOEP25TTZgA555bdhaSJEmStLMyp8W+N6V0fEqpK3+8EPhlSqkT+GX+GOA0oDO/LaGEYrUAAAr6SURBVAC+A5AXil8B3gGcCHwlIiblz/lOvm//804d5D0kSZIkSQ1opXMuzwCuzbevBc6saL8uZe4B9ouIqcApwJKU0ov56OMS4NQ8tk9K6TcppQRcN+C1qr2HJEmSJKkBZRWXCbgzIu6PiP51Tg9KKa0FyO8PzNunAU9XPHd13lavfXWV9nrvsZOIWBARSyNi6XPPPbeLH1GSJEmSdh9lLejzrpTSmog4EFgSESvq7BtV2tIutA9ZSukq4CqArq6uYT1XkiRJknZHpYxcppTW5PfPAjeRnTO5Pp/SSn7/bL77auCQiqdPB9YM0j69Sjt13kOSJEmS1IDCi8uIGB8RE/u3gbnAI8AtwPx8t/nAzfn2LcDZkZkFvJJPab0DmBsRk/KFfOYCd+SxvoiYla80e/aA16r2HpIkSZKkBpQxLfYg4Kas7qMDuD6ldHtE3AfcGBHnAk8BH8/3v43sMiS9ZJci+TRASunFiPgqcF++3xUppRfz7QvYcSmSX+Q3gEU13kOSJEmS1IDIFlRVLV1dXWnp0qVlpyFJkiRJpYiI+ysuIVlTK12KRJIkSZLUpiwuJUmSJEkNs7iUJEmSJDXM4lKSJEmS1DCLS0mSJElSwywuJUmSJEkNs7iUJEmSJDXM4lKSJEmS1DCLS0mSJElSwywuJUmSJEkNs7iUJEmSJDUsUkpl59DSIuI54MkGX+YA4PkmpKORYf+0Nvuntdk/rc3+aW32T2uzf1qb/VOsQ1NKkwfbyeKyABGxNKXUVXYeqs7+aW32T2uzf1qb/dPa7J/WZv+0NvunNTktVpIkSZLUMItLSZIkSVLDLC6LcVXZCagu+6e12T+tzf5pbfZPa7N/Wpv909rsnxbkOZeSJEmSpIY5cilJkiRJapjFpSRJkiSpYRaXTRIRqyLi4YhYFhFL87b9I2JJRKzM7yfl7RER346I3oj4bUTMLDf70S0ijsr7pf/2akR8MSIuj4hnKtpPr3jOZXn/PBYRp5SZ/2gUEVdHxLMR8UhF27CPl4iYn++/MiLml/FZRqMa/fPNiFiR98FNEbFf3n5YRPyx4jj6bsVzTsi/F3vzPowyPs9oUqNvhv1dFhGn5m29EbGw6M8xWtXon+6KvlkVEcvydo+dgkXEIRHx3xHxaEQsj4gv5O3+/rSAOv3j7087SSl5a8INWAUcMKDtG8DCfHsh8PV8+3TgF0AAs4B7y85/d7kBY4F1wKHA5cClVfY5BngIGAfMAB4Hxpad+2i6Ae8GZgKPVLQN63gB9geeyO8n5duTyv5so+FWo3/mAh359tcr+uewyv0GvM7/ASflffcL4LSyP1u732r0zbC+y/Lb48DhwB75PseU/dlGw61a/wyIfwv4h3zbY6f4/pkKzMy3JwK/z48Tf39a4Fanf/z9aaObI5cj6wzg2nz7WuDMivbrUuYeYL+ImFpGgruhvwAeTyk9WWefM4Afp5Q2pZT+APQCJxaS3W4ipfRr4MUBzcM9Xk4BlqSUXkwpvQQsAU4d+exHv2r9k1K6M6W0JX94DzC93mvkfbRPSuk3Kfulv44dfapdVOPYqaXWd9mJQG9K6YmU0mbgx/m+alC9/slHTs4Cbqj3Gh47IyeltDal9EC+3Qc8CkzD35+WUKt//P1pLxaXzZOAOyPi/ohYkLcdlFJaC9kBAxyYt08Dnq547uq8TSPvE+z8w/65fJrF1f3TYLB/yjLc48V+Ks9nyP4S3G9GRDwYEb+KiJPztmlkfdLP/hlZw/ku89gpx8nA+pTSyoo2j52SRMRhwNuAe/H3p+UM6J9K/v60OIvL5nlXSmkmcBpwUUS8u86+1eZ9e02YERYRewAfAn6SN30HOAI4HlhLNl0J7J9WU6s/7KcSRMSXgS3Aj/KmtcCfppTeBlwCXB8R+2D/FGm432X2TTk+yc5/3PTYKUlETAB+CnwxpfRqvV2rtHkMjbBa/ePvT3uwuGySlNKa/P5Z4CayaUfr+6e75vfP5ruvBg6pePp0YE1x2e62TgMeSCmtB0gprU8pbU0pbQO+z46pr/ZPOYZ7vNhPBcsXrfgg8Kl8qhH5lMsX8u37yc7l+zOy/qmcumT/jJBd+C7z2ClYRHQAHwG6+9s8dsoREW8hK1x+lFL6r7zZ358WUaN//P1pIxaXTRAR4yNiYv822YnHjwC3AP0riM0Hbs63bwHOzlchmwW80j8dQyNqp78aDzjP9cNkfQZZ/3wiIsZFxAygk+zEcI2s4R4vdwBzI2JSPg1wbt6mERARpwJ/C3wopbSxon1yRIzNtw8nO16eyPuoLyJm5eeanc2OPlUT7cJ32X1AZ0TMyGd0fCLfVyPnfcCKlNL2qXoeO8XL/3v+AHg0pfQvFSF/f1pArf7x96e9dJSdwChxEHBTvspxB3B9Sun2iLgPuDEizgWeAj6e738b2QpkvcBG4NPFp7x7iYi9gfcD51c0fyMijiebKrGqP5ZSWh4RNwK/I5t+cVFKaWuxGY9uEXEDMAc4ICJWA18BFjGM4yWl9GJEfJXsf5QBrkgpDXWhE9VRo38uI1t1dEn+XXdPSumzZKtjXhERW4CtwGcr+uEC4BpgL7JzZCrPk9EuqNE3c4b7XRYRnyP7n+GxwNUppeUFf5RRqVr/pJR+wJvP9wePnTK8C/hr4OHILwkD/B3+/rSKWv3zbfz9aRuRjyxLkiRJkrTLnBYrSZIkSWqYxaUkSZIkqWEWl5IkSZKkhllcSpIkSZIaZnEpSZIkSWqYxaUkScMQEV+OiOUR8duIWBYR7yg7J4CIuDwinslzWhYRi3bxdc6MiGOanZ8kafTzOpeSJA1RRJwEfBCYmVLaFBEHAHuM8HuOHca1dv81pfTPDb7lmcDPya6PKUnSkDlyKUnS0E0Fnk8pbQJIKT2fUloDEBGnRsSKiLg7Ir4dET/P2y+PiEv7XyAiHomIw/Ltn0XE/flI6IKKfV6LiCsi4l7gpIg4ISJ+le97R0RMHWrCtZ4bEUdExO15+/9ExNER8U7gQ8A389HPIxr9DyZJ2n1YXEqSNHR3AodExO8j4sqIeA9AROwJfB/4S+BkYMoQX+8zKaUTgC7g8xHxJ3n7eOCRlNI7gHuBfwc+lu97NfBPNV7v4oppsadExFvqPPcq4G/y9kuBK1NK/wvcAnwppXR8SunxIX4OSZKcFitJ0lCllF6LiBPICsj3At0RsRBYBvwhpbQSICL+E1hQ+5W2+3xEfDjfPgToBF4AtgI/zduPAo4FlkQEwFhgbY3X22labEQcW+25ETEBeCfwk7wdYNwQ8pUkqSaLS0mShiE//7EH6ImIh4H5ZMVlqvGULew8U2hPgIiYA7wPOCmltDEievpjwOsV51kGsDyldNIupFv1uRGxD/BySun4XXhNSZKqclqsJElDFBFHRURnRdPxwJPACmBGxTmKn6zYZxUwM3/+TGBG3r4v8FJeWB4NzKrxto8Bk/PFhIiIt0TEW4eYctXnppReBf4QER/P2yMi/jx/Th8wcYivL0nSdhaXkiQN3QTg2oj4XUT8FjgGuDyl9DrZNNhbI+JusoKz30+B/SNiGXAB8Pu8/XagI3+drwL3VHvDlNJm4GPA1yPiIbJR0ncOJdlBnvsp4Ny8fTlwRt7+Y+BLEfGgC/pIkoYjUqo1i0eSJO2KfMrrpSmlD5adiyRJRXHkUpIkSZLUMEcuJUmSJEkNc+RSkiRJktQwi0tJkiRJUsMsLiVJkiRJDbO4lCRJkiQ1zOJSkiRJktSw/wdVhI3Vs92eFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_scatter_chart(df,city_district):\n",
    "    total_bedrooms_2 = df[(df.city_district == city_district) & (df.total_bedrooms == 2)]\n",
    "    total_bedrooms_3 = df[(df.city_district == city_district) & (df.total_bedrooms == 3)]\n",
    "    matplotlib.rcParams['figure.figsize'] = (15,10)\n",
    "    plt.scatter(total_bedrooms_2.sqft,total_bedrooms_2.final_price,color = 'blue',label = '2 beds', s = 50)\n",
    "    plt.scatter(total_bedrooms_3.sqft,total_bedrooms_3.final_price,marker = '+',color = 'green',label = '3 beds', s = 50)\n",
    "    plt.xlabel(\"Square Feet\")\n",
    "    plt.ylabel(\"Price\")\n",
    "    plt.title(city_district)\n",
    "    plt.legend()\n",
    "plot_scatter_chart(df7,\"Waterfront Communities-The Island\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 506,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] = (20,10)\n",
    "plt.hist(df7.price_per_sqft, rwidth = 0.8)\n",
    "plt.xlabel(\"Price Per Square Feet\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  2,  5,  4,  1,  6, 14,  7,  8,  9, 10], dtype=int64)"
      ]
     },
     "execution_count": 507,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7.bathrooms.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "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>index</th>\n",
       "      <th>final_price</th>\n",
       "      <th>list_price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft</th>\n",
       "      <th>type</th>\n",
       "      <th>city_district</th>\n",
       "      <th>bedrooms_ag</th>\n",
       "      <th>bedrooms_bg</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>9479</td>\n",
       "      <td>3380000</td>\n",
       "      <td>2999000</td>\n",
       "      <td>9 + 2 beds</td>\n",
       "      <td>14</td>\n",
       "      <td>4250.0</td>\n",
       "      <td>Detached</td>\n",
       "      <td>Annex</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>795.294118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  final_price  list_price    bedrooms  bathrooms    sqft      type  \\\n",
       "208   9479      3380000     2999000  9 + 2 beds         14  4250.0  Detached   \n",
       "\n",
       "    city_district  bedrooms_ag  bedrooms_bg  total_bedrooms  price_per_sqft  \n",
       "208         Annex            9            2              11      795.294118  "
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7[df7.bathrooms > 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 509,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df7.bathrooms, rwidth = 0.8)\n",
    "plt.xlabel(\"Bathrooms\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "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>index</th>\n",
       "      <th>final_price</th>\n",
       "      <th>list_price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft</th>\n",
       "      <th>type</th>\n",
       "      <th>city_district</th>\n",
       "      <th>bedrooms_ag</th>\n",
       "      <th>bedrooms_bg</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>9479</td>\n",
       "      <td>3380000</td>\n",
       "      <td>2999000</td>\n",
       "      <td>9 + 2 beds</td>\n",
       "      <td>14</td>\n",
       "      <td>4250.0</td>\n",
       "      <td>Detached</td>\n",
       "      <td>Annex</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>795.294118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>13886</td>\n",
       "      <td>2610000</td>\n",
       "      <td>2790000</td>\n",
       "      <td>4 + 1 beds</td>\n",
       "      <td>7</td>\n",
       "      <td>4250.0</td>\n",
       "      <td>Detached</td>\n",
       "      <td>Bayview Woods-Steeles</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>614.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2047</th>\n",
       "      <td>11666</td>\n",
       "      <td>2600000</td>\n",
       "      <td>2688800</td>\n",
       "      <td>4 + 1 beds</td>\n",
       "      <td>7</td>\n",
       "      <td>4250.0</td>\n",
       "      <td>Detached</td>\n",
       "      <td>Englemount-Lawrence</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>611.764706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3382</th>\n",
       "      <td>16705</td>\n",
       "      <td>4200000</td>\n",
       "      <td>4295000</td>\n",
       "      <td>4 + 1 beds</td>\n",
       "      <td>7</td>\n",
       "      <td>4250.0</td>\n",
       "      <td>Detached</td>\n",
       "      <td>Lawrence Park South</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>988.235294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3407</th>\n",
       "      <td>12628</td>\n",
       "      <td>3500000</td>\n",
       "      <td>2995000</td>\n",
       "      <td>4 + 1 beds</td>\n",
       "      <td>7</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>Detached</td>\n",
       "      <td>Leaside-Bennington</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1076.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7875</th>\n",
       "      <td>1015</td>\n",
       "      <td>3100000</td>\n",
       "      <td>2500000</td>\n",
       "      <td>4 + 1 beds</td>\n",
       "      <td>7</td>\n",
       "      <td>4250.0</td>\n",
       "      <td>Detached</td>\n",
       "      <td>Willowdale West</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>729.411765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7938</th>\n",
       "      <td>14069</td>\n",
       "      <td>3000000</td>\n",
       "      <td>3288000</td>\n",
       "      <td>4 + 1 beds</td>\n",
       "      <td>7</td>\n",
       "      <td>4250.0</td>\n",
       "      <td>Detached</td>\n",
       "      <td>Willowdale West</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>705.882353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  final_price  list_price    bedrooms  bathrooms    sqft      type  \\\n",
       "208    9479      3380000     2999000  9 + 2 beds         14  4250.0  Detached   \n",
       "804   13886      2610000     2790000  4 + 1 beds          7  4250.0  Detached   \n",
       "2047  11666      2600000     2688800  4 + 1 beds          7  4250.0  Detached   \n",
       "3382  16705      4200000     4295000  4 + 1 beds          7  4250.0  Detached   \n",
       "3407  12628      3500000     2995000  4 + 1 beds          7  3250.0  Detached   \n",
       "7875   1015      3100000     2500000  4 + 1 beds          7  4250.0  Detached   \n",
       "7938  14069      3000000     3288000  4 + 1 beds          7  4250.0  Detached   \n",
       "\n",
       "              city_district  bedrooms_ag  bedrooms_bg  total_bedrooms  \\\n",
       "208                   Annex            9            2              11   \n",
       "804   Bayview Woods-Steeles            4            1               5   \n",
       "2047    Englemount-Lawrence            4            1               5   \n",
       "3382    Lawrence Park South            4            1               5   \n",
       "3407     Leaside-Bennington            4            1               5   \n",
       "7875        Willowdale West            4            1               5   \n",
       "7938        Willowdale West            4            1               5   \n",
       "\n",
       "      price_per_sqft  \n",
       "208       795.294118  \n",
       "804       614.117647  \n",
       "2047      611.764706  \n",
       "3382      988.235294  \n",
       "3407     1076.923077  \n",
       "7875      729.411765  \n",
       "7938      705.882353  "
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7[df7.bathrooms > df7.total_bedrooms+1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4836, 12)"
      ]
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8 = df7[df7.bathrooms < df7.total_bedrooms+1]\n",
    "df8.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   final_price  list_price  bathrooms    sqft             type  \\\n",
       "0       634000      639000          3  1300.0  Condo Townhouse   \n",
       "1       453000      448000          2   950.0        Condo Apt   \n",
       "3       798000      768000          3  1750.0         Detached   \n",
       "6       890000      789000          5  1750.0         Detached   \n",
       "7       818000      838000          4  1750.0         Detached   \n",
       "\n",
       "     city_district  total_bedrooms  price_per_sqft  \n",
       "0  Agincourt North               3      487.692308  \n",
       "1  Agincourt North               2      476.842105  \n",
       "3  Agincourt North               5      456.000000  \n",
       "6  Agincourt North               6      508.571429  \n",
       "7  Agincourt North               5      467.428571  "
      ]
     },
     "execution_count": 512,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9 = df8.drop(['bedrooms_ag','bedrooms_bg','bedrooms','index'],axis='columns')\n",
    "df9.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [
    {
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       "   Agincourt North  Agincourt South-Malvern West  Alderwood  Annex  \\\n",
       "0                1                             0          0      0   \n",
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       "0                        0                   0  \n",
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       "3                        0                   0  \n",
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     "execution_count": 513,
     "metadata": {},
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   ],
   "source": [
    "dummies = pd.get_dummies(df9.city_district)\n",
    "dummies.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [
    {
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       "<p>3 rows × 138 columns</p>\n",
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      "text/plain": [
       "   final_price  list_price  bathrooms    sqft             type  \\\n",
       "0       634000      639000          3  1300.0  Condo Townhouse   \n",
       "1       453000      448000          2   950.0        Condo Apt   \n",
       "3       798000      768000          3  1750.0         Detached   \n",
       "\n",
       "     city_district  total_bedrooms  price_per_sqft  Agincourt North  \\\n",
       "0  Agincourt North               3      487.692308                1   \n",
       "1  Agincourt North               2      476.842105                1   \n",
       "3  Agincourt North               5      456.000000                1   \n",
       "\n",
       "   Agincourt South-Malvern West  ...  Willowdale East  Willowdale West  \\\n",
       "0                             0  ...                0                0   \n",
       "1                             0  ...                0                0   \n",
       "3                             0  ...                0                0   \n",
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       "0                                 0       0                  0         0   \n",
       "1                                 0       0                  0         0   \n",
       "3                                 0       0                  0         0   \n",
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       "   Yonge-Eglinton  Yonge-St.Clair  York University Heights  Yorkdale-Glen Park  \n",
       "0               0               0                        0                   0  \n",
       "1               0               0                        0                   0  \n",
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     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10 = pd.concat([df9,dummies.drop('Other', axis = 'columns')], axis = \"columns\")\n",
    "df10.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [
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       "      <td>Condo Apt</td>\n",
       "      <td>2</td>\n",
       "      <td>476.842105</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 137 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   final_price  list_price  bathrooms    sqft             type  \\\n",
       "0       634000      639000          3  1300.0  Condo Townhouse   \n",
       "1       453000      448000          2   950.0        Condo Apt   \n",
       "\n",
       "   total_bedrooms  price_per_sqft  Agincourt North  \\\n",
       "0               3      487.692308                1   \n",
       "1               2      476.842105                1   \n",
       "\n",
       "   Agincourt South-Malvern West  Alderwood  ...  Willowdale East  \\\n",
       "0                             0          0  ...                0   \n",
       "1                             0          0  ...                0   \n",
       "\n",
       "   Willowdale West  Willowridge-Martingrove-Richview  Woburn  \\\n",
       "0                0                                 0       0   \n",
       "1                0                                 0       0   \n",
       "\n",
       "   Woodbine Corridor  Wychwood  Yonge-Eglinton  Yonge-St.Clair  \\\n",
       "0                  0         0               0               0   \n",
       "1                  0         0               0               0   \n",
       "\n",
       "   York University Heights  Yorkdale-Glen Park  \n",
       "0                        0                   0  \n",
       "1                        0                   0  \n",
       "\n",
       "[2 rows x 137 columns]"
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11 = df10.drop('city_district',axis = 'columns')\n",
    "df11.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4836, 137)"
      ]
     },
     "execution_count": 516,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {},
   "outputs": [
    {
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       "   list_price  bathrooms    sqft             type  total_bedrooms  \\\n",
       "0      639000          3  1300.0  Condo Townhouse               3   \n",
       "1      448000          2   950.0        Condo Apt               2   \n",
       "3      768000          3  1750.0         Detached               5   \n",
       "6      789000          5  1750.0         Detached               6   \n",
       "7      838000          4  1750.0         Detached               5   \n",
       "\n",
       "   price_per_sqft  Agincourt North  Agincourt South-Malvern West  Alderwood  \\\n",
       "0      487.692308                1                             0          0   \n",
       "1      476.842105                1                             0          0   \n",
       "3      456.000000                1                             0          0   \n",
       "6      508.571429                1                             0          0   \n",
       "7      467.428571                1                             0          0   \n",
       "\n",
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       "0      0  ...                0                0   \n",
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       "3      0  ...                0                0   \n",
       "6      0  ...                0                0   \n",
       "7      0  ...                0                0   \n",
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       "   Willowridge-Martingrove-Richview  Woburn  Woodbine Corridor  Wychwood  \\\n",
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       "6                                 0       0                  0         0   \n",
       "7                                 0       0                  0         0   \n",
       "\n",
       "   Yonge-Eglinton  Yonge-St.Clair  York University Heights  Yorkdale-Glen Park  \n",
       "0               0               0                        0                   0  \n",
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       "3               0               0                        0                   0  \n",
       "6               0               0                        0                   0  \n",
       "7               0               0                        0                   0  \n",
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     },
     "execution_count": 517,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df11.drop('final_price',axis = 'columns')\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.drop('type',axis = 'columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>618880</td>\n",
       "      <td>2</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>4</td>\n",
       "      <td>473.076923</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>279000</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2</td>\n",
       "      <td>429.230769</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>585000</td>\n",
       "      <td>3</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>4</td>\n",
       "      <td>453.846154</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>848888</td>\n",
       "      <td>3</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>4</td>\n",
       "      <td>477.142857</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>399800</td>\n",
       "      <td>2</td>\n",
       "      <td>850.0</td>\n",
       "      <td>2</td>\n",
       "      <td>470.352941</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>799000</td>\n",
       "      <td>4</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>5</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>345000</td>\n",
       "      <td>1</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2</td>\n",
       "      <td>453.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>405000</td>\n",
       "      <td>1</td>\n",
       "      <td>750.0</td>\n",
       "      <td>3</td>\n",
       "      <td>516.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>849000</td>\n",
       "      <td>4</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>6</td>\n",
       "      <td>462.857143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>409900</td>\n",
       "      <td>2</td>\n",
       "      <td>850.0</td>\n",
       "      <td>2</td>\n",
       "      <td>470.588235</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>419999</td>\n",
       "      <td>2</td>\n",
       "      <td>850.0</td>\n",
       "      <td>2</td>\n",
       "      <td>490.588235</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>465000</td>\n",
       "      <td>2</td>\n",
       "      <td>950.0</td>\n",
       "      <td>2</td>\n",
       "      <td>457.894737</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>478000</td>\n",
       "      <td>2</td>\n",
       "      <td>850.0</td>\n",
       "      <td>2</td>\n",
       "      <td>558.823529</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>497800</td>\n",
       "      <td>2</td>\n",
       "      <td>850.0</td>\n",
       "      <td>3</td>\n",
       "      <td>567.647059</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>449700</td>\n",
       "      <td>2</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2</td>\n",
       "      <td>626.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>429900</td>\n",
       "      <td>1</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2</td>\n",
       "      <td>573.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>509900</td>\n",
       "      <td>2</td>\n",
       "      <td>850.0</td>\n",
       "      <td>3</td>\n",
       "      <td>585.882353</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>458000</td>\n",
       "      <td>2</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2</td>\n",
       "      <td>672.307692</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>485000</td>\n",
       "      <td>2</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2</td>\n",
       "      <td>626.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>698000</td>\n",
       "      <td>3</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>6</td>\n",
       "      <td>433.142857</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8225</th>\n",
       "      <td>400000</td>\n",
       "      <td>1</td>\n",
       "      <td>950.0</td>\n",
       "      <td>2</td>\n",
       "      <td>412.631579</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8227</th>\n",
       "      <td>609900</td>\n",
       "      <td>2</td>\n",
       "      <td>950.0</td>\n",
       "      <td>2</td>\n",
       "      <td>628.421053</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8228</th>\n",
       "      <td>474900</td>\n",
       "      <td>1</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2</td>\n",
       "      <td>633.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8230</th>\n",
       "      <td>358800</td>\n",
       "      <td>1</td>\n",
       "      <td>950.0</td>\n",
       "      <td>2</td>\n",
       "      <td>371.578947</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8232</th>\n",
       "      <td>339000</td>\n",
       "      <td>1</td>\n",
       "      <td>950.0</td>\n",
       "      <td>2</td>\n",
       "      <td>361.052632</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8235</th>\n",
       "      <td>840000</td>\n",
       "      <td>5</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>6</td>\n",
       "      <td>363.111111</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8237</th>\n",
       "      <td>465900</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>908.909091</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8238</th>\n",
       "      <td>1199900</td>\n",
       "      <td>5</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>8</td>\n",
       "      <td>432.727273</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8239</th>\n",
       "      <td>655000</td>\n",
       "      <td>2</td>\n",
       "      <td>850.0</td>\n",
       "      <td>3</td>\n",
       "      <td>765.882353</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8242</th>\n",
       "      <td>750000</td>\n",
       "      <td>2</td>\n",
       "      <td>950.0</td>\n",
       "      <td>4</td>\n",
       "      <td>768.421053</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8244</th>\n",
       "      <td>2099900</td>\n",
       "      <td>5</td>\n",
       "      <td>4250.0</td>\n",
       "      <td>5</td>\n",
       "      <td>458.823529</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8245</th>\n",
       "      <td>469900</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2</td>\n",
       "      <td>847.272727</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8246</th>\n",
       "      <td>498000</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2</td>\n",
       "      <td>815.384615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8247</th>\n",
       "      <td>1385000</td>\n",
       "      <td>4</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>4</td>\n",
       "      <td>762.857143</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8248</th>\n",
       "      <td>424900</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>805.272727</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8251</th>\n",
       "      <td>467900</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>883.454545</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8259</th>\n",
       "      <td>670888</td>\n",
       "      <td>2</td>\n",
       "      <td>850.0</td>\n",
       "      <td>3</td>\n",
       "      <td>741.176471</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8261</th>\n",
       "      <td>669000</td>\n",
       "      <td>2</td>\n",
       "      <td>850.0</td>\n",
       "      <td>3</td>\n",
       "      <td>764.705882</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8262</th>\n",
       "      <td>1099000</td>\n",
       "      <td>2</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>6</td>\n",
       "      <td>702.857143</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8264</th>\n",
       "      <td>1175300</td>\n",
       "      <td>2</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>933.846154</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8271</th>\n",
       "      <td>455600</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>818.181818</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8272</th>\n",
       "      <td>451900</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>854.363636</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8274</th>\n",
       "      <td>399999</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>718.181818</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8275</th>\n",
       "      <td>459000</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2</td>\n",
       "      <td>816.363636</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8277</th>\n",
       "      <td>888000</td>\n",
       "      <td>3</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>6</td>\n",
       "      <td>491.428571</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8278</th>\n",
       "      <td>1338800</td>\n",
       "      <td>4</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>5</td>\n",
       "      <td>403.076923</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8280</th>\n",
       "      <td>424900</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>772.545455</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8281</th>\n",
       "      <td>469900</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>1</td>\n",
       "      <td>707.692308</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8285</th>\n",
       "      <td>439900</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>790.909091</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8286</th>\n",
       "      <td>459800</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4836 rows × 135 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      list_price  bathrooms    sqft  total_bedrooms  price_per_sqft  \\\n",
       "0         639000          3  1300.0               3      487.692308   \n",
       "1         448000          2   950.0               2      476.842105   \n",
       "3         768000          3  1750.0               5      456.000000   \n",
       "6         789000          5  1750.0               6      508.571429   \n",
       "7         838000          4  1750.0               5      467.428571   \n",
       "12        829000          5  1750.0               7      457.142857   \n",
       "13        588000          3  1300.0               5      461.538462   \n",
       "14        439000          2   950.0               2      457.894737   \n",
       "15        408800          2   850.0               2      476.470588   \n",
       "16        429999          2   950.0               2      458.947368   \n",
       "19        618880          2  1300.0               4      473.076923   \n",
       "20        279000          1   650.0               2      429.230769   \n",
       "21        585000          3  1300.0               4      453.846154   \n",
       "22        848888          3  1750.0               4      477.142857   \n",
       "24        399800          2   850.0               2      470.352941   \n",
       "28        799000          4  1750.0               5      440.000000   \n",
       "29        345000          1   750.0               2      453.333333   \n",
       "31        405000          1   750.0               3      516.000000   \n",
       "33        849000          4  1750.0               6      462.857143   \n",
       "34        409900          2   850.0               2      470.588235   \n",
       "37        419999          2   850.0               2      490.588235   \n",
       "40        465000          2   950.0               2      457.894737   \n",
       "41        478000          2   850.0               2      558.823529   \n",
       "42        497800          2   850.0               3      567.647059   \n",
       "44        449700          2   750.0               2      626.666667   \n",
       "45        429900          1   750.0               2      573.333333   \n",
       "47        509900          2   850.0               3      585.882353   \n",
       "48        458000          2   650.0               2      672.307692   \n",
       "49        485000          2   750.0               2      626.666667   \n",
       "51        698000          3  1750.0               6      433.142857   \n",
       "...          ...        ...     ...             ...             ...   \n",
       "8225      400000          1   950.0               2      412.631579   \n",
       "8227      609900          2   950.0               2      628.421053   \n",
       "8228      474900          1   750.0               2      633.333333   \n",
       "8230      358800          1   950.0               2      371.578947   \n",
       "8232      339000          1   950.0               2      361.052632   \n",
       "8235      840000          5  2250.0               6      363.111111   \n",
       "8237      465900          1   550.0               1      908.909091   \n",
       "8238     1199900          5  2750.0               8      432.727273   \n",
       "8239      655000          2   850.0               3      765.882353   \n",
       "8242      750000          2   950.0               4      768.421053   \n",
       "8244     2099900          5  4250.0               5      458.823529   \n",
       "8245      469900          1   550.0               2      847.272727   \n",
       "8246      498000          1   650.0               2      815.384615   \n",
       "8247     1385000          4  1750.0               4      762.857143   \n",
       "8248      424900          1   550.0               1      805.272727   \n",
       "8251      467900          1   550.0               1      883.454545   \n",
       "8259      670888          2   850.0               3      741.176471   \n",
       "8261      669000          2   850.0               3      764.705882   \n",
       "8262     1099000          2  1750.0               6      702.857143   \n",
       "8264     1175300          2  1300.0               5      933.846154   \n",
       "8271      455600          1   550.0               1      818.181818   \n",
       "8272      451900          1   550.0               1      854.363636   \n",
       "8274      399999          1   550.0               1      718.181818   \n",
       "8275      459000          1   550.0               2      816.363636   \n",
       "8277      888000          3  1750.0               6      491.428571   \n",
       "8278     1338800          4  3250.0               5      403.076923   \n",
       "8280      424900          1   550.0               1      772.545455   \n",
       "8281      469900          1   650.0               1      707.692308   \n",
       "8285      439900          1   550.0               1      790.909091   \n",
       "8286      459800          1   550.0               2      815.000000   \n",
       "\n",
       "      Agincourt North  Agincourt South-Malvern West  Alderwood  Annex  \\\n",
       "0                   1                             0          0      0   \n",
       "1                   1                             0          0      0   \n",
       "3                   1                             0          0      0   \n",
       "6                   1                             0          0      0   \n",
       "7                   1                             0          0      0   \n",
       "12                  1                             0          0      0   \n",
       "13                  1                             0          0      0   \n",
       "14                  1                             0          0      0   \n",
       "15                  1                             0          0      0   \n",
       "16                  1                             0          0      0   \n",
       "19                  1                             0          0      0   \n",
       "20                  1                             0          0      0   \n",
       "21                  1                             0          0      0   \n",
       "22                  1                             0          0      0   \n",
       "24                  1                             0          0      0   \n",
       "28                  1                             0          0      0   \n",
       "29                  1                             0          0      0   \n",
       "31                  1                             0          0      0   \n",
       "33                  1                             0          0      0   \n",
       "34                  1                             0          0      0   \n",
       "37                  1                             0          0      0   \n",
       "40                  0                             1          0      0   \n",
       "41                  0                             1          0      0   \n",
       "42                  0                             1          0      0   \n",
       "44                  0                             1          0      0   \n",
       "45                  0                             1          0      0   \n",
       "47                  0                             1          0      0   \n",
       "48                  0                             1          0      0   \n",
       "49                  0                             1          0      0   \n",
       "51                  0                             1          0      0   \n",
       "...               ...                           ...        ...    ...   \n",
       "8225                0                             0          0      0   \n",
       "8227                0                             0          0      0   \n",
       "8228                0                             0          0      0   \n",
       "8230                0                             0          0      0   \n",
       "8232                0                             0          0      0   \n",
       "8235                0                             0          0      0   \n",
       "8237                0                             0          0      0   \n",
       "8238                0                             0          0      0   \n",
       "8239                0                             0          0      0   \n",
       "8242                0                             0          0      0   \n",
       "8244                0                             0          0      0   \n",
       "8245                0                             0          0      0   \n",
       "8246                0                             0          0      0   \n",
       "8247                0                             0          0      0   \n",
       "8248                0                             0          0      0   \n",
       "8251                0                             0          0      0   \n",
       "8259                0                             0          0      0   \n",
       "8261                0                             0          0      0   \n",
       "8262                0                             0          0      0   \n",
       "8264                0                             0          0      0   \n",
       "8271                0                             0          0      0   \n",
       "8272                0                             0          0      0   \n",
       "8274                0                             0          0      0   \n",
       "8275                0                             0          0      0   \n",
       "8277                0                             0          0      0   \n",
       "8278                0                             0          0      0   \n",
       "8280                0                             0          0      0   \n",
       "8281                0                             0          0      0   \n",
       "8285                0                             0          0      0   \n",
       "8286                0                             0          0      0   \n",
       "\n",
       "      Banbury-Don Mills  ...  Willowdale East  Willowdale West  \\\n",
       "0                     0  ...                0                0   \n",
       "1                     0  ...                0                0   \n",
       "3                     0  ...                0                0   \n",
       "6                     0  ...                0                0   \n",
       "7                     0  ...                0                0   \n",
       "12                    0  ...                0                0   \n",
       "13                    0  ...                0                0   \n",
       "14                    0  ...                0                0   \n",
       "15                    0  ...                0                0   \n",
       "16                    0  ...                0                0   \n",
       "19                    0  ...                0                0   \n",
       "20                    0  ...                0                0   \n",
       "21                    0  ...                0                0   \n",
       "22                    0  ...                0                0   \n",
       "24                    0  ...                0                0   \n",
       "28                    0  ...                0                0   \n",
       "29                    0  ...                0                0   \n",
       "31                    0  ...                0                0   \n",
       "33                    0  ...                0                0   \n",
       "34                    0  ...                0                0   \n",
       "37                    0  ...                0                0   \n",
       "40                    0  ...                0                0   \n",
       "41                    0  ...                0                0   \n",
       "42                    0  ...                0                0   \n",
       "44                    0  ...                0                0   \n",
       "45                    0  ...                0                0   \n",
       "47                    0  ...                0                0   \n",
       "48                    0  ...                0                0   \n",
       "49                    0  ...                0                0   \n",
       "51                    0  ...                0                0   \n",
       "...                 ...  ...              ...              ...   \n",
       "8225                  0  ...                0                0   \n",
       "8227                  0  ...                0                0   \n",
       "8228                  0  ...                0                0   \n",
       "8230                  0  ...                0                0   \n",
       "8232                  0  ...                0                0   \n",
       "8235                  0  ...                0                0   \n",
       "8237                  0  ...                0                0   \n",
       "8238                  0  ...                0                0   \n",
       "8239                  0  ...                0                0   \n",
       "8242                  0  ...                0                0   \n",
       "8244                  0  ...                0                0   \n",
       "8245                  0  ...                0                0   \n",
       "8246                  0  ...                0                0   \n",
       "8247                  0  ...                0                0   \n",
       "8248                  0  ...                0                0   \n",
       "8251                  0  ...                0                0   \n",
       "8259                  0  ...                0                0   \n",
       "8261                  0  ...                0                0   \n",
       "8262                  0  ...                0                0   \n",
       "8264                  0  ...                0                0   \n",
       "8271                  0  ...                0                0   \n",
       "8272                  0  ...                0                0   \n",
       "8274                  0  ...                0                0   \n",
       "8275                  0  ...                0                0   \n",
       "8277                  0  ...                0                0   \n",
       "8278                  0  ...                0                0   \n",
       "8280                  0  ...                0                0   \n",
       "8281                  0  ...                0                0   \n",
       "8285                  0  ...                0                0   \n",
       "8286                  0  ...                0                0   \n",
       "\n",
       "      Willowridge-Martingrove-Richview  Woburn  Woodbine Corridor  Wychwood  \\\n",
       "0                                    0       0                  0         0   \n",
       "1                                    0       0                  0         0   \n",
       "3                                    0       0                  0         0   \n",
       "6                                    0       0                  0         0   \n",
       "7                                    0       0                  0         0   \n",
       "12                                   0       0                  0         0   \n",
       "13                                   0       0                  0         0   \n",
       "14                                   0       0                  0         0   \n",
       "15                                   0       0                  0         0   \n",
       "16                                   0       0                  0         0   \n",
       "19                                   0       0                  0         0   \n",
       "20                                   0       0                  0         0   \n",
       "21                                   0       0                  0         0   \n",
       "22                                   0       0                  0         0   \n",
       "24                                   0       0                  0         0   \n",
       "28                                   0       0                  0         0   \n",
       "29                                   0       0                  0         0   \n",
       "31                                   0       0                  0         0   \n",
       "33                                   0       0                  0         0   \n",
       "34                                   0       0                  0         0   \n",
       "37                                   0       0                  0         0   \n",
       "40                                   0       0                  0         0   \n",
       "41                                   0       0                  0         0   \n",
       "42                                   0       0                  0         0   \n",
       "44                                   0       0                  0         0   \n",
       "45                                   0       0                  0         0   \n",
       "47                                   0       0                  0         0   \n",
       "48                                   0       0                  0         0   \n",
       "49                                   0       0                  0         0   \n",
       "51                                   0       0                  0         0   \n",
       "...                                ...     ...                ...       ...   \n",
       "8225                                 0       0                  0         0   \n",
       "8227                                 0       0                  0         0   \n",
       "8228                                 0       0                  0         0   \n",
       "8230                                 0       0                  0         0   \n",
       "8232                                 0       0                  0         0   \n",
       "8235                                 0       0                  0         0   \n",
       "8237                                 0       0                  0         0   \n",
       "8238                                 0       0                  0         0   \n",
       "8239                                 0       0                  0         0   \n",
       "8242                                 0       0                  0         0   \n",
       "8244                                 0       0                  0         0   \n",
       "8245                                 0       0                  0         0   \n",
       "8246                                 0       0                  0         0   \n",
       "8247                                 0       0                  0         0   \n",
       "8248                                 0       0                  0         0   \n",
       "8251                                 0       0                  0         0   \n",
       "8259                                 0       0                  0         0   \n",
       "8261                                 0       0                  0         0   \n",
       "8262                                 0       0                  0         0   \n",
       "8264                                 0       0                  0         0   \n",
       "8271                                 0       0                  0         0   \n",
       "8272                                 0       0                  0         0   \n",
       "8274                                 0       0                  0         0   \n",
       "8275                                 0       0                  0         0   \n",
       "8277                                 0       0                  0         0   \n",
       "8278                                 0       0                  0         0   \n",
       "8280                                 0       0                  0         0   \n",
       "8281                                 0       0                  0         0   \n",
       "8285                                 0       0                  0         0   \n",
       "8286                                 0       0                  0         0   \n",
       "\n",
       "      Yonge-Eglinton  Yonge-St.Clair  York University Heights  \\\n",
       "0                  0               0                        0   \n",
       "1                  0               0                        0   \n",
       "3                  0               0                        0   \n",
       "6                  0               0                        0   \n",
       "7                  0               0                        0   \n",
       "12                 0               0                        0   \n",
       "13                 0               0                        0   \n",
       "14                 0               0                        0   \n",
       "15                 0               0                        0   \n",
       "16                 0               0                        0   \n",
       "19                 0               0                        0   \n",
       "20                 0               0                        0   \n",
       "21                 0               0                        0   \n",
       "22                 0               0                        0   \n",
       "24                 0               0                        0   \n",
       "28                 0               0                        0   \n",
       "29                 0               0                        0   \n",
       "31                 0               0                        0   \n",
       "33                 0               0                        0   \n",
       "34                 0               0                        0   \n",
       "37                 0               0                        0   \n",
       "40                 0               0                        0   \n",
       "41                 0               0                        0   \n",
       "42                 0               0                        0   \n",
       "44                 0               0                        0   \n",
       "45                 0               0                        0   \n",
       "47                 0               0                        0   \n",
       "48                 0               0                        0   \n",
       "49                 0               0                        0   \n",
       "51                 0               0                        0   \n",
       "...              ...             ...                      ...   \n",
       "8225               0               0                        1   \n",
       "8227               0               0                        1   \n",
       "8228               0               0                        1   \n",
       "8230               0               0                        1   \n",
       "8232               0               0                        1   \n",
       "8235               0               0                        1   \n",
       "8237               0               0                        0   \n",
       "8238               0               0                        0   \n",
       "8239               0               0                        0   \n",
       "8242               0               0                        0   \n",
       "8244               0               0                        0   \n",
       "8245               0               0                        0   \n",
       "8246               0               0                        0   \n",
       "8247               0               0                        0   \n",
       "8248               0               0                        0   \n",
       "8251               0               0                        0   \n",
       "8259               0               0                        0   \n",
       "8261               0               0                        0   \n",
       "8262               0               0                        0   \n",
       "8264               0               0                        0   \n",
       "8271               0               0                        0   \n",
       "8272               0               0                        0   \n",
       "8274               0               0                        0   \n",
       "8275               0               0                        0   \n",
       "8277               0               0                        0   \n",
       "8278               0               0                        0   \n",
       "8280               0               0                        0   \n",
       "8281               0               0                        0   \n",
       "8285               0               0                        0   \n",
       "8286               0               0                        0   \n",
       "\n",
       "      Yorkdale-Glen Park  \n",
       "0                      0  \n",
       "1                      0  \n",
       "3                      0  \n",
       "6                      0  \n",
       "7                      0  \n",
       "12                     0  \n",
       "13                     0  \n",
       "14                     0  \n",
       "15                     0  \n",
       "16                     0  \n",
       "19                     0  \n",
       "20                     0  \n",
       "21                     0  \n",
       "22                     0  \n",
       "24                     0  \n",
       "28                     0  \n",
       "29                     0  \n",
       "31                     0  \n",
       "33                     0  \n",
       "34                     0  \n",
       "37                     0  \n",
       "40                     0  \n",
       "41                     0  \n",
       "42                     0  \n",
       "44                     0  \n",
       "45                     0  \n",
       "47                     0  \n",
       "48                     0  \n",
       "49                     0  \n",
       "51                     0  \n",
       "...                  ...  \n",
       "8225                   0  \n",
       "8227                   0  \n",
       "8228                   0  \n",
       "8230                   0  \n",
       "8232                   0  \n",
       "8235                   0  \n",
       "8237                   1  \n",
       "8238                   1  \n",
       "8239                   1  \n",
       "8242                   1  \n",
       "8244                   1  \n",
       "8245                   1  \n",
       "8246                   1  \n",
       "8247                   1  \n",
       "8248                   1  \n",
       "8251                   1  \n",
       "8259                   1  \n",
       "8261                   1  \n",
       "8262                   1  \n",
       "8264                   1  \n",
       "8271                   1  \n",
       "8272                   1  \n",
       "8274                   1  \n",
       "8275                   1  \n",
       "8277                   1  \n",
       "8278                   1  \n",
       "8280                   1  \n",
       "8281                   1  \n",
       "8285                   1  \n",
       "8286                   1  \n",
       "\n",
       "[4836 rows x 135 columns]"
      ]
     },
     "execution_count": 519,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    634000\n",
       "1    453000\n",
       "3    798000\n",
       "6    890000\n",
       "7    818000\n",
       "Name: final_price, dtype: int64"
      ]
     },
     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df11.final_price\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9903583570615362"
      ]
     },
     "execution_count": 522,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr_clf = LinearRegression().fit(X_train,y_train)\n",
    "lr_clf.fit(X_train,y_train)\n",
    "lr_clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "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>list_price</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>price_per_sqft</th>\n",
       "      <th>Agincourt North</th>\n",
       "      <th>Agincourt South-Malvern West</th>\n",
       "      <th>Alderwood</th>\n",
       "      <th>Annex</th>\n",
       "      <th>Banbury-Don Mills</th>\n",
       "      <th>...</th>\n",
       "      <th>Willowdale East</th>\n",
       "      <th>Willowdale West</th>\n",
       "      <th>Willowridge-Martingrove-Richview</th>\n",
       "      <th>Woburn</th>\n",
       "      <th>Woodbine Corridor</th>\n",
       "      <th>Wychwood</th>\n",
       "      <th>Yonge-Eglinton</th>\n",
       "      <th>Yonge-St.Clair</th>\n",
       "      <th>York University Heights</th>\n",
       "      <th>Yorkdale-Glen Park</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5720</th>\n",
       "      <td>949000</td>\n",
       "      <td>2</td>\n",
       "      <td>1100.0</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>7477</th>\n",
       "      <td>605000</td>\n",
       "      <td>3</td>\n",
       "      <td>1100.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3862</th>\n",
       "      <td>549900</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2</td>\n",
       "      <td>835.384615</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5653</th>\n",
       "      <td>728000</td>\n",
       "      <td>1</td>\n",
       "      <td>950.0</td>\n",
       "      <td>1</td>\n",
       "      <td>752.631579</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>415000</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2</td>\n",
       "      <td>819.796364</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2381</th>\n",
       "      <td>998900</td>\n",
       "      <td>2</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>978.461538</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>1377000</td>\n",
       "      <td>2</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>4</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>1299900</td>\n",
       "      <td>4</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>4</td>\n",
       "      <td>691.428571</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1477</th>\n",
       "      <td>489900</td>\n",
       "      <td>2</td>\n",
       "      <td>850.0</td>\n",
       "      <td>2</td>\n",
       "      <td>556.470588</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>522000</td>\n",
       "      <td>3</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>4</td>\n",
       "      <td>401.538462</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2867</th>\n",
       "      <td>409900</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>772.727273</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8272</th>\n",
       "      <td>451900</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>854.363636</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6635</th>\n",
       "      <td>515000</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000.000000</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6142</th>\n",
       "      <td>289000</td>\n",
       "      <td>1</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1</td>\n",
       "      <td>396.000000</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6570</th>\n",
       "      <td>875900</td>\n",
       "      <td>2</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1133.333333</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7547</th>\n",
       "      <td>480000</td>\n",
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       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>836.363636</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6537</th>\n",
       "      <td>849990</td>\n",
       "      <td>2</td>\n",
       "      <td>850.0</td>\n",
       "      <td>2</td>\n",
       "      <td>985.882353</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3158</th>\n",
       "      <td>1695000</td>\n",
       "      <td>4</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>4</td>\n",
       "      <td>973.028571</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6202</th>\n",
       "      <td>440000</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>929.090909</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>425000</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>867.272727</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>499900</td>\n",
       "      <td>1</td>\n",
       "      <td>750.0</td>\n",
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       "      <td>688.000000</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>848888</td>\n",
       "      <td>3</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>4</td>\n",
       "      <td>477.142857</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3517</th>\n",
       "      <td>479000</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2</td>\n",
       "      <td>736.923077</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4442</th>\n",
       "      <td>1399000</td>\n",
       "      <td>3</td>\n",
       "      <td>2124.0</td>\n",
       "      <td>3</td>\n",
       "      <td>664.312618</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5031</th>\n",
       "      <td>469000</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>841.818182</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7708</th>\n",
       "      <td>554900</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2</td>\n",
       "      <td>846.153846</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6833</th>\n",
       "      <td>599000</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>972.727273</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4829</th>\n",
       "      <td>549800</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1018.181818</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2565</th>\n",
       "      <td>816800</td>\n",
       "      <td>3</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>5</td>\n",
       "      <td>451.428571</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2128</th>\n",
       "      <td>619500</td>\n",
       "      <td>2</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>3</td>\n",
       "      <td>457.692308</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6737</th>\n",
       "      <td>449000</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>1</td>\n",
       "      <td>832.307692</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061</th>\n",
       "      <td>739000</td>\n",
       "      <td>2</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>2</td>\n",
       "      <td>750.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2192</th>\n",
       "      <td>1339000</td>\n",
       "      <td>2</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>2</td>\n",
       "      <td>829.166667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>452000</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>818.181818</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>479000</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>929.090909</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7159</th>\n",
       "      <td>699900</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5416</th>\n",
       "      <td>485000</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2</td>\n",
       "      <td>750.769231</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1523</th>\n",
       "      <td>458800</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>1</td>\n",
       "      <td>773.846154</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>699900</td>\n",
       "      <td>2</td>\n",
       "      <td>900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>781.111111</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5929</th>\n",
       "      <td>599900</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1064.615385</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5879</th>\n",
       "      <td>491000</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>887.272727</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3588</th>\n",
       "      <td>320000</td>\n",
       "      <td>1</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1</td>\n",
       "      <td>426.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4219</th>\n",
       "      <td>725000</td>\n",
       "      <td>1</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1</td>\n",
       "      <td>966.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>449900</td>\n",
       "      <td>1</td>\n",
       "      <td>850.0</td>\n",
       "      <td>1</td>\n",
       "      <td>517.647059</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7625</th>\n",
       "      <td>818000</td>\n",
       "      <td>2</td>\n",
       "      <td>950.0</td>\n",
       "      <td>3</td>\n",
       "      <td>842.105263</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4397</th>\n",
       "      <td>419900</td>\n",
       "      <td>2</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>2</td>\n",
       "      <td>368.181818</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7088</th>\n",
       "      <td>490000</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>1</td>\n",
       "      <td>827.692308</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1698</th>\n",
       "      <td>449700</td>\n",
       "      <td>2</td>\n",
       "      <td>950.0</td>\n",
       "      <td>2</td>\n",
       "      <td>457.894737</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1616</th>\n",
       "      <td>449000</td>\n",
       "      <td>1</td>\n",
       "      <td>650.0</td>\n",
       "      <td>1</td>\n",
       "      <td>723.076923</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3453</th>\n",
       "      <td>499999</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>972.727273</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3288</th>\n",
       "      <td>460000</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>836.363636</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2409</th>\n",
       "      <td>998000</td>\n",
       "      <td>2</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>3</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8275</th>\n",
       "      <td>459000</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2</td>\n",
       "      <td>816.363636</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3390</th>\n",
       "      <td>1249000</td>\n",
       "      <td>2</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>4</td>\n",
       "      <td>960.769231</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1855</th>\n",
       "      <td>449000</td>\n",
       "      <td>2</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5947</th>\n",
       "      <td>774900</td>\n",
       "      <td>1</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2103</th>\n",
       "      <td>485000</td>\n",
       "      <td>2</td>\n",
       "      <td>850.0</td>\n",
       "      <td>2</td>\n",
       "      <td>582.352941</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7989</th>\n",
       "      <td>469900</td>\n",
       "      <td>2</td>\n",
       "      <td>850.0</td>\n",
       "      <td>2</td>\n",
       "      <td>564.705882</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>609900</td>\n",
       "      <td>1</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>2</td>\n",
       "      <td>457.692308</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3868 rows × 135 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      list_price  bathrooms    sqft  total_bedrooms  price_per_sqft  \\\n",
       "5720      949000          2  1100.0               2      844.545455   \n",
       "540       609000          1   650.0               1      923.076923   \n",
       "7477      605000          3  1100.0               3      533.636364   \n",
       "3862      549900          1   650.0               2      835.384615   \n",
       "5653      728000          1   950.0               1      752.631579   \n",
       "682       415000          1   550.0               2      819.796364   \n",
       "2381      998900          2  1300.0               5      978.461538   \n",
       "1649     1377000          2  2250.0               4      560.000000   \n",
       "1017     1299900          4  1750.0               4      691.428571   \n",
       "1477      489900          2   850.0               2      556.470588   \n",
       "997       522000          3  1300.0               4      401.538462   \n",
       "2867      409900          1   550.0               1      772.727273   \n",
       "8272      451900          1   550.0               1      854.363636   \n",
       "6635      515000          1   550.0               1     1000.000000   \n",
       "6142      289000          1   750.0               1      396.000000   \n",
       "6570      875900          2   750.0               2     1133.333333   \n",
       "7547      480000          1   550.0               1      836.363636   \n",
       "6537      849990          2   850.0               2      985.882353   \n",
       "3158     1695000          4  1750.0               4      973.028571   \n",
       "6202      440000          1   550.0               1      929.090909   \n",
       "1256      425000          1   550.0               1      867.272727   \n",
       "1908      499900          1   750.0               1      688.000000   \n",
       "22        848888          3  1750.0               4      477.142857   \n",
       "3517      479000          1   650.0               2      736.923077   \n",
       "4442     1399000          3  2124.0               3      664.312618   \n",
       "5031      469000          1   550.0               1      841.818182   \n",
       "7708      554900          1   650.0               2      846.153846   \n",
       "6833      599000          1   550.0               1      972.727273   \n",
       "4829      549800          1   550.0               1     1018.181818   \n",
       "2565      816800          3  1750.0               5      451.428571   \n",
       "...          ...        ...     ...             ...             ...   \n",
       "2128      619500          2  1300.0               3      457.692308   \n",
       "6737      449000          1   650.0               1      832.307692   \n",
       "1061      739000          2  1100.0               2      750.000000   \n",
       "2192     1339000          2  1500.0               2      829.166667   \n",
       "630       452000          1   550.0               1      818.181818   \n",
       "7040      479000          1   550.0               1      929.090909   \n",
       "7159      699900          1   650.0               2     1100.000000   \n",
       "5416      485000          1   650.0               2      750.769231   \n",
       "1523      458800          1   650.0               1      773.846154   \n",
       "953       699900          2   900.0               3      781.111111   \n",
       "5929      599900          1   650.0               2     1064.615385   \n",
       "5879      491000          1   550.0               1      887.272727   \n",
       "3588      320000          1   750.0               1      426.666667   \n",
       "4219      725000          1   750.0               1      966.666667   \n",
       "362       449900          1   850.0               1      517.647059   \n",
       "7625      818000          2   950.0               3      842.105263   \n",
       "4397      419900          2  1100.0               2      368.181818   \n",
       "7088      490000          1   650.0               1      827.692308   \n",
       "1698      449700          2   950.0               2      457.894737   \n",
       "1616      449000          1   650.0               1      723.076923   \n",
       "3453      499999          1   550.0               1      972.727273   \n",
       "3288      460000          1   550.0               1      836.363636   \n",
       "2409      998000          2  1100.0               3      850.000000   \n",
       "8275      459000          1   550.0               2      816.363636   \n",
       "3390     1249000          2  1300.0               4      960.769231   \n",
       "1855      449000          2   750.0               2      576.000000   \n",
       "5947      774900          1   750.0               1     1030.000000   \n",
       "2103      485000          2   850.0               2      582.352941   \n",
       "7989      469900          2   850.0               2      564.705882   \n",
       "2020      609900          1  1300.0               2      457.692308   \n",
       "\n",
       "      Agincourt North  Agincourt South-Malvern West  Alderwood  Annex  \\\n",
       "5720                0                             0          0      0   \n",
       "540                 0                             0          0      0   \n",
       "7477                0                             0          0      0   \n",
       "3862                0                             0          0      0   \n",
       "5653                0                             0          0      0   \n",
       "682                 0                             0          0      0   \n",
       "2381                0                             0          0      0   \n",
       "1649                0                             0          0      0   \n",
       "1017                0                             0          0      0   \n",
       "1477                0                             0          0      0   \n",
       "997                 0                             0          0      0   \n",
       "2867                0                             0          0      0   \n",
       "8272                0                             0          0      0   \n",
       "6635                0                             0          0      0   \n",
       "6142                0                             0          0      0   \n",
       "6570                0                             0          0      0   \n",
       "7547                0                             0          0      0   \n",
       "6537                0                             0          0      0   \n",
       "3158                0                             0          0      0   \n",
       "6202                0                             0          0      0   \n",
       "1256                0                             0          0      0   \n",
       "1908                0                             0          0      0   \n",
       "22                  1                             0          0      0   \n",
       "3517                0                             0          0      0   \n",
       "4442                0                             0          0      0   \n",
       "5031                0                             0          0      0   \n",
       "7708                0                             0          0      0   \n",
       "6833                0                             0          0      0   \n",
       "4829                0                             0          0      0   \n",
       "2565                0                             0          0      0   \n",
       "...               ...                           ...        ...    ...   \n",
       "2128                0                             0          0      0   \n",
       "6737                0                             0          0      0   \n",
       "1061                0                             0          0      0   \n",
       "2192                0                             0          0      0   \n",
       "630                 0                             0          0      0   \n",
       "7040                0                             0          0      0   \n",
       "7159                0                             0          0      0   \n",
       "5416                0                             0          0      0   \n",
       "1523                0                             0          0      0   \n",
       "953                 0                             0          0      0   \n",
       "5929                0                             0          0      0   \n",
       "5879                0                             0          0      0   \n",
       "3588                0                             0          0      0   \n",
       "4219                0                             0          0      0   \n",
       "362                 0                             0          0      0   \n",
       "7625                0                             0          0      0   \n",
       "4397                0                             0          0      0   \n",
       "7088                0                             0          0      0   \n",
       "1698                0                             0          0      0   \n",
       "1616                0                             0          0      0   \n",
       "3453                0                             0          0      0   \n",
       "3288                0                             0          0      0   \n",
       "2409                0                             0          0      0   \n",
       "8275                0                             0          0      0   \n",
       "3390                0                             0          0      0   \n",
       "1855                0                             0          0      0   \n",
       "5947                0                             0          0      0   \n",
       "2103                0                             0          0      0   \n",
       "7989                0                             0          0      0   \n",
       "2020                0                             0          0      0   \n",
       "\n",
       "      Banbury-Don Mills  ...  Willowdale East  Willowdale West  \\\n",
       "5720                  0  ...                0                0   \n",
       "540                   0  ...                0                0   \n",
       "7477                  0  ...                0                0   \n",
       "3862                  0  ...                0                0   \n",
       "5653                  0  ...                0                0   \n",
       "682                   0  ...                0                0   \n",
       "2381                  0  ...                0                0   \n",
       "1649                  0  ...                0                0   \n",
       "1017                  0  ...                0                0   \n",
       "1477                  0  ...                0                0   \n",
       "997                   0  ...                0                0   \n",
       "2867                  0  ...                0                0   \n",
       "8272                  0  ...                0                0   \n",
       "6635                  0  ...                0                0   \n",
       "6142                  0  ...                0                0   \n",
       "6570                  0  ...                0                0   \n",
       "7547                  0  ...                1                0   \n",
       "6537                  0  ...                0                0   \n",
       "3158                  0  ...                0                0   \n",
       "6202                  0  ...                0                0   \n",
       "1256                  0  ...                0                0   \n",
       "1908                  0  ...                0                0   \n",
       "22                    0  ...                0                0   \n",
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       "7708                  0  ...                1                0   \n",
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       "2565                  0  ...                0                0   \n",
       "...                 ...  ...              ...              ...   \n",
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       "630                   0  ...                0                0   \n",
       "7040                  0  ...                0                0   \n",
       "7159                  0  ...                0                0   \n",
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       "953                   0  ...                0                0   \n",
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       "5879                  0  ...                0                0   \n",
       "3588                  0  ...                0                0   \n",
       "4219                  0  ...                0                0   \n",
       "362                   1  ...                0                0   \n",
       "7625                  0  ...                1                0   \n",
       "4397                  0  ...                0                0   \n",
       "7088                  0  ...                0                0   \n",
       "1698                  0  ...                0                0   \n",
       "1616                  0  ...                0                0   \n",
       "3453                  0  ...                0                0   \n",
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       "2409                  0  ...                0                0   \n",
       "8275                  0  ...                0                0   \n",
       "3390                  0  ...                0                0   \n",
       "1855                  0  ...                0                0   \n",
       "5947                  0  ...                0                0   \n",
       "2103                  0  ...                0                0   \n",
       "7989                  0  ...                0                0   \n",
       "2020                  0  ...                0                0   \n",
       "\n",
       "      Willowridge-Martingrove-Richview  Woburn  Woodbine Corridor  Wychwood  \\\n",
       "5720                                 0       0                  0         0   \n",
       "540                                  0       0                  0         0   \n",
       "7477                                 0       0                  0         0   \n",
       "3862                                 0       0                  0         0   \n",
       "5653                                 0       0                  0         0   \n",
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       "1477                                 0       0                  0         0   \n",
       "997                                  0       0                  0         0   \n",
       "2867                                 0       0                  0         0   \n",
       "8272                                 0       0                  0         0   \n",
       "6635                                 0       0                  0         0   \n",
       "6142                                 0       0                  0         0   \n",
       "6570                                 0       0                  0         0   \n",
       "7547                                 0       0                  0         0   \n",
       "6537                                 0       0                  0         0   \n",
       "3158                                 0       0                  0         0   \n",
       "6202                                 0       0                  0         0   \n",
       "1256                                 0       0                  0         0   \n",
       "1908                                 0       0                  0         0   \n",
       "22                                   0       0                  0         0   \n",
       "3517                                 0       0                  0         0   \n",
       "4442                                 0       0                  0         0   \n",
       "5031                                 0       0                  0         0   \n",
       "7708                                 0       0                  0         0   \n",
       "6833                                 0       0                  0         0   \n",
       "4829                                 0       0                  0         0   \n",
       "2565                                 0       0                  0         0   \n",
       "...                                ...     ...                ...       ...   \n",
       "2128                                 0       0                  0         0   \n",
       "6737                                 0       0                  0         0   \n",
       "1061                                 0       0                  0         0   \n",
       "2192                                 0       0                  0         0   \n",
       "630                                  0       0                  0         0   \n",
       "7040                                 0       0                  0         0   \n",
       "7159                                 0       0                  0         0   \n",
       "5416                                 0       0                  0         0   \n",
       "1523                                 0       0                  0         0   \n",
       "953                                  0       0                  0         0   \n",
       "5929                                 0       0                  0         0   \n",
       "5879                                 0       0                  0         0   \n",
       "3588                                 0       0                  0         0   \n",
       "4219                                 0       0                  0         0   \n",
       "362                                  0       0                  0         0   \n",
       "7625                                 0       0                  0         0   \n",
       "4397                                 0       0                  0         0   \n",
       "7088                                 0       0                  0         0   \n",
       "1698                                 0       0                  0         0   \n",
       "1616                                 0       0                  0         0   \n",
       "3453                                 0       0                  0         0   \n",
       "3288                                 0       0                  0         0   \n",
       "2409                                 0       0                  0         0   \n",
       "8275                                 0       0                  0         0   \n",
       "3390                                 0       0                  0         0   \n",
       "1855                                 0       0                  0         0   \n",
       "5947                                 0       0                  0         0   \n",
       "2103                                 0       0                  0         0   \n",
       "7989                                 0       1                  0         0   \n",
       "2020                                 0       0                  0         0   \n",
       "\n",
       "      Yonge-Eglinton  Yonge-St.Clair  York University Heights  \\\n",
       "5720               0               0                        0   \n",
       "540                0               0                        0   \n",
       "7477               0               0                        0   \n",
       "3862               0               0                        0   \n",
       "5653               0               0                        0   \n",
       "682                0               0                        0   \n",
       "2381               0               0                        0   \n",
       "1649               0               0                        0   \n",
       "1017               0               0                        0   \n",
       "1477               0               0                        0   \n",
       "997                0               0                        0   \n",
       "2867               0               0                        0   \n",
       "8272               0               0                        0   \n",
       "6635               0               0                        0   \n",
       "6142               0               0                        0   \n",
       "6570               0               0                        0   \n",
       "7547               0               0                        0   \n",
       "6537               0               0                        0   \n",
       "3158               0               0                        0   \n",
       "6202               0               0                        0   \n",
       "1256               0               0                        0   \n",
       "1908               0               0                        0   \n",
       "22                 0               0                        0   \n",
       "3517               0               0                        0   \n",
       "4442               0               0                        0   \n",
       "5031               0               0                        0   \n",
       "7708               0               0                        0   \n",
       "6833               0               0                        0   \n",
       "4829               0               0                        0   \n",
       "2565               0               0                        0   \n",
       "...              ...             ...                      ...   \n",
       "2128               0               0                        0   \n",
       "6737               0               0                        0   \n",
       "1061               0               0                        0   \n",
       "2192               0               0                        0   \n",
       "630                0               0                        0   \n",
       "7040               0               0                        0   \n",
       "7159               0               0                        0   \n",
       "5416               0               0                        0   \n",
       "1523               0               0                        0   \n",
       "953                0               0                        0   \n",
       "5929               0               0                        0   \n",
       "5879               0               0                        0   \n",
       "3588               0               0                        0   \n",
       "4219               0               0                        0   \n",
       "362                0               0                        0   \n",
       "7625               0               0                        0   \n",
       "4397               0               0                        0   \n",
       "7088               0               0                        0   \n",
       "1698               0               0                        0   \n",
       "1616               0               0                        0   \n",
       "3453               0               0                        0   \n",
       "3288               0               0                        0   \n",
       "2409               0               0                        0   \n",
       "8275               0               0                        0   \n",
       "3390               0               0                        0   \n",
       "1855               0               0                        0   \n",
       "5947               0               0                        0   \n",
       "2103               0               0                        0   \n",
       "7989               0               0                        0   \n",
       "2020               0               0                        0   \n",
       "\n",
       "      Yorkdale-Glen Park  \n",
       "5720                   0  \n",
       "540                    0  \n",
       "7477                   0  \n",
       "3862                   0  \n",
       "5653                   0  \n",
       "682                    0  \n",
       "2381                   0  \n",
       "1649                   0  \n",
       "1017                   0  \n",
       "1477                   0  \n",
       "997                    0  \n",
       "2867                   0  \n",
       "8272                   1  \n",
       "6635                   0  \n",
       "6142                   0  \n",
       "6570                   0  \n",
       "7547                   0  \n",
       "6537                   0  \n",
       "3158                   0  \n",
       "6202                   0  \n",
       "1256                   0  \n",
       "1908                   0  \n",
       "22                     0  \n",
       "3517                   0  \n",
       "4442                   0  \n",
       "5031                   0  \n",
       "7708                   0  \n",
       "6833                   0  \n",
       "4829                   0  \n",
       "2565                   0  \n",
       "...                  ...  \n",
       "2128                   0  \n",
       "6737                   0  \n",
       "1061                   0  \n",
       "2192                   0  \n",
       "630                    0  \n",
       "7040                   0  \n",
       "7159                   0  \n",
       "5416                   0  \n",
       "1523                   0  \n",
       "953                    0  \n",
       "5929                   0  \n",
       "5879                   0  \n",
       "3588                   0  \n",
       "4219                   0  \n",
       "362                    0  \n",
       "7625                   0  \n",
       "4397                   0  \n",
       "7088                   0  \n",
       "1698                   0  \n",
       "1616                   0  \n",
       "3453                   0  \n",
       "3288                   0  \n",
       "2409                   0  \n",
       "8275                   1  \n",
       "3390                   0  \n",
       "1855                   0  \n",
       "5947                   0  \n",
       "2103                   0  \n",
       "7989                   0  \n",
       "2020                   0  \n",
       "\n",
       "[3868 rows x 135 columns]"
      ]
     },
     "execution_count": 523,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.98965225, 0.98986635, 0.9887956 , 0.98748847, 0.98943141])"
      ]
     },
     "execution_count": 524,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)\n",
    "\n",
    "cross_val_score(LinearRegression(), X, y, cv=cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Username\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "C:\\Users\\Username\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "C:\\Users\\Username\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "C:\\Users\\Username\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "C:\\Users\\Username\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "C:\\Users\\Username\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "C:\\Users\\Username\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "C:\\Users\\Username\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "C:\\Users\\Username\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "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",
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       "\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>best_score</th>\n",
       "      <th>best_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>linear_regression</td>\n",
       "      <td>0.989047</td>\n",
       "      <td>{'normalize': True}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lasso</td>\n",
       "      <td>0.989026</td>\n",
       "      <td>{'alpha': 1, 'selection': 'cyclic'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>decision_tree</td>\n",
       "      <td>0.984828</td>\n",
       "      <td>{'criterion': 'mse', 'splitter': 'best'}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model  best_score                               best_params\n",
       "0  linear_regression    0.989047                       {'normalize': True}\n",
       "1              lasso    0.989026       {'alpha': 1, 'selection': 'cyclic'}\n",
       "2      decision_tree    0.984828  {'criterion': 'mse', 'splitter': 'best'}"
      ]
     },
     "execution_count": 542,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "def find_best_model_using_gridsearchcv(X,y):\n",
    "    algos = {\n",
    "        'linear_regression' : {\n",
    "            'model': LinearRegression(),\n",
    "            'params': {\n",
    "                'normalize': [True, False]\n",
    "            }\n",
    "        },\n",
    "        'lasso': {\n",
    "            'model': Lasso(),\n",
    "            'params': {\n",
    "                'alpha': [1,2],\n",
    "                'selection': ['random', 'cyclic']\n",
    "            }\n",
    "        },\n",
    "        'decision_tree': {\n",
    "            'model': DecisionTreeRegressor(),\n",
    "            'params': {\n",
    "                'criterion' : ['mse','friedman_mse'],\n",
    "                'splitter': ['best','random']\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "    scores = []\n",
    "    cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)\n",
    "    for algo_name, config in algos.items():\n",
    "        gs =  GridSearchCV(config['model'], config['params'], cv=cv, return_train_score=False)\n",
    "        gs.fit(X,y)\n",
    "        scores.append({\n",
    "            'model': algo_name,\n",
    "            'best_score': gs.best_score_,\n",
    "            'best_params': gs.best_params_\n",
    "        })\n",
    "\n",
    "    return pd.DataFrame(scores,columns=['model','best_score','best_params'])\n",
    "\n",
    "find_best_model_using_gridsearchcv(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['list_price', 'bathrooms', 'sqft', 'total_bedrooms', 'price_per_sqft',\n",
       "       'Agincourt North', 'Agincourt South-Malvern West', 'Alderwood', 'Annex',\n",
       "       'Banbury-Don Mills',\n",
       "       ...\n",
       "       'Willowdale East', 'Willowdale West',\n",
       "       'Willowridge-Martingrove-Richview', 'Woburn', 'Woodbine Corridor',\n",
       "       'Wychwood', 'Yonge-Eglinton', 'Yonge-St.Clair',\n",
       "       'York University Heights', 'Yorkdale-Glen Park'],\n",
       "      dtype='object', length=135)"
      ]
     },
     "execution_count": 525,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 526,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(X.columns==\"Agincourt North\")[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_price(location,sqft,bath,beds):    \n",
    "    loc_index = np.where(X.columns==location)[0][0]\n",
    "\n",
    "    x = np.zeros(len(X.columns))\n",
    "    x[0] = sqft\n",
    "    x[1] = bath\n",
    "    x[2] = beds\n",
    "    if loc_index >= 0:\n",
    "        x[loc_index] = 1\n",
    "\n",
    "    return lr_clf.predict([x])[0] + 1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "867596.154843465"
      ]
     },
     "execution_count": 539,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('Niagara',1500,2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "815390.5984282542"
      ]
     },
     "execution_count": 540,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('Centennial Scarborough',1500,3,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "852176.3417888238"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('Annex',900,2,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('Toronto Home Prices.pickle','wb') as f:\n",
    "    pickle.dump(lr_clf, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "columns = {\n",
    "    'data_columns' : [col.lower() for col in X.columns]\n",
    "}\n",
    "with open(\"columns.json\",\"w\") as f:\n",
    "    f.write(json.dumps(columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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