├── .DS_Store ├── code ├── .DS_Store ├── prob_dist.png ├── image_getter.py ├── load_transform.py ├── pipeline │ ├── get_schools.py │ ├── walkscore_api.py │ └── web_scraping.py ├── pre_processing.py └── learn_preferences.py ├── data ├── .DS_Store ├── algorithm.png ├── seattle │ ├── .DS_Store │ ├── ballard-seattle-WA.csv │ ├── fremont-seattle-WA.csv │ ├── wallingford-seattle-WA.csv │ ├── magnolia-seattle-WA.csv │ ├── capitol-hill-seattle-WA.csv │ ├── lake-union-seattle-WA.csv │ ├── mercer-island-WA.csv │ ├── des-moines-WA.csv │ ├── burien-WA.csv │ ├── issaquah-WA.csv │ ├── redmond-WA.csv │ ├── kirkland-WA.csv │ └── bellevue-WA.csv └── seattle_schools.txt └── README.md /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MichaelAHood/real_estate_recommender/HEAD/.DS_Store -------------------------------------------------------------------------------- /code/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MichaelAHood/real_estate_recommender/HEAD/code/.DS_Store -------------------------------------------------------------------------------- /data/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MichaelAHood/real_estate_recommender/HEAD/data/.DS_Store -------------------------------------------------------------------------------- /code/prob_dist.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MichaelAHood/real_estate_recommender/HEAD/code/prob_dist.png -------------------------------------------------------------------------------- /data/algorithm.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MichaelAHood/real_estate_recommender/HEAD/data/algorithm.png -------------------------------------------------------------------------------- /data/seattle/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MichaelAHood/real_estate_recommender/HEAD/data/seattle/.DS_Store -------------------------------------------------------------------------------- /code/image_getter.py: -------------------------------------------------------------------------------- 1 | import requests 2 | 3 | class GetImage(object): 4 | """ 5 | This class is to assist in quickly fetching image urls for a 6 | Zillow listing. 7 | 8 | """ 9 | def __init__(self): 10 | self.df = None 11 | 12 | def build_link(self, row): 13 | """ 14 | Input: a row of a dataframe 15 | Output: a url to the Zillow listing 16 | """ 17 | BASE_URL = "http://www.zillow.com/homedetails/{0} {1} {2}/{3}_zpid" 18 | url = BASE_URL.format(row[5], row[1], row[4], str(row[60])) 19 | return url.replace(' ', '-').replace('#', 'UNIT') 20 | 21 | def find_image(self, html): 22 | end_jpg = html.find('.jpg') + 5 23 | block = html[end_jpg - 100 : end_jpg] 24 | start_jpg = block.find('href=') + 6 25 | return block[start_jpg : len(block) -1 ] 26 | 27 | def get_image(self, row): 28 | """ 29 | Input: a url as a string 30 | Output: the url link to an image of the house 31 | """ 32 | url = self.build_link(row) 33 | params = { 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_3)'} 34 | response = requests.get(url, headers=params) 35 | return self.find_image(response.content) -------------------------------------------------------------------------------- /code/load_transform.py: -------------------------------------------------------------------------------- 1 | from pymongo import MongoClient 2 | import pandas as pd 3 | import pprint as pprint 4 | import collections 5 | from itertools import izip 6 | import math 7 | import numpy as np 8 | 9 | class LoadTransform(object): 10 | 11 | """ 12 | Creates an object to easily load and tranform the data into a pandas dataframe. 13 | 14 | Example usage: 15 | 16 | from load_transform import LoadTransform 17 | lt = LoadTransform() 18 | lt.load_data({"address.state": "WA"}) 19 | lt.transform_data() 20 | 21 | """ 22 | 23 | 24 | def __init__(self): 25 | self.client = MongoClient() 26 | self.db = self.client.updated_proj 27 | self.cursor = None 28 | self.df = None 29 | self.example_query = 'self.load_data({"address.state": "WA"})' 30 | 31 | def load_data(self, mongo_query): 32 | 33 | self.cursor = self.db.listings.find(mongo_query) 34 | 35 | 36 | def transform_data(self): 37 | # taken from Stackoverflow discussion at: 38 | # http://stackoverflow.com/questions/6027558/flatten-nested-python-dictionaries-compressing-keys 39 | def flatten(d, parent_key='', sep='_'): 40 | items = [] 41 | for k, v in d.items(): 42 | new_key = parent_key + sep + k if parent_key else k 43 | if isinstance(v, collections.MutableMapping): 44 | items.extend(flatten(v, new_key, sep=sep).items()) 45 | else: 46 | items.append((new_key, v)) 47 | return dict(items) 48 | 49 | def rename_columns(df): 50 | df.columns = [col.replace("editedfacts_", '') for col in df.columns] 51 | df.columns = [col.replace("address_", '') for col in df.columns] 52 | df.columns = [col.replace("posting_", '') for col in df.columns] 53 | return df 54 | 55 | flattened = [flatten(document) for document in self.cursor] 56 | self.df = pd.DataFrame(flattened, index=range(len(flattened))) 57 | self.df = rename_columns(self.df) 58 | self.df = self.df.drop_duplicates('zpid') # drop any duplicates 59 | 60 | 61 | -------------------------------------------------------------------------------- /data/seattle/ballard-seattle-WA.csv: -------------------------------------------------------------------------------- 1 | ,bath,bed,isVavcantLand,price,sqft,zpid,address 2 | 0,2.5,3.0,False,599000.0,1331.0,2101028307,2052-NW-64th-St-Seattle-WA-98107 3 | 1,4.0,3.0,False,680000.0,1513.0,2101028308,6404-22nd-Ave-NW-Seattle-WA-98107 4 | 2,4.0,3.0,False,680000.0,1502.0,2101029232,1762-NW-61st-St-A-Seattle-WA-98107 5 | 3,2.0,3.0,False,690000.0,1668.0,2101031353,1762A-NW-61st-St-Seattle-WA-98107 6 | 4,2.0,3.0,False,690000.0,1668.0,2101031352,1764-NW-61st-St-Seattle-WA-98107 7 | 5,3.0,3.0,False,700000.0,1664.0,2101032649,1760-NW-61st-St-Seattle-WA-98107 8 | 6,2.0,3.0,False,710000.0,1658.0,2101044605,2607-NW-59th-St-Seattle-WA-98107 9 | 7,2.0,2.0,False,575000.0,1250.0,2105741419,2000-NW-57th-St-Seattle-WA-98107 10 | 8,2.0,2.0,False,600000.0,1230.0,2101116419,6411-26th-Ave-NW-Seattle-WA-98107 11 | 9,3.0,3.0,False,630000.0,1200.0,72497529,5615-24th-Ave-NW-UNIT-53-Seattle-WA-98107 12 | 10,1.0,1.0,False,360000.0,724.0,48860936,1770-NW-58th-St-APT-426-Seattle-WA-98107 13 | 11,1.0,2.0,False,325000.0,863.0,63510047,1545-NW-57th-St-UNIT-333-Seattle-WA-98107 14 | 12,1.0,1.0,False,320000.0,747.0,70585145,2641-NW-59th-St-UNIT-A-Seattle-WA-98107 15 | 13,4.0,3.0,False,575000.0,1421.0,54938925,1525-NW-57th-St-UNIT-511-Seattle-WA-98107 16 | 14,1.0,1.0,False,305000.0,698.0,63335794,2410-NW-59th-St-A-Seattle-WA-98107 17 | 15,2.5,3.0,False,579000.0,1540.0,48823536,1548-NW-58th-St-A-Seattle-WA-98107 18 | 16,2.0,2.0,False,475000.0,1520.0,2101393141,5703-20th-Ave-NW-Seattle-WA-98107 19 | 17,2.0,2.0,False,550000.0,1194.0,70585223,1736-NW-59th-St-B-Seattle-WA-98107 20 | 18,3.25,3.0,False,597000.0,1555.0,48823350,1711-NW-61st-St-Seattle-WA-98107 21 | 19,2.0,3.0,False,825000.0,2200.0,54657760,1525-NW-57th-St-UNIT-407-Seattle-WA-98107 22 | 20,2.0,2.0,False,420000.0,964.0,84759478,5440-Leary-Ave-NW-UNIT-526-Seattle-WA-98107 23 | 22,3.75,7.0,False,942000.0,3740.0,2102027315,2043A-NW-64th-St-Seattle-WA-98107 24 | 24,2.0,2.0,False,649000.0,1131.0,2103289114,1760-NW-56th-St-507-Seattle-WA-98107 25 | 26,1.75,3.0,False,510000.0,1585.0,48823200,1737-NW-62nd-St-Seattle-WA-98107 26 | 27,1.0,3.0,False,700000.0,1200.0,2106576811,1760-NW-56th-St-418-Seattle-WA-98107 27 | 28,2.0,2.0,False,730000.0,1131.0,48823195,1548-NW-61st-St-Seattle-WA-98107 28 | -------------------------------------------------------------------------------- /data/seattle/fremont-seattle-WA.csv: -------------------------------------------------------------------------------- 1 | ,bath,bed,isVavcantLand,price,sqft,zpid,address 2 | 0,2.0,2.0,False,660000.0,1602.0,80120162,120-NW-39th-St-UNIT-305-Seattle-WA-98107 3 | 1,1.0,2.0,False,339000.0,729.0,70577856,511-N-39th-St-Seattle-WA-98103 4 | 2,2.0,3.0,False,670000.0,2167.0,84757782,715-N-42nd-St-Seattle-WA-98103 5 | 3,3.0,2.0,False,595000.0,1500.0,72495803,3901-1st-Ave-NW-UNIT-202-Seattle-WA-98107 6 | 4,1.0,1.0,False,315000.0,623.0,49139481,600-N-47th-St-Seattle-WA-98103 7 | 5,2.0,5.0,False,700000.0,2580.0,2101101275,4114-Linden-Ave-N-A-Seattle-WA-98103 8 | 6,2.5,3.0,False,680000.0,1446.0,48973419,4306-Winslow-Pl-N-A-Seattle-WA-98103 9 | 7,2.25,2.0,False,520000.0,1210.0,63335489,3624-Whitman-Ave-N-Seattle-WA-98103 10 | 8,1.0,2.0,False,335000.0,888.0,48656799,4415-4th-Ave-NW-Seattle-WA-98107 11 | 9,2.5,4.0,False,850000.0,2220.0,48692878,404-N-48th-St-Seattle-WA-98103 12 | 10,3.0,9.0,False,979000.0,3560.0,48998211,4332-Baker-Ave-NW-Seattle-WA-98107 13 | 11,3.0,5.0,False,599000.0,2600.0,49040960,120-N-46th-St-Seattle-WA-98103 14 | 13,,,False,399000.0,,58389558,3824-Evanston-Ave-N-APT-4-Seattle-WA-98103 15 | 14,3.0,3.0,False,718000.0,1427.0,61766907,4310-Dayton-Ave-N-APT-203-Seattle-WA-98103 16 | 15,1.0,1.0,False,250000.0,600.0,48769172,4124-Greenwood-Ave-N-Seattle-WA-98103 17 | 16,2.0,3.0,False,895000.0,2640.0,49139449,512-N-46th-St-B-Seattle-WA-98103 18 | 17,2.5,3.0,False,535000.0,1420.0,49139534,707-N-48th-St-Seattle-WA-98103 19 | 18,2.0,3.0,False,12500000.0,2590.0,2136199209,406-N-45th-St-APT-202-Seattle-WA-98103 20 | 19,1.0,2.0,False,359000.0,778.0,48973302,4416-Dayton-Ave-N-Seattle-WA-98103 21 | 20,1.5,2.0,False,900000.0,1132.0,48769080,4037-Dayton-Ave-N-Seattle-WA-98103 22 | 21,1.0,2.0,False,649000.0,1240.0,48998324,4241-Palatine-Ave-N-Seattle-WA-98103 23 | 22,1.0,2.0,False,460000.0,800.0,48998346,4223-Greenwood-Ave-N-Seattle-WA-98103 24 | 23,3.0,4.0,False,745000.0,2470.0,48769064,415-N-42nd-St-Seattle-WA-98103 25 | 24,1.75,3.0,False,989000.0,2050.0,48907466,4000-Midvale-Ave-N-Seattle-WA-98103 26 | 25,3.0,5.0,False,12900000.0,3000.0,49141103,5012-3rd-Ave-NW-Seattle-WA-98107 27 | 26,3.0,3.0,False,850000.0,1971.0,48722917,120-NW-40th-St-Seattle-WA-98107 28 | 27,2.0,3.0,False,785000.0,2100.0,49090133,4134-1st-Ave-NW-Seattle-WA-98107 29 | 28,1.75,2.0,False,700000.0,1540.0,2103659430,417-N-50th-St-Seattle-WA-98103 30 | 29,4.0,3.0,False,10000000.0,1772.0,48998252,106-NW-42nd-St-Seattle-WA-98107 31 | 30,3.75,6.0,False,18900000.0,3337.0,58382681,3652-Dayton-Ave-N-Seattle-WA-98103 32 | 31,3.5,3.0,False,800000.0,2640.0,80117029,3601-Fremont-Ave-N-1-Seattle-WA-98103 33 | 32,,,False,85200000.0,42542.0,82239721,3632-Palatine-Ave-N-A-Seattle-WA-98103 34 | 34,,,False,909000.0,3192.0,49139723,323-N-46th-St-Seattle-WA-98103 35 | -------------------------------------------------------------------------------- /data/seattle/wallingford-seattle-WA.csv: -------------------------------------------------------------------------------- 1 | ,bath,bed,isVavcantLand,price,sqft,zpid,address 2 | 0,4.0,5.0,False,16900000,4000.0,49143608,5112-Keystone-Pl-N-Seattle-WA-98103 3 | 1,2.0,3.0,False,11900000,2260.0,48789484,1409-N-41st-St-Seattle-WA-98103 4 | 2,2.0,2.0,False,645000,1660.0,49011610,4909-Green-Lake-Way-N-Seattle-WA-98103 5 | 3,,0.0,False,825000,3300.0,48898358,5806-Latona-Ave-NE-Seattle-WA-98105 6 | 4,3.25,5.0,False,839000,2930.0,48997591,3900-2nd-Ave-NE-APT-405-Seattle-WA-98105 7 | 5,2.0,2.0,False,800000,1442.0,49143952,114-NE-51st-St-Seattle-WA-98105 8 | 6,2.0,4.0,False,835000,2860.0,49114926,1815-N-55th-St-Seattle-WA-98103 9 | 7,2.0,3.0,False,749000,1820.0,48847661,4321-4th-Ave-NE-Seattle-WA-98105 10 | 8,4.0,6.0,False,940000,2630.0,2105677171,1730-N-Northlake-Way-B315-Seattle-WA-98103 11 | 9,1.75,2.0,False,530000,1026.0,80387842,2143-N-Northlake-Way-APT-9-Seattle-WA-98103 12 | 10,1.0,1.0,False,490000,748.0,49143958,119-NE-52nd-St-Seattle-WA-98105 13 | 11,1.0,1.0,False,510000,1020.0,48898424,126-NE-57th-St-Seattle-WA-98105 14 | 12,1.0,2.0,False,399000,870.0,48919693,2304-N-38th-St-Seattle-WA-98103 15 | 13,3.0,3.0,False,11000000,2620.0,2101390850,2143-N-Northlake-Way-30-Seattle-WA-98103 16 | 14,0.75,1.0,False,347000,720.0,48898333,117-NE-59th-St-Seattle-WA-98105 17 | 16,,,False,846000,,49011649,1319-N-49th-St-Seattle-WA-98103 18 | 17,,0.0,False,380000,1190.0,49143622,5211-Kensington-Pl-N-Seattle-WA-98103 19 | 19,1.0,3.0,False,480000,1070.0,63925624,1310-N-Lucas-Pl-UNIT-301-Seattle-WA-98103 20 | 20,2.0,2.0,False,400000,916.0,48997586,3900-2nd-Ave-NE-APT-307-Seattle-WA-98105 21 | 21,2.0,2.0,False,500000,1100.0,49011628,1107-N-50th-St-Seattle-WA-98103 22 | 22,3.0,4.0,False,775000,3000.0,82365807,2143-N-Northlake-Way-APT-65-Seattle-WA-98103 23 | 23,1.1,2.0,False,519000,900.0,48928071,4006-1st-Ave-NE-Seattle-WA-98105 24 | 24,2.0,3.0,False,899000,2250.0,49144164,5728-Woodlawn-Ave-N-Seattle-WA-98103 25 | 26,1.0,3.0,False,752000,1700.0,49011727,1413-N-47th-St-Seattle-WA-98103 26 | 27,2.0,4.0,False,875000,2400.0,48847555,4327-Thackeray-Pl-NE-Seattle-WA-98105 27 | 28,2.0,3.0,False,10500000,2370.0,82366382,2143-N-Northlake-Way-APT-30-Seattle-WA-98103 28 | 29,,1.0,False,347000,720.0,49102678,4516-Thackeray-Pl-NE-Seattle-WA-98105 29 | 30,2.75,4.0,False,950000,2000.0,49102717,4757-Thackeray-Pl-NE-Seattle-WA-98105 30 | 31,4.0,4.0,False,768000,2950.0,48898162,342-NE-59th-St-Seattle-WA-98105 31 | 32,3.0,4.0,False,839000,2500.0,49144030,1406-N-55th-St-Seattle-WA-98103 32 | 33,3.5,3.0,False,650000,1700.0,49011653,4803-Stone-Way-N-Seattle-WA-98103 33 | 34,2.0,3.0,False,755000,1960.0,48898292,2219-N-59th-St-Seattle-WA-98103 34 | 35,4.0,4.0,False,15000000,2600.0,48789443,1408-N-36th-St-Seattle-WA-98103 35 | 36,4.0,5.0,False,18000000,3581.0,49114918,1909-N-55th-St-Seattle-WA-98103 36 | 37,1.0,3.0,False,785000,2280.0,48920152,3503-Wallingford-Ave-N-Seattle-WA-98103 37 | 39,1.0,4.0,False,660000,1720.0,65228606,4022-Stone-Way-N-Seattle-WA-98103 38 | 40,2.5,2.0,False,430000,1359.0,63925627,4022-Stone-Way-N-Seattle-WA-98103 39 | 41,1.75,2.0,False,356000,880.0,48927983,408-NE-40th-St-Seattle-WA-98105 40 | -------------------------------------------------------------------------------- /code/pipeline/get_schools.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import json 3 | import requests 4 | import requesocks 5 | import time 6 | from bs4 import BeautifulSoup as BS 7 | import re 8 | import os 9 | 10 | class GetSchools(object): 11 | """ 12 | How to use this class to get school info into a pandas dataframe: 13 | 14 | df = pd.read_csv('seattle.csv', index_col=0) 15 | gs = GetSchools() 16 | gs.load_school('seattle_schools.txt') 17 | gs.get_html(df, 0, len(df)) 18 | gs.compute_school_scores() 19 | gs.to_dataframe() 20 | gs.impute_missing_values() 21 | gs.df.to_csv('seattle.csv') 22 | 23 | """ 24 | 25 | 26 | def __init__(self): 27 | self.schools = None 28 | self.df = None 29 | self.school_scores = [] 30 | self.df_scores = None 31 | 32 | def load_school(self, filename): 33 | with open(filename, 'r') as f: 34 | d = f.read() 35 | self.schools = json.loads(d) 36 | 37 | def get_html(self, df, start, stop): 38 | self.df = df 39 | params = {'user-agent': 'Mozilla/5.0'} 40 | session = requesocks.session() 41 | #Use Tor for both HTTP and HTTPS 42 | #session.proxies = {'http': 'socks5://127.0.0.1:9150', 43 | # 'https': 'socks5://127.0.0.1:9150'} 44 | 45 | os.chdir('../data') 46 | for row in df.ix[start:stop].iterrows(): # iterate through each address in the df and get the html for it 47 | query = (row[1][5]+' '+row[1][1]+' '+row[1][4]+' '+str(row[1][6])).replace(' ', '%20') 48 | # this URL is formatted to query www.noodle.com/schools 49 | URL = "https://www.noodle.com/search/schools?distance=10&location={0}".format(query) 50 | query_url = URL + query + "&sort=best_fit" 51 | try: 52 | r = requests.get(query_url, headers=params) 53 | except: 54 | print "Row {0} HTTP get failed.".format(row[0]) 55 | with open("school_index_{0}.txt".format(row[0]), 'w') as f: # write each html to a txt file so I can parse it later 56 | f.write(r.content) 57 | 58 | print "writing {0}".format(row[0]) 59 | 60 | 61 | def compute_school_average(self, html_source): 62 | school_count = 0 63 | address_score = 0 64 | for school in self.schools.keys(): # school_dict is the school info from the string_to_dict function 65 | m = re.search(school, html_source) 66 | if m: 67 | school_count += 1 68 | address_score += int(self.schools[school]) 69 | if school_count == 0: 70 | return 0 71 | return float(address_score) / school_count # returns an average of all the schools within ten miles 72 | 73 | 74 | def aggregate_scores(self): 75 | 76 | for row in self.df.iterrows(): 77 | with open("school_index_{0}.txt".format(row[0]), 'r') as f: 78 | doc = f.read() 79 | 80 | score = self.compute_school_average(doc) 81 | self.school_scores.append(score) 82 | 83 | 84 | def compute_school_scores(self): 85 | self.aggregate_scores() 86 | 87 | def to_dataframe(self): 88 | self.df_scores = pd.DataFrame({'school_index': self.school_scores}, 89 | index=range(len(self.school_scores))) 90 | self.df_scores['city'] = self.df['city'] 91 | self.df['school_index'] = self.df_scores['school_index'] 92 | 93 | def impute_missing_values(self): 94 | self.df['city'] = self.df['city'].apply(lambda x: x[0] + x[1:].lower()) 95 | for city in self.df['city'].unique(): 96 | 97 | mean_score = self.df_scores['school_index'][(self.df_scores['city'] == city) & (self.df_scores['school_index'] > 0)].mean() 98 | print city, mean_score 99 | self.df['school_index'][(self.df['city'] == city) & (self.df['school_index'] == 0)] = mean_score 100 | self.df['school_index'][self.df['city'] == 'Mercer island'] = 10 101 | self.df['school_index'][self.df['city'] == 'Des moines'] = 4 102 | 103 | 104 | -------------------------------------------------------------------------------- /data/seattle/magnolia-seattle-WA.csv: -------------------------------------------------------------------------------- 1 | ,bath,bed,isVavcantLand,price,sqft,zpid,address 2 | 1,3.5,4.0,False,10400000.0,2544.0,48942783,3423-W-Government-Way-APT-7-Seattle-WA-98199 3 | 2,2.0,2.0,False,360000.0,960.0,58383901,3613-22nd-Ave-W-A-Seattle-WA-98199 4 | 3,3.0,3.0,False,510000.0,1780.0,2101028537,4426-Montana-Cir-W-Seattle-WA-98199 5 | 4,3.0,3.0,False,799000.0,1674.0,49137089,3710-27th-Pl-W-APT-203-Seattle-WA-98199 6 | 5,1.0,1.0,False,185000.0,680.0,89822002,3411-21st-Ave-W-B-Seattle-WA-98199 7 | 6,4.0,3.0,False,565000.0,1760.0,49009400,3825-33rd-Ave-W-Seattle-WA-98199 8 | 7,3.0,3.0,False,575000.0,1900.0,49009939,3314-W-Armour-St-Seattle-WA-98199 9 | 8,1.0,3.0,False,675000.0,1150.0,48906511,3215-Magnolia-Blvd-W-Seattle-WA-98199 10 | 10,4.0,4.0,False,970000.0,2369.0,48726564,2570-Magnolia-Blvd-W-Seattle-WA-98199 11 | 11,6.25,5.0,False,44000000.0,5864.0,48824797,3243-22nd-Ave-W-Seattle-WA-98199 12 | 12,2.0,3.0,False,529000.0,1560.0,61719047,3451-22nd-Ave-W-A-Seattle-WA-98199 13 | 13,4.0,3.0,False,545000.0,1851.0,49084204,3022-W-Smith-St-Seattle-WA-98199 14 | 14,2.0,3.0,False,549000.0,1620.0,48824881,3455-21st-Ave-W-Seattle-WA-98199 15 | 15,2.5,3.0,False,450000.0,1400.0,48702895,2621-40th-Ave-W-Seattle-WA-98199 16 | 16,2.5,4.0,False,930000.0,3030.0,48930759,4433-Brygger-Dr-W-Seattle-WA-98199 17 | 17,2.5,4.0,False,820000.0,2300.0,2102215734,3010A-31st-Ave-W-Seattle-WA-98199 18 | 18,2.0,2.0,False,620000.0,1766.0,48824713,2628-23rd-Ave-W-Seattle-WA-98199 19 | 19,2.25,3.0,False,565000.0,1690.0,48677745,3518-29th-Ave-W-Seattle-WA-98199 20 | 20,3.0,4.0,False,915000.0,2920.0,48824880,3443-21st-Ave-W-Seattle-WA-98199 21 | 21,2.0,3.0,False,480000.0,1640.0,49013230,3850-32nd-Ave-W-Seattle-WA-98199 22 | 22,2.0,3.0,False,705000.0,2300.0,49009929,3036-36th-Ave-W-Seattle-WA-98199 23 | 23,1.0,3.0,False,675000.0,1620.0,48701034,4324-W-Cramer-St-Seattle-WA-98199 24 | 24,,,False,,,48855572,3214-43rd-Ave-W-Seattle-WA-98199 25 | 25,3.5,3.0,False,19800000,5280.0,49009427,3844-33rd-Ave-W-Seattle-WA-98199 26 | 26,2.0,4.0,False,649000,2000.0,2101190820,0-Perkins-Ln-W-Seattle-WA-98199 27 | 27,,,True,499000,,48726287,2515-Perkins-Ln-W-Seattle-WA-98199 28 | 28,3.0,3.0,False,13000000,2410.0,48942763,3725-W-Armour-Pl-Seattle-WA-98199 29 | 30,,,False,,,79975081,3045-20th-Ave-W-APT-408-Seattle-WA-98199 30 | 31,1.0,1.0,False,225000,555.0,49120585,3010-25th-Ave-W-Seattle-WA-98199 31 | 32,1.75,4.0,False,950000,2320.0,119114083,4400-Brygger-Dr-W-A-Seattle-WA-98199 32 | 33,2.25,3.0,False,529000,1570.0,48880378,2724-W-Blaine-St-Seattle-WA-98199 33 | 34,2.75,3.0,False,895000,3030.0,48663296,2932-25th-Ave-W-Seattle-WA-98199 34 | 35,2.5,4.0,False,849000,2990.0,48711739,2922-28th-Ave-W-Seattle-WA-98199 35 | 36,2.5,4.0,False,899000,2500.0,48700921,5443-45th-Ave-W-Seattle-WA-98199 36 | 37,3.0,4.0,False,15500000,3350.0,60972341,3302-W-Government-Way-APT-C-Seattle-WA-98199 37 | 38,2.25,3.0,False,480000,1440.0,48825834,2607-W-Newton-St-Seattle-WA-98199 38 | 39,4.0,4.0,False,12500000,3430.0,48825617,2438-W-Lynn-St-Seattle-WA-98199 39 | 40,3.5,4.0,False,10500000,2710.0,2101392654,4009-W-Briarcliff-Ln-Seattle-WA-98199 40 | 41,3.0,4.0,False,,2286.0,2101392656,3051-Briarcliff-Ln-W-Seattle-WA-98199 41 | 42,4.0,4.0,False,10600000,2369.0,48677326,5614-40th-Ave-W-Seattle-WA-98199 42 | 44,,,False,600000,,96676240,2120-W-Ruffner-St-Seattle-WA-98199 43 | 45,2.5,3.0,False,450000,1360.0,48824807,3432-23rd-Ave-W-Seattle-WA-98199 44 | 46,1.0,1.0,False,400000,610.0,48880402,2601-W-Blaine-St-Seattle-WA-98199 45 | 47,2.0,3.0,False,585000,1900.0,49009376,3836-36th-Ave-W-Seattle-WA-98199 46 | 48,2.0,3.0,False,10200000,3370.0,2101683958,3608-26th-Place-W-401-Seattle-WA-98199 47 | 49,1.75,3.0,False,619000,1576.0,48677293,5435-40th-Ave-W-Seattle-WA-98199 48 | 50,2.5,3.0,False,13000000,2760.0,48927718,2612-23rd-Ave-W-APT-3-Seattle-WA-98199 49 | 51,3.1,4.0,False,729000,2400.0,2101746196,3010-31st-Ave-W-A-Seattle-WA-98199 50 | 52,2.0,2.0,False,620000,1766.0,48942894,3625-Magnolia-Blvd-W-Seattle-WA-98199 51 | 53,3.75,4.0,False,11900000,3370.0,48824958,2853-21st-Ave-W-Seattle-WA-98199 52 | 54,1.75,4.0,False,700000,1590.0,48727137,2805-43rd-Ave-W-Seattle-WA-98199 53 | 55,2.25,4.0,False,20000000,3560.0,48727073,2546-36th-Ave-W-Seattle-WA-98199 54 | 56,1.75,3.0,False,739000,2110.0,92571423,3841-34th-Ave-W-UNIT-B-Seattle-WA-98199 55 | 57,3.0,2.0,False,500000,1416.0,48726480,2323-Eyres-Pl-W-Seattle-WA-98199 56 | 58,4.0,5.0,False,25000000,4300.0,48726895,1636-Magnolia-Blvd-W-Seattle-WA-98199 57 | 59,4.1,6.0,False,28900000,4380.0,49009891,3037-34th-Ave-W-Seattle-WA-98199 58 | 60,1.0,3.0,False,650000,1300.0,2102421280,3900-W-Briarcliff-Ln-Seattle-WA-98199 59 | 61,4.0,4.0,False,11000000,2550.0,49014386,3108-W-Jameson-St-Seattle-WA-98199 60 | 62,4.0,0.0,False,639000,1452.0,49014432,2863-W-Government-Way-Seattle-WA-98199 61 | 63,2.5,4.0,False,640000,2010.0,48731143,3039-31st-Ave-W-Seattle-WA-98199 62 | 64,4.5,5.0,False,15000000,4390.0,48824938,2840-22nd-Ave-W-Seattle-WA-98199 63 | 65,1.0,2.0,False,397000,760.0,48942946,3611-43rd-Ave-W-Seattle-WA-98199 64 | 66,2.0,5.0,False,900000,3120.0,96660547,2116-W-Ruffner-St-Seattle-WA-98199 65 | 67,2.5,3.0,False,480000,1140.0,48727148,2844-43rd-Ave-W-Seattle-WA-98199 66 | 68,2.75,5.0,False,16500000,3620.0,49013136,4012-30th-Ave-W-Seattle-WA-98199 67 | 69,2.0,3.0,False,775000,2040.0,48942772,3615-W-Fulton-St-Seattle-WA-98199 68 | 70,2.0,3.0,False,895000,2240.0,48726264,2333-Perkins-Ln-W-Seattle-WA-98199 69 | 71,3.5,4.0,False,25000000,4900.0,48702935,2608-39th-Ave-W-Seattle-WA-98199 70 | 72,1.5,2.0,False,721000,1120.0,48943274,3007-38th-Ave-W-Seattle-WA-98199 71 | 76,1.0,2.0,False,590000,1080.0,2110264795,2825-Perkins-Ln-W-Seattle-WA-98199 72 | 77,,0.0,True,12000000,,49034868,3430-W-Viewmont-Way-W-Seattle-WA-98199 73 | 78,1.75,3.0,False,950000,2720.0,49029217,5634-39th-Ave-W-Seattle-WA-98199 74 | 79,5.0,4.0,False,18000000,4237.0,48726265,2323-Perkins-Ln-W-APT-A-Seattle-WA-98199 75 | 80,3.0,4.0,False,19500000,2600.0,48943387,2625-28th-Ave-W-Seattle-WA-98199 76 | -------------------------------------------------------------------------------- /data/seattle_schools.txt: -------------------------------------------------------------------------------- 1 | {"Juanita High School": "7", "Cedarhurst Elementary School": "6", "McMicken Heights Elementary School": "2", "Nova High School": "3", "John Rogers Elementary School": "7", "Bryant Elementary School": "10", "Showalter Middle School": "3", "Shorewood Elementary School": "3", "Gatzert Elementary School": "3", "Northgate Elementary School": "3", "Dearborn Park Elementary School": "4", "Newport Senior High School": "9", "Midway Elementary School": "1", "Arbor Heights Elementary School": "6", "Mercer Island High School": "10", "Garfield High School": "6", "Ingraham High School": "6", "Olympic View Elementary School": "7", "Hawthorne Elementary School": "2", "Sacajawea Middle School": "5", "Bryn Mawr Elementary School": "4", "Stella Schola": "10", "Cascade View Elementary School": "3", "Maple Elementary School": "9", "Southern Heights Elementary School": "3", "Rainier Beach High School": "4", "Cleveland High School": "7", "Bellevue High School": "8", "Nelsen Middle School": "5", "Sand Point Elementary School": "6", "Henry David Thoreau Elementary School": "8", "North Beach Elementary School": "10", "Seahurst Elementary School": "1", "Loyal Heights Elementary School": "10", "Academy of Citizenship and Empowerment": "3", "Daniel Bagley Elementary School": "9", "Montlake Elementary School": "10", "Interlake Senior High School": "6", "Hilltop Elementary School": "2", "Alelxander Graham Bell Elementary School": "6", "Washington Middle School": "7", "Federal Way Senior High School": "3", "Cascade Elementary School": "5", "Beacon Hill Elementary School": "8", "Green Lake Elementary School": "9", "Queen Anne Elementary School": "9", "Wing Luke Elementary School": "8", "Sylvester Middle School": "7", "Middle College High School": "1", "Highlands Elementary School": "5", "Chinook Middle School": "7", "Bellevue Big Picture School": "8", "Gatewood Elementary School": "6", "Woodmont Elementary School": "6", "View Ridge Elementary School": "10", "Hay Elementary School": "10", "Des Moines Elementary School": "2", "Leschi Elementary School": "3", "Interagency Programs": "2", "West Seattle High School": "7", "Health Sciences & Human Services": "4", "Kimball Elementary School": "6", "Lowell Elementary School": "4", "Schmitz Park Elementary School": "8", "Thorndyke Elementary School": "3", "Talbot Hill Elementary School": "7", "Benjamin Franklin Elementary School": "9", "Catharine Blaine K-8": "9", "Madrona K-8": "2", "Highland Park Elementary School": "3", "Madrona Elementary School": "2", "Rose Hill Junior High School": "9", "Sanislo Elementary School": "3", "McKnight Middle School": "7", "Highline High School": "4", "Northstar Middle School": "10", "Sacajawea Elementary School": "9", "Chief Sealth High School": "5", "Rose Hill Elementary School": "7", "Discovery Community School": "10", "White Center Heights Elementary": "4", "Graham Hill Elementary School": "2", "Franklin High School": 7, "North Hill-Primary School": "8", "Highline Big Picture School": "4", "Environmental & Adventure School": "10", "Hamilton International Middle School": "10", "Island Park Elementary School": "10", "McClure Middle School": "8", "International Community School": "10", "Whitman Middle School": "7", "Helen Keller Elementary School": "9", "Broadview-Thomson Elementary School": "6", "Kirkland Junior High School": "10", "Kennydale Elementary School": "8", "West Mercer Elementary School": "10", "Mc Donald Elementary School": "9", "Thurgood Marshall Elementary School": "10", "Sierra Heights Elementary School": "6", "Peter Kirk Elementary School": "10", "Beverly Park Elementary at Glendale": "1", "Dunlap Elementary School": "4", "Hazelwood Elementary School": "8", "Tukwila Elementary School": "3", "Tiffany Park Elementary School": "8", "Adams Elementary School": "7", "Lakeview Elementary School": "9", "Eckstein Middle School": "9", "Technology Engineering & Communications": "4", "West Woodland Elementary School": "10", "International School": "10", "Arts & Academics Academy": "2", "Pacific Middle School": "2", "Sammamish Senior High School": "6", "Lawton Elementary School": "10", "Nathan Hale High School": "9", "Concord Elementary School": "5", "Carl Sandburg Elementary School": "9", "Campbell Hill Elementary School": "3", "Parkside Elementary School": "3", "Whittier Elementary School": "10", "Greenwood Elementary School": "8", "Wedgwood Elementary School": "10", "Coe Elementary School": "10", "Finn Hill Junior High School": "8", "Mercer Middle School": "9", "Aviation High School": "8", "Roosevelt High School": "9", "John Stanford International Elementary School": "10", "Aki Kurose Middle School": "4", "Hazen Senior High School": "8", "Mount Rainier High School": "5", "Mount View Elementary School": "1", "McGilvra Elementary School": "8", "Futures School": 1, "Juanita Elementary School": "9", "Lakeridge Elementary School": "5", "Lafayette Elementary School": "9", "John Muir Elementary School": "5", "Roxhill Elementary School": "2", "Madison Middle School": "8", "Hazel Valley Elementary School": "1", "Foster Senior High School": "4", "Ballard High School": "9", "Robert Frost Elementary School": "7", "Olympic Hills Elementary School": "5", "Islander Middle School": "10", "Lindbergh Senior High School": "8", "Cascade Middle School": "5", "Bow Lake Elementary School": "1", "South Lake High School": "1", "Orca (Columbia)": "4", "Emerson Elementary School": "2", "Honey Dew Elementary School": "4", "Salmon Bay School": "9", "Kamiakin Junior High School": "7", "Jane Addams Middle School": "8", "Pathfinder K-8": "8", "Dimmitt Middle School": "5", "Maplewood Heights Elementary School": "9", "Laurelhurst Elementary School": "10", "New Start": 1, "Pinehurst K-8 School": "4", "The Center School": "7", "Odyssey - the Essential School": "4", "Stevens Elementary School": "5", "Tops K-8": "7", "Renton Senior High School": "6", "Benson Hill Elementary School": "7", "B F Day Elementary School": "6", "Marvista Elementary School": "8", "Gregory Heights Elementary School": "5", "H.O.M.E. Program": "9", "Global Connections High School": "3", "Alki Elementary School": "7", "Renton Park Elementary School": "3"} -------------------------------------------------------------------------------- /data/seattle/capitol-hill-seattle-WA.csv: -------------------------------------------------------------------------------- 1 | ,bath,bed,isVavcantLand,price,sqft,zpid,address 2 | 0,2.0,2.0,False,515000.0,900.0,49053959,601-Belmont-Ave-E-APT-E5-Seattle-WA-98102 3 | 1,1.0,1.0,False,450000.0,806.0,71551381,1610-Belmont-Ave-Seattle-WA-98122 4 | 2,2.0,2.0,False,515000.0,900.0,48708132,116-11th-Ave-E-APT-202-Seattle-WA-98102 5 | 3,1.75,2.0,False,428000.0,1045.0,48657902,505-E-Denny-Way-A405-Seattle-WA-98122 6 | 4,1.0,1.0,False,250000.0,548.0,2101029786,513-20th-Ave-E-C-Seattle-WA-98112 7 | 5,2.0,3.0,False,850000.0,1607.0,2101031506,1140-10th-Ave-E-A-Seattle-WA-98102 8 | 6,3.0,2.0,False,940000.0,1900.0,2101031502,1146-10th-Ave-E-A-Seattle-WA-98102 9 | 7,3.0,3.0,False,900000.0,1775.0,2109644889,626-13th-Ave-E-APT-L-Seattle-WA-98102 10 | 8,1.0,2.0,False,349000.0,790.0,88877079,530-Broadway-E-APT-134-Seattle-WA-98102 11 | 9,2.5,2.0,False,540000.0,1172.0,2101041132,227-B-23rd-Ave-E-Seattle-WA-98112 12 | 10,2.0,3.0,False,700000.0,1460.0,48725575,927-20th-Ave-E-Seattle-WA-98112 13 | 11,4.25,6.0,False,19000000.0,3320.0,65227536,1640-18th-Ave-APT-3-Seattle-WA-98122 14 | 12,1.0,1.0,False,310000.0,533.0,48710255,1950-15th-Ave-E-Seattle-WA-98112 15 | 13,4.0,4.0,False,16500000.0,3450.0,66109176,1813-13th-Ave-A-Seattle-WA-98122 16 | 14,3.5,3.0,False,10000000.0,1890.0,48684991,710-Belmont-Pl-E-APT-308-Seattle-WA-98102 17 | 15,1.0,1.0,False,380000.0,834.0,48976178,1208-E-Thomas-St-Seattle-WA-98102 18 | 16,1.75,4.0,False,775000.0,1950.0,67684943,200-17th-Ave-E-Seattle-WA-98112 19 | 17,1.0,1.0,False,395000.0,713.0,88876063,752-Bellevue-Ave-E-APT-308-Seattle-WA-98102 20 | 18,2.0,2.0,False,350000.0,692.0,48782487,1019-Belmont-Pl-E-Seattle-WA-98102 21 | 19,2.5,5.0,False,17300000.0,4930.0,2121744047,1185-Broadway-E-Seattle-WA-98102 22 | 20,1.75,3.0,False,26500000.0,2363.0,2101164072,1185-Broadway-E-UNIT-PH7-Seattle-WA-98102 23 | 21,2.0,3.0,False,26500000.0,2363.0,49097931,111-14th-Ave-E-APT-E-Seattle-WA-98112 24 | 22,1.0,1.0,False,398000.0,788.0,2111529332,1818-18th-Ave-APT-303-Seattle-WA-98122 25 | 23,1.0,1.0,False,339000.0,747.0,2101186913,125-16th-Ave-E-Seattle-WA-98112 26 | 24,1.0,1.0,False,367000.0,589.0,48724913,711-12th-Ave-E-Seattle-WA-98102 27 | 25,3.0,7.0,False,990000.0,2280.0,82365995,1408-12th-Ave-UNIT-508-Seattle-WA-98122 28 | 27,,,False,896000.0,,48657961,506-E-Howell-St-APT-E305-Seattle-WA-98122 29 | 28,1.0,1.0,False,315000.0,546.0,63335596,941-11th-Ave-E-Seattle-WA-98102 30 | 29,2.0,2.0,False,11500000.0,2121.0,72495182,1125-E-Olive-St-APT-502-Seattle-WA-98122 31 | 30,2.0,2.0,False,600000.0,910.0,82363915,1408-12th-Ave-UNIT-502-Seattle-WA-98122 32 | 31,1.0,1.0,False,600000.0,821.0,82364725,1408-12th-Ave-UNIT-307-Seattle-WA-98122 33 | 32,1.0,1.0,False,425000.0,677.0,48826207,411-Boylston-Ave-E-Seattle-WA-98102 34 | 33,1.0,0.0,False,230000.0,502.0,89208540,1111-E-Pike-St-APT-602-Seattle-WA-98122 35 | 34,1.0,1.0,False,415000.0,667.0,48976127,109-11th-Ave-E-Seattle-WA-98102 36 | 35,3.0,3.0,False,11200000.0,2710.0,82364821,1707-Boylston-Ave-APT-209-Seattle-WA-98122 37 | 36,1.0,2.0,False,425000.0,817.0,48826190,411-Boylston-Ave-E-APT-108-Seattle-WA-98102 38 | 37,1.0,0.0,False,189000.0,502.0,48725241,1153-17th-Ave-E-Seattle-WA-98112 39 | 38,3.1,5.0,False,16000000.0,2160.0,79520310,400-Melrose-Ave-E-APT-607-Seattle-WA-98102 40 | 39,1.0,1.0,False,345000.0,818.0,48819603,232-Belmont-Ave-E-APT-303-Seattle-WA-98102 41 | 40,1.5,1.0,False,430000.0,835.0,48976103,1110-E-Denny-Way-Seattle-WA-98122 42 | 41,2.5,5.0,False,14500000.0,3080.0,48725066,902-17th-Ave-E-Seattle-WA-98112 43 | 42,4.1,8.0,False,22500000.0,7549.0,2101679502,1175-Broadway-E-UNIT-D-6-Seattle-WA-98102 44 | 43,2.25,2.0,False,18500000.0,2007.0,48790019,1819-17th-Ave-APT-1-Seattle-WA-98122 45 | 44,1.0,1.0,False,350000.0,623.0,48989106,730-Belmont-Ave-E-APT-1-Seattle-WA-98102 46 | 45,1.0,2.0,False,825000.0,1333.0,68478240,1711-E-Olive-Way-APT-501-Seattle-WA-98102 47 | 46,1.0,1.0,False,450000.0,619.0,48754792,131-Bellevue-Ave-E-APT-401-Seattle-WA-98102 48 | 47,2.0,2.0,False,550000.0,1054.0,48818955,955-10th-Ave-E-Seattle-WA-98102 49 | 48,3.0,2.0,False,949000.0,1400.0,96698419,812-E-Blaine-St-Seattle-WA-98102 50 | 49,2.0,2.0,False,11000000.0,1750.0,48819627,232-Belmont-Ave-E-APT-606-Seattle-WA-98102 51 | 50,2.0,2.0,False,675000.0,1136.0,2124190651,1410-E-Pine-St-UNIT-W324-Seattle-WA-98122 52 | 51,1.75,2.0,False,450000.0,888.0,48930203,1811-E-Mercer-St-Seattle-WA-98112 53 | 52,2.0,4.0,False,899000.0,2580.0,58386475,215-20th-Ave-E-APT-104-Seattle-WA-98112 54 | 53,2.0,2.0,False,550000.0,951.0,67685683,1420-E-Pine-St-UNIT-710-Seattle-WA-98122 55 | 54,1.0,1.0,False,400000.0,698.0,48871559,1000-E-Garfield-St-Seattle-WA-98102 56 | 55,11.0,6.0,False,27500000.0,10160.0,65229180,1804-15th-Ave-A-Seattle-WA-98122 57 | 56,3.25,3.0,False,11000000.0,1630.0,88876049,1114-Lakeview-Blvd-E-APT-B-Seattle-WA-98102 58 | 57,3.0,2.0,False,785000.0,1670.0,2110508023,1550-Lakeview-Blvd-E-Seattle-WA-98102 59 | 58,,0.0,True,350000.0,,118557153,730-Boylston-Ave-E-APT-A-Seattle-WA-98102 60 | 59,3.5,4.0,False,16500000.0,2860.0,48725484,1103-20th-Ave-E-Seattle-WA-98112 61 | 60,3.25,5.0,False,16500000.0,3520.0,48821223,516-E-Thomas-St-APT-304-Seattle-WA-98102 62 | 61,1.0,1.0,False,400000.0,600.0,2103548570,505-E-Denny-Way-APT-207-Seattle-WA-98122 63 | 62,1.0,1.0,False,300000.0,501.0,2103690552,1614-Summit-Ave-S-505-Seattle-WA-98122 64 | 63,1.0,1.0,False,300000.0,445.0,48926974,505-Belmont-Ave-E-APT-1002-Seattle-WA-98102 65 | 64,1.0,1.0,False,474000.0,720.0,49072197,404-E-Harrison-St-APT-201-Seattle-WA-98102 66 | 65,1.0,1.0,False,226000.0,586.0,2104616497,1708-14th-Ave-Seattle-WA-98122 67 | 66,2.5,2.0,False,800000.0,1390.0,48725511,1217-24th-Ave-E-Seattle-WA-98112 68 | 67,1.0,2.0,False,500000.0,1040.0,48657101,1610-Lakeview-Blvd-E-Seattle-WA-98102 69 | 68,2.75,3.0,False,715000.0,2180.0,52423404,215-20th-Ave-E-APT-301-Seattle-WA-98112 70 | 69,2.0,2.0,False,430000.0,948.0,2115164676,325-Harvard-Ave-E-APT-301-Seattle-WA-98102 71 | 70,1.0,0.0,False,245000.0,490.0,108801719,1802-E-John-St-Seattle-WA-98112 72 | 71,2.0,2.0,False,750000.0,1504.0,2126244921,1812-19th-Ave-APT-305-Seattle-WA-98122 73 | 72,1.5,2.0,False,274000.0,710.0,82363926,756-Broadway-E-APT-104-Seattle-WA-98102 74 | 73,,,False,30200000.0,11835.0,48724918,712-11th-Ave-E-Seattle-WA-98102 75 | -------------------------------------------------------------------------------- /code/pre_processing.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | 4 | class PreProcess(object): 5 | 6 | def __init__(self, df): 7 | self.df = df 8 | self.dropped_columns_ = ['_id', 'city', 'latitude', 'longitude', 'state', 9 | 'street', 'zipcode', 'appliances', 'architecture', 10 | 'basement', 'coolingsystem', 'exteriormaterial', 11 | 'floorcovering', 'floornumber', 'heatingsources', 12 | 'heatingsystem', 'numfloors', 'numunits', 'roof', 13 | 'rooms', 'view', 'yearupdated', 'elementaryschool', 14 | 'highschool', 'homedescription', 'images_count', 15 | 'images_image', 'links_homedetails', 'links_homeinfo', 16 | 'links_photogallery', 'middleschool', 'neighborhood', 17 | 'pageviewcount_currentmonth', 'pageviewcount_total', 18 | 'agentname' ,'agentprofileurl', 'externalurl', 19 | 'lastupdateddate', 'mls', 'openhousedates', 'status', 20 | 'schooldistrict', 'usecode', 'type', 'trans_desc', 21 | 'trans_summary', 'walkscore_desc', 'whatownerloves', 'zpid'] 22 | 23 | def drop_columns(self): 24 | """ 25 | 26 | Notes: this function will drop all of the columns from a dataframe that are not 27 | needed for computation of any of the similarity metrics. For example, 'heatingsources' 28 | is not currently used in any of the similarity metrics, so it is dropped. 29 | 30 | """ 31 | self.df = self.df.drop(self.dropped_columns_, axis=1) 32 | try: 33 | self.df = self.df.drop('editedfacts', axis=1) 34 | except: 35 | pass 36 | 37 | def preprocess_df(self): 38 | self.df = self.df.drop(['numrooms', 'price'], axis=1) #there are too many NA values for price for it ot be useful 39 | self.df = self.df.dropna(axis=0, how='any') 40 | # remove 'None' values in trans_score 41 | indices = self.df[self.df['trans_score'].isin(['None'])].index 42 | self.df = self.df[~(self.df.index.isin(indices))] 43 | indices = self.df[self.df['walkscore_score'].isin(['None'])].index 44 | self.df = self.df[~(self.df.index.isin(indices))] 45 | # cast as ints 46 | 47 | self.df[['trans_score', 'walkscore_score']] = self.df[['trans_score', 'walkscore_score']].astype(int) 48 | # remove the rows that are absurdly big and are probably mistakes 49 | self.df = self.df[self.df.bedrooms <= 20] 50 | self.df = self.df[self.df.bathrooms <= 20] 51 | self.df[['bathrooms', 'bedrooms', 'finishedsqft']] = self.df[['bathrooms', 'bedrooms', 'finishedsqft']].astype(float) 52 | 53 | 54 | def normalize_num(self, x, col_min, col_max): 55 | """ 56 | Normalize everything from 0 to 1 57 | """ 58 | return float((x - col_min)) / (col_max - col_min) 59 | 60 | 61 | def normalize_columns(self, columns): 62 | """ 63 | Input: a list of columns to normalize 64 | Output: a dataframe that has normalized the columns between 0 and 1 65 | """ 66 | 67 | for col in columns: 68 | min_val = self.df[col].min() 69 | max_val = self.df[col].max() 70 | self.df[col] = self.df[col].apply(self.normalize_num, args=(min_val, max_val)) 71 | 72 | 73 | def create_parking_index(self): 74 | """ 75 | Input: a dataframe 76 | Output: a dateframe 77 | Notes: this function will take the uncleaned columns of parkingtype and coveredparkingspaces and 78 | apply score_parking to return a column that is a numerical index of parking quality for a listing 79 | """ 80 | 81 | cols = ['parkingtype', 'coveredparkingspaces'] 82 | self.df[cols] = self.df[cols].fillna('None') 83 | # score_parking is created and called within the scope of create_parking_index 84 | def score_parking(parkingtype, coveredparkingspaces): 85 | type_score = 0 86 | space_score = 0 87 | if parkingtype != "None": 88 | if "Garage - Attached" in parkingtype: 89 | type_score += 0.5 90 | elif "Garage - Detached" in parkingtype: 91 | type_score += 0.4 92 | elif "Carport" in parkingtype: 93 | type_score += 0.3 94 | elif "Off-street" in parkingtype: 95 | type_score += 0.2 96 | elif "On-street" in parkingtype: 97 | type_score += 0.1 98 | else: 99 | type_score = 0 100 | if coveredparkingspaces != "None": 101 | if coveredparkingspaces >= 5: 102 | space_score += 0.5 103 | else: 104 | space_score += coveredparkingspaces * 0.1 105 | else: 106 | space_score = 0 107 | return type_score + space_score 108 | 109 | self.df['parking_index'] = self.df[cols].apply(lambda x: score_parking(x[cols[0]], x[cols[1]]), axis=1) 110 | self.df = self.df.drop(cols, axis=1) # drop the unnecessary columns 111 | 112 | 113 | 114 | 115 | 116 | def filter_df(self, metric): 117 | 118 | """ 119 | Input: takes a dataframe, and the name of a similarity metric as a string 120 | Output: the cleaned and filtered df for the appropraite metric 121 | 122 | Notes: this function is meant to trim the dataframe to use only the 123 | columns that are relevant to a given similarity metric. 124 | """ 125 | if metric == "walk_distance": 126 | self.df = self.df[['trans_score', 'walkscore_score']].dropna(axis=0, how='any') 127 | return self.df.astype(float) 128 | 129 | if metric == "space_distance": 130 | self.df = self.df[['bathrooms', 'bedrooms', 'finishedsqft']].dropna(axis=0, how='any') 131 | return self.df.astype(float) 132 | 133 | if metric == "family_distance": 134 | self.df = self.df[['bedrooms', 'yearbuilt', 'lotsizesqft', 'parking_index', 'school_index']] 135 | return self.df 136 | 137 | 138 | 139 | -------------------------------------------------------------------------------- /code/pipeline/walkscore_api.py: -------------------------------------------------------------------------------- 1 | import requests 2 | import pandas as pd 3 | import pprint as pprint 4 | import json 5 | from itertools import izip 6 | from pymongo import MongoClient 7 | from bson.objectid import ObjectId 8 | import os 9 | 10 | class WalkScore(object): 11 | """ 12 | How to use this class: 13 | 14 | from walkscore_api import WalkScore 15 | ws = WalkScore() 16 | ws.query_api() # queries all the listings in self.df 17 | ws.get_api_report() # check how many api calls were successful or not 18 | ws.get_and_format_data() # put all the queried data into a pandas df 19 | ws.update_mongo() # insert all the data into MongoDB 20 | 21 | """ 22 | 23 | 24 | def __init__(self): 25 | print "Test XXX!" 26 | self.df = None 27 | self.walk_scores = [] 28 | self.transit_scores = [] 29 | self.df_api = None 30 | self.response_status = {} 31 | self.incompletes = [] 32 | self.df_final = None 33 | 34 | def query_api(self, df): 35 | self.df = df 36 | WSAPI_KEY = os.getenv('WALKABLE_API_KEY') 37 | TRANSIT_BASE_URL = "http://transit.walkscore.com/transit/score/?lat={0}&lon={1}&city={2}&state={3}&wsapikey={4}&research=yes" 38 | WALK_BASE_URL = "http://api.walkscore.com/score?format={0}&address={1}%20{2}%20{3}%20{4}&lat={5}&lon={6}&wsapikey={7}" 39 | self.df['city'] = self.df['city'].apply(lambda city: city.replace(' ', '-')) 40 | self.df['city'] = self.df['city'].apply(lambda city: "Seattle" if city=="Des-Moines" else city) 41 | self.df['street'] = self.df['street'].apply(lambda street: street[:street.find('#')-1] if '#' in street else street) 42 | self.df['street'] = self.df['street'].apply(lambda street: street.replace(' ', '-')) 43 | count = 1 44 | for row in self.df.iterrows(): 45 | city = row[1][1] 46 | lat = row[1][2] 47 | lon = row[1][3] 48 | state = row[1][4] 49 | address = row[1][5] 50 | zip_code = row[1][6] 51 | walk_api_query = WALK_BASE_URL.format('json', address, zip_code, city, state, lat, lon, WSAPI_KEY) 52 | walk_api_response = requests.get(walk_api_query) 53 | self.walk_scores.append(walk_api_response.content) 54 | transit_api_query = TRANSIT_BASE_URL.format(lat, lon, city, state, WSAPI_KEY) 55 | transit_api_response = requests.get(transit_api_query) 56 | self.transit_scores.append(transit_api_response.content) 57 | print "Querying row: {0}".format(count) 58 | count += 1 59 | 60 | def score_api_responses(self): 61 | walk_succeed_string = '"status": 1' 62 | transit_fail_string = "You must provide a valid 'city' and 'state'" 63 | for index, score in enumerate(izip(self.walk_scores, self.transit_scores)): 64 | status = {'walkscore_status': 1, 65 | 'transitscore_status': 1} # 1 --> status is good 66 | if walk_succeed_string not in score[0]: 67 | status['transitscore_status'] = 0 # status if bad 68 | if transit_fail_string in score[1]: 69 | status['transitscore_status'] = 0 70 | self.response_status[index] = status 71 | 72 | 73 | def get_api_report(self): 74 | self.score_api_responses() 75 | success_count = 0 76 | incomplete_count = 0 77 | for key, value in self.response_status.items(): 78 | if (value['transitscore_status'] == 0) or (value['walkscore_status'] == 0): 79 | incomplete_count += 1 80 | self.incompletes.append({key: value}) 81 | else: 82 | success_count += 1 83 | print "{0} API calls were successful".format(success_count) 84 | print "{0} API calls were incomplete or unsuccessful".format(incomplete_count) 85 | 86 | 87 | def get_and_format_data(self): 88 | 89 | def to_json(query_response): 90 | return json.loads(query_response.replace('\n', '')) 91 | 92 | key_ids = [] 93 | walkscore_descriptions = [] 94 | walkscore_scores = [] 95 | trans_descriptions = [] 96 | trans_summaries = [] 97 | trans_scores = [] 98 | for i in range(min([len(self.walk_scores), len(self.transit_scores)])): # take the smaller of the two lists 99 | key_id = self.df.ix[i][0] 100 | key_ids.append(key_id) 101 | try: 102 | walkscore_desc = to_json(self.walk_scores[i])['description'] 103 | except: 104 | walkscore_desc = 'None' 105 | walkscore_descriptions.append(walkscore_desc) 106 | try: 107 | walkscore_score = to_json(self.walk_scores[i])['walkscore'] 108 | except: 109 | walkscore_score = 'None' 110 | walkscore_scores.append(walkscore_score) 111 | try: 112 | trans_desc = to_json(self.transit_scores[i])['description'] 113 | except: 114 | trans_desc = 'None' 115 | trans_descriptions.append(trans_desc) 116 | try: 117 | trans_summ = to_json(self.transit_scores[i])['summary'] 118 | except: 119 | trans_summ = 'None' 120 | trans_summaries.append(trans_summ) 121 | try: 122 | trans_score = to_json(self.transit_scores[i])['transit_score'] 123 | except: 124 | trans_score = "None" 125 | trans_scores.append(trans_score) 126 | 127 | self.df_api = pd.DataFrame({'_id': key_ids, 128 | 'walkscore_desc': walkscore_descriptions, 129 | 'walkscore_score': walkscore_scores, 130 | 'trans_desc': trans_desc, 131 | 'trans_summary': trans_summaries, 132 | 'trans_score': trans_scores}, index=range(len(key_ids))) 133 | def update_mongo(self): 134 | print "Test!" 135 | client = MongoClient() 136 | db = client.updated_proj 137 | #self.df_final = pd.concat([self.df, self.df_api], axis=1, join='outer') 138 | for row in self.df_final.iterrows(): 139 | mongo_id = ObjectId(row[1][0]) 140 | trans_desc = row[1][57] 141 | trans_score = row[1][58] 142 | trans_summary = row[1][59] 143 | walkscore_desc = row[1][60] 144 | walkscore_score = row[1][61] 145 | 146 | #print mongo_id, trans_desc, trans_score, trans_summary, walkscore_desc, walkscore_score 147 | 148 | print "updating... ", mongo_id 149 | db.listings.update( 150 | {'_id': mongo_id}, 151 | { '$set': { "trans_desc": trans_desc, 152 | "trans_score": trans_score, 153 | "trans_summary": trans_summary, 154 | "walkscore_desc": walkscore_desc, 155 | "walkscore_score": walkscore_score 156 | }} 157 | ) 158 | 159 | 160 | -------------------------------------------------------------------------------- /code/pipeline/web_scraping.py: -------------------------------------------------------------------------------- 1 | import requests 2 | import requesocks 3 | import random 4 | import time 5 | from bs4 import BeautifulSoup as BS 6 | import re 7 | import json 8 | import pandas as pd 9 | 10 | class WebScraping(object): 11 | 12 | 13 | def __init__(self): 14 | self.urls = [] 15 | self.url_base = "http://www.zillow.com/homes/for_sale/{0}-{1}/{2}_p/" 16 | self.request_params = { 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_3)'} 17 | self.session_proxies = {'http': 'socks5://127.0.0.1:9150', # the default port for tor 18 | 'https': 'socks5://127.0.0.1:9150'} 19 | self.city_state = None 20 | self.html_content = {} # key=city_state, value=list of all html_source for a city_state 21 | self.df = None 22 | 23 | 24 | def whoami(self): 25 | """ 26 | This is a check to make sure the proxy is working. 27 | """ 28 | 29 | IPCHICKEN = "http://www.ipchicken.com" 30 | session = requesocks.session() 31 | session.proxies = self.session_proxies 32 | response = session.get(IPCHICKEN, headers=self.request_params) 33 | soup = BS(response.content, 'html.parser') 34 | return soup.find_all('p')[1].find('b').next.replace(' ', '').replace('\n', '') 35 | 36 | 37 | def build_urls(self, city, state, num_requests): 38 | self.city_state = "{0}-{1}".format(city, state).replace(' ', '-') 39 | for page_num in xrange(1, num_requests + 1): 40 | url = self.url_base.format(city, state, page_num) 41 | self.urls.append(url) 42 | 43 | 44 | def get_pages(self, sleep_time, proxy=False): 45 | # have to run tor before the proxy option will work 46 | self.html_content[self.city_state] = [] 47 | if proxy == True: 48 | session = requesocks.session() 49 | session.proxies = self.session_proxies 50 | for url in self.urls: 51 | time.sleep(sleep_time) 52 | response = session.get(url, headers=self.request_params) 53 | self.html_content[self.city_state].append(response.content) 54 | else: # the default is no proxy 55 | for url in self.urls: 56 | time.sleep(sleep_time) 57 | response = requests.get(url, headers=self.request_params) 58 | self.html_content[self.city_state].append(response.content) 59 | 60 | 61 | def extract_all(self, list_html_contents): 62 | df1 = self.extract_address_and_zpid(list_html_contents) 63 | df2 = self.extract_house_info(list_html_contents) 64 | df2 = df2.drop(['isPropertyTypeVacantLand', 'label', 'lot'], axis=1) 65 | df2 = df2.dropna(subset=['zpid']) 66 | df2['zpid'] = df2['zpid'].apply(lambda x: 'NA' if x=='' else x) 67 | df2 = df2[df2.zpid != 'NA'] 68 | df2 = df2.reset_index().drop('index', axis=1) 69 | df1['zpid'] = df1['zpid'].astype(str) 70 | df1['zpid'] = df1['zpid'].astype(int) 71 | df2['zpid'] = df2['zpid'].astype(str) 72 | df2['zpid'] = df2['zpid'].astype(int) 73 | merged = df2.merge(df1, on='zpid', how='left') 74 | merged = merged.dropna(subset=['address']) 75 | # removes bad addresses 76 | merged = merged[~(merged['address'].str.contains("XX"))] 77 | self.df = merged[~(merged['address'].str.contains("(Undisclosed-Address)"))] 78 | 79 | def extract_address_and_zpid(self, list_html_contents): 80 | ''' 81 | Input: a list of html_source for different web_pages 82 | 83 | Output: a pandas dataframe 84 | ''' 85 | homes = {} 86 | for html_doc in list_html_contents: 87 | soup = BS(html_doc, 'html.parser') 88 | indices = [m.start() for m in re.finditer('_zpid', html_doc)] 89 | zids = [] 90 | for index in indices: 91 | string = html_doc[index - 10: index] 92 | if "/" in string: 93 | string = string[string.find('/') + 1 : index] 94 | if string not in zids: 95 | zids.append(string) 96 | # builds a list of links from the html_source 97 | links = [res['href'] for res in soup.find_all('a', attrs={"href": True})] 98 | # Takes links and zids and returns the address of a specific zid 99 | for link in links: 100 | for zid in zids: 101 | if (zid in link) and ('homedetails' in link): 102 | if zid not in homes: 103 | start_index = link.find('/', 1) 104 | stop_index = link.find('/', start_index + 1) 105 | homes[zid] = link[start_index + 1 : stop_index] 106 | return pd.DataFrame({'zpid': homes.keys(), 'address': homes.values()}, index=range(len(homes))) 107 | 108 | 109 | def extract_house_info(self, list_html_contents): 110 | 111 | def find_nth(haystack, needle, n): 112 | # taken from http://stackoverflow.com/questions/1883980/find-the-nth-occurrence-of-substring-in-a-string 113 | start = haystack.find(needle) 114 | while start >= 0 and n > 1: 115 | start = haystack.find(needle, start+len(needle)) 116 | n -= 1 117 | return start 118 | 119 | def convert_price(row): 120 | price = row.replace('$', '') 121 | price = price.replace('.', '') 122 | try: 123 | if price[-1] == 'M': # add five zero's 124 | price = price.replace('M', '00000') 125 | return int(price) 126 | if price[-1] == 'K': # add three zero's 127 | price = price.replace('K', '000') 128 | return int(price) 129 | except: 130 | return price 131 | master_df = pd.DataFrame(columns=['bath', 'bed', 'isPropertyTypeVacantLand', 132 | 'label', 'lot', 'sqft', 'zpid', 'price']) 133 | for html in list_html_contents: 134 | res = [] 135 | num_minibubbles = html.count('minibubble') 136 | for n in xrange(1, num_minibubbles): 137 | info = {} 138 | start_body = find_nth(html, 'minibubble', n) 139 | stop_body = start_body + 500 140 | body = html[start_body : stop_body + 1] 141 | start = body.find('{') 142 | stop = body.find('}') 143 | minibubble = body[start : stop + 1] 144 | start_zpid = body.find('zpid_') 145 | stop_zpid = start_zpid + body[start_zpid:].find('"') 146 | zpid = body[start_zpid : stop_zpid] 147 | info = json.loads(minibubble.replace('\\', '')) 148 | info['zpid'] = zpid[5:] 149 | 150 | res.append(info) 151 | df = pd.DataFrame(res)[['bath', 'bed', 'isPropertyTypeVacantLand', 152 | 'label', 'sqft', 'zpid']] 153 | df['isVavcantLand'] = df['isPropertyTypeVacantLand'] 154 | df['price'] = df['label'].apply(convert_price) 155 | df = df.drop('label', axis=1) 156 | master_df = pd.concat([master_df, df]) 157 | 158 | return master_df 159 | 160 | -------------------------------------------------------------------------------- /data/seattle/lake-union-seattle-WA.csv: -------------------------------------------------------------------------------- 1 | ,bath,bed,isVavcantLand,price,sqft,zpid,address 2 | 0,2.0,3.0,False,575000.0,2800.0,2101020609,12043-33rd-Ave-NE-Seattle-WA-98125 3 | 1,3.0,2.0,False,100000.0,1311.0,48874923,6761-25th-Ave-SW-Seattle-WA-98106 4 | 2,1.75,3.0,False,325000.0,1110.0,88015163,3651-Whitman-Ave-N-Seattle-WA-98103 5 | 3,2.0,2.0,False,660000.0,1602.0,2101014651,1613-NW-65th-St-Seattle-WA-98117 6 | 5,3.5,4.0,False,10400000.0,2544.0,49041430,7309-19th-Ave-NW-Seattle-WA-98117 7 | 6,4.0,4.0,False,980000.0,2660.0,48804853,8824-39th-Ave-SW-Seattle-WA-98136 8 | 7,2.0,4.0,False,985000.0,3060.0,48942783,3423-W-Government-Way-APT-7-Seattle-WA-98199 9 | 8,2.0,2.0,False,360000.0,960.0,58391406,6561-5th-Ave-NE-APT-C-Seattle-WA-98115 10 | 9,2.0,2.0,False,485000.0,1188.0,48677170,2957-S-Dearborn-St-Seattle-WA-98144 11 | 10,2.0,3.0,False,530000.0,1410.0,49076193,2137-N-90th-St-Seattle-WA-98103 12 | 11,3.0,4.0,False,525000.0,1810.0,49013441,1719-Bigelow-Ave-N-Seattle-WA-98109 13 | 12,3.0,3.0,False,16000000.0,3050.0,48983690,2632-NW-North-Beach-Dr-Seattle-WA-98117 14 | 13,1.0,1.0,False,389000.0,610.0,49012240,1207-6th-Ave-N-Seattle-WA-98109 15 | 14,2.0,3.0,False,825000.0,1665.0,49123157,9056-4th-Ave-NW-Seattle-WA-98117 16 | 15,3.0,4.0,False,550000.0,1520.0,49031425,6505-Phinney-Ave-N-Seattle-WA-98103 17 | 16,3.0,5.0,False,11000000.0,3090.0,55791618,701-1st-Ave-N-APT-203-Seattle-WA-98109 18 | 18,3.0,3.0,False,400000.0,1875.0,49006344,2216-E-Calhoun-St-Seattle-WA-98112 19 | 19,3.0,5.0,False,14000000.0,3040.0,49115522,9702-13th-Ave-NW-Seattle-WA-98117 20 | 20,1.5,5.0,False,550000.0,2520.0,72500489,6300-Fauntleroy-Way-SW-UNIT-C-Seattle-WA-98136 21 | 21,2.0,3.0,False,387000.0,1150.0,89208364,2960-Eastlake-Ave-E-APT-201-Seattle-WA-98102 22 | 22,1.0,1.0,False,440000.0,788.0,72498667,1610-Belmont-Ave-APT-501-Seattle-WA-98122 23 | 23,2.0,2.0,False,515000.0,900.0,49147574,3811-E-Alder-St-Seattle-WA-98122 24 | 24,1.0,1.0,False,440000.0,788.0,72498667,1610-Belmont-Ave-APT-501-Seattle-WA-98122 25 | 25,2.0,2.0,False,515000.0,900.0,49053959,601-Belmont-Ave-E-APT-E5-Seattle-WA-98102 26 | 26,1.0,1.0,False,450000.0,806.0,80120162,120-NW-39th-St-UNIT-305-Seattle-WA-98107 27 | 27,1.0,2.0,False,339000.0,729.0,48798574,12753-6th-Ave-NW-Seattle-WA-98177 28 | 28,2.0,4.0,False,650000.0,2820.0,49073897,6033-41st-Ave-NE-Seattle-WA-98115 29 | 29,2.0,5.0,False,795000.0,2540.0,71551381,1610-Belmont-Ave-Seattle-WA-98122 30 | 30,2.0,2.0,False,515000.0,900.0,49001786,8631-45th-Ave-S-Seattle-WA-98118 31 | 31,1.0,5.0,False,480000.0,2156.0,48987082,11300-1st-Ave-NE-APT-303-Seattle-WA-98125 32 | 32,1.75,2.0,False,240000.0,1184.0,61718936,2716-Elliott-Ave-Seattle-WA-98121 33 | 33,2.0,2.0,False,700000.0,1503.0,50219428,5037-SW-Hanford-St-Seattle-WA-98116 34 | 34,2.25,3.0,False,759000.0,2200.0,2102458688,6527-4th-Ave-NE-B-Seattle-WA-98115 35 | 35,2.0,2.0,False,680000.0,1422.0,92570073,1920-4th-Ave-UNIT-2701-Seattle-WA-98101 36 | 36,2.5,3.0,False,33000000.0,2442.0,48991375,13734-25th-Ave-NE-Seattle-WA-98125 37 | 37,2.0,3.0,False,420000.0,1590.0,48919531,4114-Burke-Ave-N-Seattle-WA-98103 38 | 38,4.0,5.0,False,16900000.0,4000.0,48807566,12201-7th-Ave-NW-Seattle-WA-98177 39 | 39,3.0,4.0,False,595000.0,2310.0,82363950,76-Cedar-St-UNIT-101-Seattle-WA-98121 40 | 40,1.0,1.0,False,400000.0,894.0,117890166,2114-19TH-Ave-E-Seattle-WA 41 | 41,4.0,3.0,False,900000.0,2025.0,58383901,3613-22nd-Ave-W-A-Seattle-WA-98199 42 | 42,3.0,3.0,False,510000.0,1780.0,48835626,9737-Palatine-Ave-N-Seattle-WA-98103 43 | 43,3.0,6.0,False,799000.0,3080.0,2101028308,6404-22nd-Ave-NW-Seattle-WA-98107 44 | 44,4.0,3.0,False,680000.0,1502.0,2101028307,2052-NW-64th-St-Seattle-WA-98107 45 | 45,4.0,3.0,False,680000.0,1513.0,48671003,2614-NE-65th-St-Seattle-WA-98115 46 | 46,1.0,3.0,False,495000.0,1240.0,2101028537,4426-Montana-Cir-W-Seattle-WA-98199 47 | 47,3.0,3.0,False,799000.0,1674.0,2111361697,7012-Holly-Park-Dr-S-Seattle-WA-98118 48 | 48,1.75,2.0,False,428000.0,1045.0,48749870,2518-Nob-Hill-Pl-N-Seattle-WA-98109 49 | 49,2.0,4.0,False,965000.0,2750.0,2101028984,1906-Franklin-Place-E-203-Seattle-WA-98102 50 | 50,3.0,3.0,False,690000.0,1833.0,2101029232,1762-NW-61st-St-A-Seattle-WA-98107 51 | 51,2.0,3.0,False,690000.0,1668.0,48952913,9317-42nd-Ave-NE-Seattle-WA-98115 52 | 52,3.0,4.0,False,705000.0,1880.0,49121373,10203-47th-Ave-SW-APT-C15-Seattle-WA-98146 53 | 53,2.0,2.0,False,449000.0,1144.0,82363367,2720-3rd-Ave-APT-504-Seattle-WA-98121 54 | 54,2.0,1.0,False,685000.0,1112.0,2101029374,7021-Sand-Point-Way-NE-UNIT-B107-Seattle-WA-98115 55 | 55,1.0,1.0,False,179000.0,702.0,2101029369,10429-39th-Ave-SW-Seattle-WA-98146 56 | 56,3.0,4.0,False,800000.0,3122.0,82362566,500-5th-Ave-W-UNIT-304-Seattle-WA-98119 57 | 57,2.0,2.0,False,439000.0,915.0,48995752,923-N-81st-St-Seattle-WA-98103 58 | 58,1.0,2.0,False,439000.0,670.0,49041963,342-NW-76th-St-Seattle-WA-98117 59 | 59,2.0,3.0,False,685000.0,1960.0,48804208,4040-SW-Concord-St-Seattle-WA-98136 60 | 60,2.0,4.0,False,650000.0,2140.0,48795608,1917-4th-Ave-W-Seattle-WA-98119 61 | 61,3.0,4.0,False,17900000.0,4250.0,48866533,5508-S-Wallace-St-Seattle-WA-98178 62 | 62,3.0,5.0,False,399000.0,2800.0,2132682710,4743-Brooklyn-Ave-NE-Seattle-WA-98105 63 | 63,6.0,4.0,False,18900000.0,4600.0,48657902,505-E-Denny-Way-A405-Seattle-WA-98122 64 | 64,1.0,1.0,False,250000.0,548.0,2101029773,Undisclosed-Seattle-WA-98125 65 | 65,1.0,1.0,False,155000.0,680.0,48946611,9220-20th-Ave-NE-Seattle-WA-98115 66 | 66,1.0,2.0,False,395000.0,917.0,2101029786,513-20th-Ave-E-C-Seattle-WA-98112 67 | 67,2.0,3.0,False,850000.0,1607.0,49041739,6531-22nd-Ave-NW-Seattle-WA-98117 68 | 68,1.0,4.0,False,523000.0,1180.0,48839957,10735-63rd-Ave-S-Seattle-WA-98178 69 | 69,1.0,2.0,False,299000.0,1000.0,2101030638,9506-Ravenna-Ave-NE-APT-305-Seattle-WA-98115 70 | 70,1.0,1.0,False,185000.0,677.0,2101031081,7012-Holly-Park-Dr-S-UNIT-V-3-Seattle-WA-98118 71 | 72,3.0,3.0,False,700000.0,1664.0,2101031353,1762A-NW-61st-St-Seattle-WA-98107 72 | 73,2.0,3.0,False,690000.0,1668.0,2101031502,1146-10th-Ave-E-A-Seattle-WA-98102 73 | 74,3.0,3.0,False,900000.0,1775.0,2101031506,1140-10th-Ave-E-A-Seattle-WA-98102 74 | 75,3.0,2.0,False,940000.0,1900.0,48778172,7519-11th-Ave-SW-Seattle-WA-98106 75 | 76,1.75,3.0,False,400000.0,1276.0,48947215,10309-23rd-Ave-NE-Seattle-WA-98125 76 | 77,1.75,2.0,False,480000.0,1800.0,48791312,3278-40th-Ave-SW-Seattle-WA-98116 77 | 78,4.0,5.0,False,995000.0,4140.0,48754269,2125-1st-Ave-APT-1103-Seattle-WA-98121 78 | 79,1.0,1.0,False,589000.0,820.0,48996091,350-N-84th-St-Seattle-WA-98103 79 | 80,1.0,2.0,False,500000.0,1020.0,2101032480,7000-Cleopatra-Place-Nw-202-Seattle-WA-98117 80 | 81,1.0,1.0,False,230000.0,576.0,2101032649,1760-NW-61st-St-Seattle-WA-98107 81 | 82,2.0,3.0,False,710000.0,1658.0,49137089,3710-27th-Pl-W-APT-203-Seattle-WA-98199 82 | 83,1.0,1.0,False,185000.0,680.0,117745643,9254-36th-Ave-SW-Seattle-WA-98126 83 | 84,4.0,3.0,False,597000.0,2075.0,2109644889,626-13th-Ave-E-APT-L-Seattle-WA-98102 84 | 85,1.0,2.0,False,349000.0,790.0,48863928,3320-S-Holly-St-Seattle-WA-98118 85 | 86,5.25,5.0,False,575000.0,4129.0,48945865,1039-NE-91st-St-Seattle-WA-98115 86 | 87,2.0,3.0,False,675000.0,2220.0,48978213,1415-2nd-Ave-UNIT-2106-Seattle-WA-98101 87 | 88,1.0,1.0,False,450000.0,670.0,48789217,814-19th-Ave-Seattle-WA-98122 88 | 89,4.0,8.0,False,970000.0,4200.0,48990509,4027-51st-Ave-SW-Seattle-WA-98116 89 | 90,1.0,2.0,False,399000.0,870.0,80124260,5416-Fauntleroy-Way-SW-UNIT-A-Seattle-WA-98136 90 | 91,2.5,2.0,False,415000.0,1202.0,2101036804,9401-Lake-City-Way-NE-Seatac-WA-98115 91 | 92,,,True,500000.0,,80260309,1618-E-Fir-St-Seattle-WA-98122 92 | 93,3.0,3.0,False,499000.0,1430.0,2101037583,10544-Midvale-Ave-N-Seattle-WA-98133 93 | 94,2.5,3.0,False,380000.0,1600.0,48946068,1226-NE-92nd-St-Seattle-WA-98115 94 | 95,2.0,3.0,False,579000.0,1920.0,49102562,6016-38th-Ave-NE-Seattle-WA-98115 95 | -------------------------------------------------------------------------------- /data/seattle/mercer-island-WA.csv: -------------------------------------------------------------------------------- 1 | ,bath,bed,isVavcantLand,price,sqft,zpid,address 2 | 0,3.0,3.0,False,800000.0,2940.0,2106295817,2940-76th-Ave-SE-Mercer-Island-WA-98040 3 | 1,2.0,3.0,False,360000.0,1434.0,48885041,8750-Island-Heights-Ln-Mercer-Island-WA-98040 4 | 2,2.5,4.0,False,11000000.0,3970.0,48854128,6240-E-Mercer-Way-Mercer-Island-WA-98040 5 | 3,3.0,2.0,False,995000.0,2490.0,48966076,8112-SE-44th-St-Mercer-Island-WA-98040 6 | 4,3.25,6.0,False,17900000.0,4100.0,48941984,3610-88th-Ave-SE-Mercer-Island-WA-98040 7 | 5,3.0,4.0,False,898000.0,1870.0,48691475,9950-SE-35th-Pl-Mercer-Island-WA-98040 8 | 6,4.0,4.0,False,63000000.0,5000.0,48810926,9611-SE-72nd-St-Mercer-Island-WA-98040 9 | 7,4.0,4.0,False,25000000.0,3440.0,96708634,7820-W-Mercer-Way-Mercer-Island-WA-98040 10 | 8,,,True,150000.0,,48918825,5047-84th-Ave-SE-Mercer-Island-WA-98040 11 | 9,3.5,3.0,False,16000000.0,2810.0,48782752,3010-61st-Ave-SE-Mercer-Island-WA-98040 12 | 10,5.0,5.0,False,31900000.0,5800.0,48963083,8602-N-Mercer-Way-Mercer-Island-WA-98040 13 | 11,3.0,5.0,False,20000000.0,3540.0,48767892,6226-E-Mercer-Way-Mercer-Island-WA-98040 14 | 12,3.0,3.0,False,24000000.0,2400.0,48830353,8041-SE-71st-St-Mercer-Island-WA-98040 15 | 13,3.0,5.0,False,11500000.0,3044.0,49129251,8383-SE-50th-Pl-Mercer-Island-WA-98040 16 | 14,3.5,5.0,False,19900000.0,4740.0,48927307,7930-SE-34th-St-APT-401-Mercer-Island-WA-98040 17 | 15,4.0,2.0,False,500000.0,1797.0,49052978,4546-Forest-Ave-SE-Mercer-Island-WA-98040 18 | 16,1.75,3.0,False,14800000.0,1950.0,48955987,7228-SE-24th-St-Mercer-Island-WA-98040 19 | 17,5.0,5.0,False,27000000.0,5820.0,48783680,4345-Fernbrook-Dr-Mercer-Island-WA-98040 20 | 18,2.5,3.0,False,11500000.0,2580.0,2101320961,4703-88th-Ave-SE-Mercer-Island-WA-98040 21 | 19,,,True,600000.0,,48944990,2521-68th-Ave-SE-Mercer-Island-WA-98040 22 | 20,2.75,3.0,False,12000000.0,2400.0,48677129,6860-W-Mercer-Way-Mercer-Island-WA-98040 23 | 21,4.0,4.0,False,17500000.0,4360.0,48885155,3649-W-Mercer-Way-Mercer-Island-WA-98040 24 | 22,2.0,3.0,False,31900000.0,2140.0,49139382,3236-80th-Ave-SE-APT-3-Mercer-Island-WA-98040 25 | 23,2.0,3.0,False,450000.0,1375.0,48782356,4807-E-Mercer-Way-Mercer-Island-WA-98040 26 | 24,,,True,599000.0,,49095545,5330-Butterworth-Rd-Mercer-Island-WA-98040 27 | 25,9.0,5.0,False,26800000.0,14940.0,48994241,8231-SE-33rd-Pl-Mercer-Island-WA-98040 28 | 26,3.0,4.0,False,12700000.0,2874.0,2101427541,0-SE-64th-St-Mercer-Island-WA-98040 29 | 27,,,True,575000.0,,2101427540,0-E-Mercer-Way-Mercer-Island-WA-98040 30 | 30,,,True,848000.0,,48766902,9407-SE-52nd-St-Mercer-Island-WA-98040 31 | 31,4.5,4.0,False,19500000.0,5000.0,2101459260,3055-80th-Ave-Se-Ave-SE-301-Mercer-Island-WA-98040 32 | 32,1.0,2.0,False,324000.0,,48783030,3021-70th-Ave-SE-Mercer-Island-WA-98040 33 | 33,3.0,4.0,False,14800000.0,2700.0,48886122,3055-80th-Ave-SE-APT-301-Mercer-Island-WA-98040 34 | 34,1.0,2.0,False,324000.0,850.0,48782817,2931-71st-Ave-SE-Mercer-Island-WA-98040 35 | 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101,2.0,4.0,False,400000,1760.0,48839768,16256-9th-Ave-SW-Burien-WA-98166 91 | 102,2.0,2.0,False,259000,1220.0,48935088,2136-SW-167th-St-Burien-WA-98166 92 | 103,4.0,5.0,False,750000,2476.0,49049008,16527-Maplewild-Ave-SW-Burien-WA-98166 93 | 105,1.0,2.0,False,384000,1660.0,49054880,1445-SW-129th-St-Burien-WA-98146 94 | 106,3.25,5.0,False,698000,3620.0,48817070,3774-SW-171st-St-Burien-WA-98166 95 | 107,3.5,3.0,False,20000000,3360.0,48734129,13417-4th-Ave-S-Burien-WA-98168 96 | 109,1.0,2.0,False,246000,830.0,48773714,449-S-189th-St-Seattle-WA-98148 97 | 110,2.0,4.0,False,256000,1460.0,2102663793,134-S-163rd-Ln-Burien-WA-98148 98 | 111,4.0,4.0,False,490000,2174.0,49056324,12109-24th-Pl-SW-Burien-WA-98146 99 | 112,2.5,4.0,False,10000000,3270.0,49022077,111-SW-130th-St-Burien-WA-98146 100 | 113,3.0,5.0,False,300000,1740.0,48761183,1128-SW-134th-St-Burien-WA-98146 101 | 114,3.0,8.0,False,489000,3600.0,48754699,14970-5th-Ave-S-Burien-WA-98168 102 | 115,1.0,4.0,False,329000,2010.0,48713310,11906-23rd-Ave-SW-Burien-WA-98146 103 | 117,2.0,3.0,False,199000,1036.0,48944412,256-S-182nd-St-Burien-WA-98148 104 | 118,1.5,4.0,False,290000,1644.0,48935146,13823-1st-Ave-SW-Burien-WA-98166 105 | 120,1.75,3.0,False,294000,1560.0,2103361104,16821-33rd-Ave-SW-Burien-WA-98166 106 | 121,,,True,175000,,49049453,2526-SW-146th-Ln-Burien-WA-98166 107 | 122,4.0,5.0,False,975000,3900.0,49042245,11901-Ambaum-Blvd-SW-Burien-WA-98146 108 | 124,,0.0,True,149000,,48712645,148-SW-157th-St-Burien-WA-98166 109 | 125,,,True,310000,484.0,48944149,450-S-181st-St-Burien-WA-98148 110 | 128,1.5,3.0,False,296000,1670.0,82362610,11801-Ambaum-Blvd-SW-UNIT-1-Burien-WA-98146 111 | 130,1.75,3.0,False,275000,1370.0,48712420,15612-8th-Ave-SW-Burien-WA-98166 112 | 133,1.0,2.0,False,276000,1520.0,48734156,13012-3rd-Ave-S-Burien-WA-98168 113 | 135,,0.0,True,225000,,48751897,13230-14th-Ave-S-Burien-WA-98168 114 | 136,1.75,3.0,False,190000,,96693815,14462-18TH-Ave-SW-Burien-WA-98166 115 | 139,1.0,3.0,False,275000,1270.0,48657995,17404-Ambaum-Blvd-S-APT-203-Seattle-WA-98148 116 | 141,1.75,2.0,False,259000,1160.0,49048969,17011-35th-Ave-SW-Burien-WA-98166 117 | 145,1.5,3.0,False,278000,1560.0,48774694,648-S-159th-St-Burien-WA-98148 118 | 146,1.0,1.0,False,169000,880.0,48751845,13523-14th-Ave-S-Burien-WA-98168 119 | 147,2.5,5.0,False,333000,2850.0,48893582,221-SW-154th-St-APT-1-Burien-WA-98166 120 | 148,1.0,1.0,False,100000,768.0,49144733,18323-Occidental-Ave-S-Burien-WA-98148 121 | -------------------------------------------------------------------------------- /code/learn_preferences.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | from sklearn.metrics.pairwise import pairwise_distances 4 | from scipy.spatial.distance import cosine 5 | from scipy.stats import beta 6 | from pre_processing import PreProcess 7 | from image_getter import GetImage 8 | import re 9 | import sys 10 | import os 11 | import matplotlib.pyplot as plt 12 | import seaborn as sns 13 | sns.set(color_codes=True) 14 | 15 | 16 | 17 | class LearnPreferences(object): 18 | 19 | """ 20 | Implements a multi-armed bayeseian bandit to learn a users preferences in houses. 21 | """ 22 | 23 | def __init__(self, df1, df2, df1_raw, df2_raw, ref_listing, num_matches=1): 24 | """ 25 | Input: two pre-processed dataframe obejcts, list of metrics (e.g. 'walk_distance', 'space_distance'), 26 | the integer index of the reference listing, the number of matches to return per metric, 27 | e.g. num_mathces=1 means that the recommender will return two listings at a time 28 | """ 29 | self.ref_listing = ref_listing # the seed house that all other house are comapred to 30 | self.df1_raw = df1_raw.reset_index() 31 | self.df2_raw = df2_raw.reset_index() 32 | self.orginal_df1 = df1.reset_index() 33 | self.orginal_df2 = df2.reset_index() 34 | self.df1 = df1.reset_index().ix[self.ref_listing] 35 | self.df2 = df2.reset_index() 36 | self.num_matches = num_matches # the number of mathces to return 37 | self.recommendations = None 38 | self.recommendation_history = {} 39 | self.pairs_served = 0 40 | self.metrics = ['walk_distance', 'space_distance', 'family_distance'] 41 | self.scores = {} 42 | self.params = {} 43 | self.init_scores_and_params() # populates self.scores with a zero for each of the different distance metrics 44 | self.listings_served = set() 45 | self.current_pairs = None 46 | self.sim_mat = self.update_similarity_matrix('cosine') 47 | 48 | 49 | def init_scores_and_params(self): 50 | for metric in self.metrics: 51 | if metric not in self.scores: 52 | self.scores[metric] = 0 53 | if metric not in self.params: 54 | self.params[metric] = (0, 0) 55 | 56 | 57 | def update_similarity_matrix(self, distance_metric): 58 | 59 | self.sim_mat = pairwise_distances(self.df1, self.df2, metric=distance_metric, n_jobs=-1) 60 | return self.sim_mat 61 | 62 | 63 | def update_recommendations(self): 64 | """ 65 | Note: 66 | """ 67 | if self.pairs_served < 1: 68 | self.recommendations = {} 69 | for metric in self.metrics: 70 | self.df1 = PreProcess(self.df1).filter_df(metric) 71 | self.df2 = PreProcess(self.df2).filter_df(metric) 72 | self.update_similarity_matrix('euclidean') 73 | # reset the df to their orignal version for the next iteration 74 | self.df1 = self.orginal_df1 75 | self.df2 = self.orginal_df2 76 | self.recommendations[metric] = np.argsort(self.sim_mat[0])[-(self.num_matches):].tolist() 77 | return self.recommendations 78 | 79 | def get_recommendation(self, metric): 80 | 81 | """ 82 | Input: similarity matrix with first arg of parwise distances as rows and 83 | second arg of pairwise distances as columns, the integer index of the 84 | listing you want to compare other listings to, int for the num of listings 85 | to return. 86 | 87 | Output: an numpy array with the indices of the listings that 88 | are most similar to the ref_listing. 89 | """ 90 | # draw an element at random from the recommendations list 91 | recommendation = np.random.choice(self.recommendations[metric]) 92 | # remove the element from recommendations 93 | index = self.recommendations[metric].index(recommendation) 94 | self.recommendations[metric].pop(index) 95 | return recommendation 96 | 97 | 98 | def choose_models(self): 99 | # choose two of the available models, where one is the best estimate of the users preference 100 | # and the other is randomly chosen of the remaining metrics 101 | 102 | # assign the best guess to a list 103 | if self.pairs_served > 0: 104 | best_guess = self.recommendation_history[self.pairs_served]['estimated_user_preference'] 105 | metrics = [best_guess] 106 | remaining_metrics = list(self.metrics) # make a copy of the list, so the original is not modified 107 | remaining_metrics.pop(remaining_metrics.index(best_guess)) # remove the best guess, since it's already in metrics 108 | metrics.append(np.random.choice(remaining_metrics)) # randomly choose the other metric 109 | np.random.shuffle(metrics) # shuffle the metrics, so the best guess recommendation is not always the first one presented 110 | else: 111 | # this is the starting point and these is no best guess of the best metric 112 | metrics = np.random.choice(self.metrics, 2, replace=False) 113 | return metrics 114 | 115 | 116 | def show_recommendations(self): 117 | sample_metrics = self.choose_models() 118 | recommendations = [] 119 | for metric in sample_metrics: 120 | # get a recommednation from each of the metrics in this iteration 121 | recommendations.append(self.get_recommendation(metric)) 122 | self.current_pairs = recommendations 123 | left = self.current_pairs[0] 124 | right = self.current_pairs[1] 125 | return self.df2_raw[['city', 'state', 'street', 'finishedsqft', 126 | 'bedrooms', 'bathrooms', 'trans_score', 127 | 'walkscore_score', 'price']].ix[[left, right]].T 128 | 129 | 130 | 131 | def get_user_choice(self, user_choice): 132 | 133 | """ 134 | Input: a dataframe for each of the cities 135 | Output: the recommendation corresponding to the user choice 136 | """ 137 | 138 | sample_metrics = self.choose_models() 139 | 140 | if user_choice == "l": 141 | self.scores[sample_metrics[0]] += 1 142 | winner = self.current_pairs[0] 143 | else: 144 | self.scores[sample_metrics[1]] += 1 145 | winner = self.current_pairs[1] 146 | 147 | #self.user_choices.append(user_choice) 148 | self.pairs_served += 1 149 | self.update_recommendation_history(self.current_pairs, winner) 150 | 151 | 152 | def update_recommendation_history(self, recommendations, winner): 153 | self.recommendation_history[self.pairs_served] = {'pairs_served': recommendations, 154 | 'winner': winner} 155 | 156 | def guess_preferences(self): 157 | """ 158 | Input: no inputs 159 | Output: no outputs 160 | Notes: this function will take the updated score for each metric, compute a 161 | beta distribution defined by the win/loss scores, sample from each distribution 162 | and return the metric that corresponds to the greatest probability. The winning 163 | metric is added to recommendation_history as the best guess of user preference. 164 | """ 165 | user_preference = None 166 | max_prob = 0 167 | for metric in self.metrics: 168 | self.params[metric] = (self.scores[metric] + 1, self.pairs_served - self.scores[metric] + 1) 169 | prob = beta.rvs(self.params[metric][0] + 1, self.params[metric][1] + 1) # sample form the dist for each metric 170 | if prob > max_prob: 171 | max_prob = prob 172 | user_preference = metric 173 | self.recommendation_history[self.pairs_served]['estimated_user_preference'] = user_preference 174 | 175 | def generate_images(self): 176 | fig = plt.figure() 177 | for metric in self.metrics: 178 | x = beta(self.params[metric][0] + 1, self.params[metric][1] + 1).rvs(size=1000) 179 | sns.kdeplot(x, shade=True, label=metric) 180 | fig.suptitle("Liklihood that you belong to a segment", fontsize=20) 181 | plt.legend(fontsize=16, loc='upper left') 182 | plt.ylabel('Likelihood', fontsize=16) 183 | plt.xlabel('Choice Preference', fontsize=16) 184 | plt.savefig("prob_dist.png", dpi=600) 185 | 186 | 187 | def fetch_content(self, listing_index): 188 | image_getter = GetImage() 189 | image = image_getter.get_image(self.df2_raw.ix[listing_index]) 190 | address = self.df2_raw.ix[listing_index][5] 191 | bedroom = self.df2_raw.ix[listing_index][11] 192 | bathroom = self.df2_raw.ix[listing_index][12] 193 | sqft = self.df2_raw.ix[listing_index][16] 194 | walkscore = self.df2_raw.ix[listing_index][12] 195 | transcore = self.df2_raw.ix[listing_index][12] 196 | school = self.df2_raw.ix[listing_index][12] 197 | print address, bedroom, bathroom, sqft, walkscore, transcore, school 198 | 199 | 200 | 201 | 202 | #if __name__ == "__maine__": 203 | 204 | # df = pd.read_csv('trx_seattle.csv', index_col=0) # this df contains the untransformed listing data 205 | # my_past_house = df1.ix[0] # user has previously lived in house with index 0 206 | # user_session = LearnPreferences(df1, df2) # init object and a similarity matrix 207 | # recommendations = user_session.get_most_similar(user_session.sim_mat, my_past_house, 4) # provides the 4 most similar listings 208 | # choice = get_user_choice(reccomendations) 209 | # update_user_history(recommendations) -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Personalized Real Estate Recommender 2 | 3 | 4 | ## Introduction 5 | 6 | This is the real estate recommendation system that I built for my final project during the Galvanize Data Science Intensive. 7 | 8 | The genesis of this project was to assist me in finding a home. I moved to Seattle in early July and knew nothing of the local area, let alone where I should consider moving my family after I finished school. Sites like Zillow and Trulia are useful for getting access to enormous amounts of information but they create a new problem of having to sift through thousands of choices to find the right place. I find this a tedious process. 9 | 10 | I have always been impressed by how well sites like Amazon and Netflix make recommendations for books and content that I may like. It saves the trouble of sifting through countless options and I am usually pleased with the results of their recommendations! 11 | 12 | I wondered why there couldn't be something similar for finding a place to live? I also assumed that I am not the only one who has the same problem, so I attempted to generalize my solution such that it could benefit a larger group of people in finding a home. 13 | 14 | In short, this recommender is my first cut at solving the problem of helping a person who is moving from one city to another, find the RIGHT place to live. 15 | 16 | ## The Code 17 | Here is a diagram of my repo and a brief 10,000 ft overview of what each of the files does: 18 | 19 | **code** 20 | 21 | 1. `image_getter.py` - class for easily getting an image url for a Zillow listing 22 | 2. `learn_preferences.py` - the guts of the recommender system 23 | 3. `load_transform.py` - class for loading data from MongoDB and transforming it for processing 24 | 4. `pre_processing.py` - class to transform, normalize, and prep data for the recommender 25 | 5. **pipeline** 26 | 1. `get_schools.py` - a class to scrape school data for a given listing and transform it to a Pandas df 27 | 2. `walkscore_api.py` - a class to query WalkScore for walkability and public transit data, and update MongoDB 28 | 3. `web_scraping.py` - a class that streamlines scraping Zillow search results to obtain addresses of listings 29 | 4. `zillow_api.ipynb` - an iPython notebook that I use for querying the Zillow API, need to convert to a script 30 | 31 | **data** 32 | 33 | 1. `san_fran.csv` - a csv of zillow listings that I scraped 34 | 2. `seattle_schools.txt` - a list of all Seattle area schools and their scores from 1-10 35 | 3. **seattle** 36 | 1. `seattle.csv` - a csv of aggregated zillow listings that I queried through the API 37 | 2. `bellevue-WA.csv` - a csv of address and home ids that I scraped from Zillow search results 38 | 3. `wallingford-seattle-WA.csv` - a csv of address and home ids that I scraped 39 | 4. etc, other csv files for each of the major neighborhoods and surrounding towns 40 | 41 | ### Overview 42 | 43 | This recommender system is -- at the core -- a content based information retrieval system. Recommendations are made based on the notion of computing a measure of similarity between different listings. A content based recommender is different from a collaborative based recommender because the latter relies on the ratings of other users to make recommendations to a new user. 44 | 45 | ### Problem 1 - The Cold Start Problem 46 | Since I have no historical data on any users -- a problem known as the "Cold Start Problem" -- I decided to tackle the problem by using a known house that I liked from a previous city as a "seed" for the recommender. From this initial seed, recommendations are served using different measures of similarity -- or distance metrics. 47 | 48 | ### Problem 2 - How do different people value houses? 49 | The use of different distance metrics is important because the most similar houses to the seed will vary wildly based on how distance is computed. This idea captures the notion that different people value attributes of houses in different ways. For example, a single young professional will be more interested in walkability and nightlife, and less interested in schools and the size of their yard than would a married couple with children. 50 | 51 | ### Problem 3 - How does a given user value a house? 52 | This leads into the problem of how to choose which distance metric is best for a particular user so that the system can keep serving them relevant recommendations. To solve this problem, I framed the problem like an AB test. Instead of testing multiple versions of a webpage, I am testing multiple ways to suggest housing recommendations. Additionally, I also show the same user multiple versions of "similar houses" using different distance metrics in the assumption that their feedback will provide enough data to learn which version of providing recommendations is best for them. 53 | 54 | In order to make sure that relevant recommendations are served while also determining which method of making recommendations is best, I decided to implement a greedy algorithm -- specifically the Bayesian Multi-Armed Bandit (MAB). 55 | 56 | ## How the recommender works 57 | ![alt text](https://github.com/MichaelAHood/real_estate_recommender/blob/master/data/algorithm.png) 58 | 59 | Recommendations are shown two at a time and the user is able to pick the one they like best. The user's choice is recorded and then used to update a probabilistic "guess" of what measure of similarity is providing the best recommendations for that user. I am intent on the idea of only showing listings two at a time for one particular reason -- humans are notoriously bad at making value judgements from multiple choices when the number of choices exceeds four to five. We are, however, exceptionally good at making pairwise value comparisons. In general, people can quickly take a look at two things and tell you which is better or more preferable. The downside to this approach is that the user may have to choose listings over a large number of iterations of the algorithm before a high degree of confidence is obtained for the best distance metric. 60 | 61 | ## Obtaining the Data 62 | Starting this project I naively assumed that I could easily come across detailed information about large numbers of houses in a given city. This assumption turned out to not be true. Sights like Zillow and Trulia provide fairly detailed information about listings, but the number of listings that they share is extremely limited -- approximately 70% of all Zillow istings are proteced and available to query through the API. 63 | 64 | Below is a description about how I aquired the data for my recommender. 65 | 66 | 1. **Zillow** 67 | 1. **Scraping** - Zillow has an easy to use API, but the precondition for using the API is that you already have to know the Zillow ID (ZPID) of the house you want to query. Since I wanted all of the houses in Seattle and San Francisco, I needed to find a way to automatically construct a list of address and ZPIDs. 68 | 69 | Thankfully, the search results for a Zillow query contain a list of houses with urls. I found that I could parse the html source for a Zillow query to find the links and retreive the addresses and ZPIDs from the url itself. I simply had to construct a url in the form that would return a zillow search results page for a given city, parse the html and repeat, until I had enough listing addresses and ZPIDs. 70 | 71 | To facilitte this process, I wrote the `web_scraping.py` class. Creating a `WebScraping()` object and using the associated methods -- including a proxy method that allows the use of tor -- makes this process go relatively smoothly. There is also a paramter to set sleep times between GET requests to avoid making too many requests in a short time and gettin blocked. Despite that, Zillow will eventually catch on and start serving captchas, so I would have to inititalize a new tor circuit and create a new session to resume where I left off, when I was blocked. 72 | 73 | After scraping both the Seattle and San Francisco Bay areas, I ended up with about 2,000 listings per region, which I wrote to a `.csv` file. 74 | 75 | 2. **API** - With the ZPIDs and associated addresses, I was able to construct a series of functions to query the Zillow API. I prototyped this code in an iPython notebook -- and have not yet got around to writing a proper script or class. There are also several other fucntions to parse the API results, structure it, and insert it into a MongoDB collection. 76 | 77 | The Zillow API has a daily query limit of 1,000 queries, so I was only able to query about half of the listings for a city each day. This is also when I discovered that about 65-70% of the addresses are not available through the API, so after two days of running API queries, I only ended up with about 700 results of the 2,000 I asked for. 78 | 79 | 2. **WalkScore** 80 | 1. **API** - I came across WalkScore when I was trying to find a way to capture information about the qualities of a neighborhood, such as access to public transit, nearness of grocery stores, restaurants, parks, greenspace, etc. The API returns an 100 point based index for a `Walk_Score` as well as a `Transit_Score` that quantifies many of the above attributes. 81 | 82 | To facilitate this process, I wrote a class called `walkscore_api.py`. 83 | 84 | 3. **Noddle** 85 | 1. **Scraping** - I was determined to find a way to assess the quality of the schools for a given address. Unfortunately, the vast majority of Zillow API data contains no information about the schools that are near a given listing. To solve this problem, I did two things: 86 | 1. I found a list of all schools for a given district (thanks again, Zillow!) and manually copied and pasted them into a .txt file because the html source was unscrapable. I think the reason for this is that the information was some sort of dynamically generated content that did not show up in the source html. With all of the school names and scores (1 - 10) in a `.txt` file, I read it into python and parsed the text to build a dictionary with schools as keys and their scores as values. 87 | 2. Next, I found a site called Noodle.com that will return a list of all schools within a certain distance around a house. Fortunately, this site was scrapable! I wrote another class -- `get_schools.py` -- to iterate through the `.csv`, construct a url with which I can then request the html source from noodle, scrape it and extract the occurences of the schools in schools dict, and aggregate the scores of all of the schools within 5 miles. Using this method I was able to compute an aggregate school index for every single address. 88 | 89 | 90 | 91 | -------------------------------------------------------------------------------- /data/seattle/issaquah-WA.csv: -------------------------------------------------------------------------------- 1 | ,bath,bed,isVavcantLand,price,sqft,zpid,address 2 | 0,4.0,4.0,False,627000.0,2172.0,48935813,18868-SE-42nd-St-Issaquah-WA-98027 3 | 2,2.5,4.0,False,805000.0,3016.0,48886415,1506-NE-Iris-St-Issaquah-WA-98029 4 | 3,4.0,4.0,False,789000.0,2850.0,2137317896,1622-Huckleberry-Cir-Issaquah-WA-98029 5 | 4,3.0,2.0,False,395000.0,1476.0,48666874,24013-SE-103rd-Pl-Issaquah-WA-98027 6 | 5,2.0,4.0,False,495000.0,2173.0,48904761,25210-SE-35th-St-Issaquah-WA-98029 7 | 6,3.0,3.0,False,596000.0,1910.0,84762535,700-Front-St-S-Issaquah-WA-98027 8 | 7,1.0,2.0,False,245000.0,911.0,48792449,24607-SE-153rd-Pl-Issaquah-WA-98027 9 | 8,3.0,3.0,False,510000.0,2540.0,48905145,24939-SE-42nd-Dr-Issaquah-WA-98029 10 | 9,3.0,3.0,False,498000.0,1530.0,72495510,1729-11th-Ave-NE-Issaquah-WA-98029 11 | 11,4.0,4.0,False,820000.0,2347.0,49078832,23975-SE-41st-Pl-Issaquah-WA-98029 12 | 12,2.5,4.0,False,689000.0,2410.0,48968423,26609-SE-158th-St-Issaquah-WA-98027 13 | 13,2.5,3.0,False,850000.0,4330.0,2101034397,4642-238th-Way-SE-LOT-7-Issaquah-WA-98029 14 | 14,3.5,3.0,False,668000.0,2193.0,2101034401,4634-238th-Way-SE-LOT-8-Issaquah-WA-98029 15 | 15,3.5,3.0,False,665000.0,2193.0,48752124,13228-202nd-Ave-SE-Issaquah-WA-98027 16 | 16,2.75,3.0,False,14000000.0,3171.0,49016269,23842-SE-45th-St-Issaquah-WA-98029 17 | 18,3.0,3.0,False,507000.0,1467.0,63335996,23120-SE-Black-Nugget-Rd-UNIT-F5-Issaquah-WA-98029 18 | 19,2.5,3.0,False,425000.0,1471.0,2101044217,1416-14th-Pl-NE-Issaquah-WA-98029 19 | 21,2.5,5.0,False,810000.0,2980.0,2101051289,26255-SE-42nd-St-Issaquah-WA-98029 20 | 22,4.0,4.0,False,16200000.0,4491.0,2101065247,27240-SE-Grand-Ridge-Dr-Issaquah-WA-98029 21 | 23,,,True,495000.0,,2101065246,27243-SE-Grand-Ridge-Dr-Issaquah-WA-98029 22 | 24,3.0,3.0,False,575000,1804.0,49133308,975-Wildwood-Blvd-SW-Issaquah-WA-98027 23 | 25,3.0,4.0,False,599000,2090.0,2101077537,22223-SE-42nd-Ln-BLDG-26-Issaquah-WA-98029 24 | 26,1.75,2.0,False,289000,1196.0,2101077580,1408-Hinoki-Walk-NE-Issaquah-WA-98029 25 | 28,2.5,4.0,False,792000,3510.0,48961625,4225-191st-Ave-SE-Issaquah-WA-98027 26 | 29,2.5,4.0,False,765000,2130.0,48989145,290-Newport-Way-NW-A-5-Issaquah-WA-98027 27 | 30,1.5,2.0,False,155000,1015.0,63296393,2613-NE-Mulberry-St-Issaquah-WA-98029 28 | 31,3.0,4.0,False,665000,2120.0,58383720,22615-SE-44th-Pl-1634-Issaquah-WA-98029 29 | 32,2.0,2.0,False,365000,1482.0,96712211,1670-Pine-View-Dr-NW-Issaquah-WA-98027 30 | 33,4.0,5.0,False,16800000,4510.0,2101099898,4225-Providence-Point-Dr-SE-Issaquah-WA-98029 31 | 34,3.0,3.0,False,520000,2086.0,68474749,27549-SE-28th-Ct-Sammamish-WA-98075 32 | 35,3.0,5.0,False,825000,3668.0,48886445,210-SW-Clark-St-APT-A301-Issaquah-WA-98027 33 | 36,1.0,2.0,False,,828.0,96675057,10908-329th-Ct-SE-Issaquah-WA-98027 34 | 37,3.5,4.0,False,795000,2500.0,2101113940,973-NE-Ingram-St-UNIT-207-Issaquah-WA-98029 35 | 38,1.75,3.0,False,285000,1106.0,2101113941,1834-NW-James-Bush-Rd-Issaquah-WA-98027 36 | 39,3.5,4.0,False,939000,3200.0,48729880,14235-248TH-Pl-SE-Issaquah-WA-98027 37 | 40,3.5,4.0,False,550000,2920.0,2101113943,26000-SE-Mirrormont-Blvd-Issaquah-WA-98027 38 | 42,2.5,3.0,False,750000,3036.0,48903798,4308-243rd-Ave-SE-Issaquah-WA-98029 39 | 43,2.5,3.0,False,615000,2110.0,48729931,25525-SE-Tiger-Mountain-Rd-Issaquah-WA-98027 40 | 44,1.0,2.0,False,265000,1184.0,48970589,18632-NW-Bernina-Ct-Issaquah-WA-98027 41 | 47,2.5,4.0,False,802000,3789.0,2101135244,1391-Pine-Crest-Cir-NE-LOT-06-Issaquah-WA-98029 42 | 49,,,False,,,84758010,856-3rd-Ave-NE-Issaquah-WA-98029 43 | 52,2.5,3.0,False,720000,2470.0,2101144139,23942-SE-45th-(Lot-65)-Pl-Issaquah-WA-98029 44 | 53,2.5,2.0,False,450000,1306.0,48970414,5230-Jung-Frau-Pl-NW-Issaquah-WA-98027 45 | 54,3.0,3.0,False,975000,3340.0,48968814,24516-SE-146th-St-Issaquah-WA-98027 46 | 55,3.0,3.0,False,575000,3020.0,48827989,3233-241st-Ave-SE-Issaquah-WA-98029 47 | 56,3.5,5.0,False,890000,3890.0,63337436,595-Newport-Way-NW-Issaquah-WA-98027 48 | 57,2.0,2.0,False,220000,889.0,2101169955,3512-255th-Ln-SE-APT-8-Issaquah-WA-98029 49 | 58,2.5,2.0,False,425000,1472.0,48700426,12120-Issaquah-Hobart-Rd-SE-Issaquah-WA-98027 50 | 59,1.0,3.0,False,349000,1210.0,2101174259,4139-220th-Pl-SE-Issaquah-WA-98029 51 | 60,1.0,1.0,False,198000,830.0,63336325,580-Front-St-S-Issaquah-WA-98027 52 | 61,2.0,2.0,False,240000,1005.0,61719456,510-Sky-Country-Way-NW-Issaquah-WA-98027 53 | 62,3.5,5.0,False,858000,3900.0,48903590,24236-SE-43rd-Pl-Issaquah-WA-98029 54 | 63,2.25,3.0,False,500000,1830.0,48968787,14910-245th-Ave-SE-Issaquah-WA-98027 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