As Github does not support folium map, so if you want to see fully rendered notebook, click on below link
https://nbviewer.jupyter.org/github/Mr-Piyush-Kumar/Data_Science_Projects/blob/master/Finding_Best_Biryani_Point/Finding_Best_Biryani_Point_In_Hyderabad.ipynb
In this project, I am trying to find best reataurant to eat chicken biryani for a person who came to Hyderabad for the first time and staying in a Hotel within 5 km range from his location. This project is also targeting the people those want to know the best biryani point in their areas.
As there may be many restaurants within 5 km range so it's difficult to say which one is serving best biryani.
To solve this problem I will use the magic of Data Science.
To solve this problem I required following data about restaurants within 5 km range from the user's location :
Following data sources will be needed to extract/generate the required information:
(I am using zomato api for ratings because zomato is more used here in Hyderabad than foursquare)
Note
# installing required libraries
!pip install geopy # Installing geopy library, this library helps in getting Latitude and Longitude of a given address.
!pip install folium # map visualizing library.
#avoiding warnigs
pd.options.mode.chained_assignment = None # avoiding setting with copy warning
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
Requirement already satisfied: geopy in /home/piyush/anaconda3/lib/python3.6/site-packages Requirement already satisfied: geographiclib<2,>=1.49 in /home/piyush/anaconda3/lib/python3.6/site-packages (from geopy) You are using pip version 9.0.1, however version 20.0.2 is available. You should consider upgrading via the 'pip install --upgrade pip' command. Requirement already satisfied: folium in /home/piyush/anaconda3/lib/python3.6/site-packages Requirement already satisfied: numpy in /home/piyush/.local/lib/python3.6/site-packages (from folium) Requirement already satisfied: requests in /home/piyush/anaconda3/lib/python3.6/site-packages (from folium) Requirement already satisfied: branca>=0.3.0 in /home/piyush/anaconda3/lib/python3.6/site-packages (from folium) Requirement already satisfied: jinja2>=2.9 in /home/piyush/.local/lib/python3.6/site-packages (from folium) Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /home/piyush/anaconda3/lib/python3.6/site-packages (from requests->folium) Requirement already satisfied: idna<2.7,>=2.5 in /home/piyush/anaconda3/lib/python3.6/site-packages (from requests->folium) Requirement already satisfied: urllib3<1.23,>=1.21.1 in /home/piyush/anaconda3/lib/python3.6/site-packages (from requests->folium) Requirement already satisfied: certifi>=2017.4.17 in /home/piyush/anaconda3/lib/python3.6/site-packages (from requests->folium) Requirement already satisfied: six in /home/piyush/.local/lib/python3.6/site-packages (from branca>=0.3.0->folium) Requirement already satisfied: MarkupSafe>=0.23 in /home/piyush/.local/lib/python3.6/site-packages (from jinja2>=2.9->folium) You are using pip version 9.0.1, however version 20.0.2 is available. You should consider upgrading via the 'pip install --upgrade pip' command.
#importing required libraries
import pandas as pd
import numpy as np
import requests
import folium
from geopy.geocoders import Nominatim # Nominatim converts an address into latitude and longitude values.
#Getting Location's Coordinates of Hyderabad City.......
address = 'Hyderabad'
geolocator = Nominatim(user_agent='Hyderabad_explorer')
Hyd_location = geolocator.geocode(address)
Hyd_latitude = Hyd_location.latitude
Hyd_longitude = Hyd_location.longitude
print('latitide=',Hyd_latitude,' longitude=',Hyd_longitude)
latitide= 17.38878595 longitude= 78.46106473453146
#Getting Hotel location where user stay...
# I am considering that for this project, user stay in Taj Banjara Hotel in Hyderabad.
Hotel_address = 'Taj Banjara,Hyderabad'
H_location = geolocator.geocode(Hotel_address)
H_lat = H_location.latitude
H_long = H_location.longitude
print('latitude=',H_lat,' longitude=',H_long)
latitude= 17.4096457 longitude= 78.4488565
# defining forsquare credentials to get list of 100 restaurants near by user's location.
LIMIT = 100 # no. of restaurants.
radius = 6000 # defing range 6 KM, I am considering range+1.
CLIENT_ID = 'enter your id' # my Foursquare ID
CLIENT_SECRET = 'enter your secret' # my Foursquare Secret
VERSION = '20180605' # Foursquare API version
url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
CLIENT_ID,
CLIENT_SECRET,
VERSION,
H_lat,
H_long,
radius,
LIMIT)
url
'https://api.foursquare.com/v2/venues/explore?&client_id=MUINF3SJELTWX0T2R3GWA5P5R3QYAGI2PDFGFR0HCERWTFNH&client_secret=TNBO5TIGKMZR0RR1ARMSUHBMPJ2V0JZBNZQ2G2220FAMS05U&v=20180605&ll=17.4096457,78.4488565&radius=6000&limit=100'
# API calling to get data in Json format
result = requests.get(url).json()
result
{'meta': {'code': 200, 'requestId': '5e3f828aaba2977c4b25cc28'}, 'response': {'suggestedFilters': {'header': 'Tap to show:', 'filters': [{'name': 'Open now', 'key': 'openNow'}]}, 'headerLocation': 'Hyderabad', 'headerFullLocation': 'Hyderabad', 'headerLocationGranularity': 'city', 'totalResults': 242, 'suggestedBounds': {'ne': {'lat': 17.46364575400005, 'lng': 78.5053433960252}, 'sw': {'lat': 17.355645645999946, 'lng': 78.39236960397481}}, 'groups': [{'type': 'Recommended Places', 'name': 'recommended', 'items': [{'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '53ee0f6f498e013a31a899e7', 'name': 'Spice 6', 'location': {'address': 'Opposite Ruosh, Road No. 1, Banjara Hills', 'lat': 17.409007042478336, 'lng': 78.45055865151815, 'labeledLatLngs': [{'label': 'display', 'lat': 17.409007042478336, 'lng': 78.45055865151815}], 'distance': 194, 'cc': 'IN', 'city': 'Hyderabad', 'state': 'Telangana', 'country': 'India', 'formattedAddress': ['Opposite Ruosh, Road No. 1, Banjara Hills', 'Hyderabad', 'Telangana', 'India']}, 'categories': [{'id': '52e81612bcbc57f1066b79f1', 'name': 'Bistro', 'pluralName': 'Bistros', 'shortName': 'Bistro', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/default_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-53ee0f6f498e013a31a899e7-0'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4bdae2dac4e5952110c36a08', 'name': 'Lamakaan', 'location': {'address': 'Next to C Bay, Banjara Hills', 'crossStreet': 'Rd No 1', 'lat': 17.42101717782916, 'lng': 78.44747414471229, 'labeledLatLngs': [{'label': 'display', 'lat': 17.42101717782916, 'lng': 78.44747414471229}], 'distance': 1274, 'cc': 'IN', 'city': 'Hyderabad', 'state': 'Telangana', 'country': 'India', 'formattedAddress': ['Next to C Bay, Banjara Hills (Rd No 1)', 'Hyderabad', 'Telangana', 'India']}, 'categories': [{'id': '4bf58dd8d48988d1f2931735', 'name': 'Performing Arts Venue', 'pluralName': 'Performing Arts Venues', 'shortName': 'Performing Arts', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/arts_entertainment/performingarts_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4bdae2dac4e5952110c36a08-1'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4bdb0a2a63c5c9b643042668', 'name': 'Bikanervala', 'location': {'address': '6-3-190/2, Banjara Hills', 'crossStreet': 'Rd No 1', 'lat': 17.4107930193358, 'lng': 78.44979337882813, 'labeledLatLngs': [{'label': 'display', 'lat': 17.4107930193358, 'lng': 78.44979337882813}], 'distance': 161, 'postalCode': '500034', 'cc': 'IN', 'city': 'Hyderabad', 'state': 'Telangana', 'country': 'India', 'formattedAddress': ['6-3-190/2, Banjara Hills (Rd No 1)', 'Hyderabad 500034', 'Telangana', 'India']}, 'categories': [{'id': '54135bf5e4b08f3d2429dfe2', 'name': 'Chaat Place', 'pluralName': 'Chaat Places', 'shortName': 'Chaat', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/default_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4bdb0a2a63c5c9b643042668-2'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4c752eec604a370413e18149', 'name': 'Barbeque Nation', 'location': {'address': 'Opp City Center Mall, Banjara Hills', 'crossStreet': 'Rd No 1', 'lat': 17.415006386717224, 'lng': 78.44986900354539, 'labeledLatLngs': [{'label': 'display', 'lat': 17.415006386717224, 'lng': 78.44986900354539}], 'distance': 606, 'cc': 'IN', 'city': 'Hyderabad', 'state': 'Telangana', 'country': 'India', 'formattedAddress': ['Opp City Center Mall, Banjara Hills (Rd No 1)', 'Hyderabad', 'Telangana', 'India']}, 'categories': [{'id': '4bf58dd8d48988d1df931735', 'name': 'BBQ Joint', 'pluralName': 'BBQ Joints', 'shortName': 'BBQ', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/bbqalt_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4c752eec604a370413e18149-3'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4bc97fc50687ef3b0612dacc', 'name': 'GVK One', 'location': {'address': 'Banjara Hills', 'crossStreet': 'Rd No 1', 'lat': 17.419411036100108, 'lng': 78.44806879531454, 'labeledLatLngs': [{'label': 'display', 'lat': 17.419411036100108, 'lng': 78.44806879531454}], 'distance': 1090, 'postalCode': '500034', 'cc': 'IN', 'city': 'Hyderabad', 'state': 'Telangana', 'country': 'India', 'formattedAddress': ['Banjara Hills (Rd No 1)', 'Hyderabad 500034', 'Telangana', 'India']}, 'categories': [{'id': '4bf58dd8d48988d1fd941735', 'name': 'Shopping Mall', 'pluralName': 'Shopping Malls', 'shortName': 'Mall', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/mall_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4bc97fc50687ef3b0612dacc-4'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4b4cb2c1f964a520beba26e3', 'name': 'Hard Rock Cafe Hyderabad', 'location': {'address': 'Road Number 1, Gvk One, Banjara Hills', 'crossStreet': 'Rd No 1', 'lat': 17.419399617919183, 'lng': 78.44853258395597, 'labeledLatLngs': [{'label': 'display', 'lat': 17.419399617919183, 'lng': 78.44853258395597}], 'distance': 1086, 'postalCode': '500034', 'cc': 'IN', 'city': 'Hyderabad', 'state': 'Telangana', 'country': 'India', 'formattedAddress': ['Road Number 1, Gvk One, Banjara Hills (Rd No 1)', 'Hyderabad 500034', 'Telangana', 'India']}, 'categories': [{'id': '4bf58dd8d48988d14e941735', 'name': 'American Restaurant', 'pluralName': 'American Restaurants', 'shortName': 'American', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/default_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4b4cb2c1f964a520beba26e3-5'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4bbc7e2ced7776b010aa3f51', 'name': 'DineHill', 'location': {'address': 'One Downing Street, Masab Tank', 'lat': 17.405255858379928, 'lng': 78.4516736049954, 'labeledLatLngs': [{'label': 'display', 'lat': 17.405255858379928, 'lng': 78.4516736049954}], 'distance': 573, 'cc': 'IN', 'city': 'Hyderabad', 'state': 'Telangana', 'country': 'India', 'formattedAddress': ['One Downing Street, Masab Tank', 'Hyderabad', 'Telangana', 'India']}, 'categories': [{'id': '4bf58dd8d48988d10f941735', 'name': 'Indian Restaurant', 'pluralName': 'Indian Restaurants', 'shortName': 'Indian', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/indian_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4bbc7e2ced7776b010aa3f51-6'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '54f74881498eab131ee6f9ac', 'name': 'water', 'location': {'lat': 17.417200135793934, 'lng': 78.44295182609976, 'labeledLatLngs': [{'label': 'display', 'lat': 17.417200135793934, 'lng': 78.44295182609976}], 'distance': 1049, 'cc': 'IN', 'country': 'India', 'formattedAddress': ['India']}, 'categories': [{'id': '4bf58dd8d48988d119941735', 'name': 'Hookah Bar', 'pluralName': 'Hookah Bars', 'shortName': 'Hookah Bar', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/nightlife/hookahbar_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-54f74881498eab131ee6f9ac-7'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '54997b40498e523caf850411', 'name': 'Guilt Trip', 'location': {'lat': 17.41740061095305, 'lng': 78.44321896565191, 'labeledLatLngs': [{'label': 'display', 'lat': 17.41740061095305, 'lng': 78.44321896565191}], 'distance': 1050, 'cc': 'IN', 'country': 'India', 'formattedAddress': ['India']}, 'categories': [{'id': '4bf58dd8d48988d146941735', 'name': 'Deli / Bodega', 'pluralName': 'Delis / Bodegas', 'shortName': 'Deli / Bodega', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/deli_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-54997b40498e523caf850411-8'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4cf0e66088de3704a3c96e2b', 'name': 'Sarvi', 'location': {'address': 'Banjara Hills', 'crossStreet': 'Rd No 11', 'lat': 17.41269784920106, 'lng': 78.4497038615383, 'labeledLatLngs': [{'label': 'display', 'lat': 17.41269784920106, 'lng': 78.4497038615383}], 'distance': 351, 'postalCode': '500034', 'cc': 'IN', 'city': 'Hyderabad', 'state': 'Telangana', 'country': 'India', 'formattedAddress': ['Banjara Hills (Rd No 11)', 'Hyderabad 500034', 'Telangana', 'India']}, 'categories': [{'id': '4bf58dd8d48988d10f941735', 'name': 'Indian Restaurant', 'pluralName': 'Indian Restaurants', 'shortName': 'Indian', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/indian_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4cf0e66088de3704a3c96e2b-9'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4c4ef8b9f53d0f47ef9167a7', 'name': 'Zafraan Exotica', 'location': {'address': 'Rd #12 Banjara Hills', 'lat': 17.41032532519568, 'lng': 78.4372992545256, 'labeledLatLngs': [{'label': 'display', 'lat': 17.41032532519568, 'lng': 78.4372992545256}], 'distance': 1229, 'cc': 'IN', 'city': 'Hyderabad', 'state': 'Telangana', 'country': 'India', 'formattedAddress': ['Rd #12 Banjara Hills', 'Hyderabad', 'Telangana', 'India']}, 'categories': [{'id': '4bf58dd8d48988d10f941735', 'name': 'Indian Restaurant', 'pluralName': 'Indian Restaurants', 'shortName': 'Indian', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/indian_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4c4ef8b9f53d0f47ef9167a7-10'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4dbff93dcda12687c83f8b24', 'name': 'The Lounge (CCD)', 'location': {'address': 'Banjara Hills', 'crossStreet': 'Rd No 10', 'lat': 17.414837386258828, 'lng': 78.4480484145704, 'labeledLatLngs': [{'label': 'display', 'lat': 17.414837386258828, 'lng': 78.4480484145704}], 'distance': 584, 'postalCode': '500034', 'cc': 'IN', 'city': 'Hyderabad', 'state': 'Telangana', 'country': 'India', 'formattedAddress': ['Banjara Hills (Rd No 10)', 'Hyderabad 500034', 'Telangana', 'India']}, 'categories': [{'id': '4bf58dd8d48988d16d941735', 'name': 'Café', 'pluralName': 'Cafés', 'shortName': 'Café', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/cafe_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4dbff93dcda12687c83f8b24-11'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '551aba89498e7ca6c75914ee', 'name': 'Starbucks', 'location': {'address': 'GVK One Mall', 'lat': 17.41941894752865, 'lng': 78.44813367018776, 'labeledLatLngs': [{'label': 'display', 'lat': 17.41941894752865, 'lng': 78.44813367018776}], 'distance': 1090, 'cc': 'IN', 'city': 'Hyderabad', 'state': 'Telangana', 'country': 'India', 'formattedAddress': ['GVK One Mall', 'Hyderabad', 'Telangana', 'India']}, 'categories': [{'id': '4bf58dd8d48988d1e0931735', 'name': 'Coffee Shop', 'pluralName': 'Coffee Shops', 'shortName': 'Coffee Shop', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/coffeeshop_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-551aba89498e7ca6c75914ee-12'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '56f6cd1dcd106d63a069c738', 'name': "Chicha's", 'location': {'lat': 17.40325495743855, 'lng': 78.46015157913072, 'labeledLatLngs': [{'label': 'display', 'lat': 17.40325495743855, 'lng': 78.46015157913072}], 'distance': 1394, 'cc': 'IN', 'city': 'Hyderabad', 'state': 'Telangana', 'country': 'India', 'formattedAddress': ['Hyderabad', 'Telangana', 'India']}, 'categories': [{'id': '54135bf5e4b08f3d2429dfe6', 'name': 'Hyderabadi Restaurant', 'pluralName': 'Hyderabadi Restaurants', 'shortName': 'Hyderabadi', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/default_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-56f6cd1dcd106d63a069c738-13'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4bb613462f70c9b6ccd38430', 'name': 'Fusion 9', 'location': {'address': '1st Avenue, Road No. 1, Banjara Hills', 'lat': 17.41444393015282, 'lng': 78.4503568643216, 'labeledLatLngs': [{'label': 'display', 'lat': 17.41444393015282, 'lng': 78.4503568643216}], 'distance': 557, 'postalCode': '500034', 'cc': 'IN', 'city': 'Hyderabad', 'state': 'Telangana', 'country': 'India', 'formattedAddress': ['1st Avenue, Road No. 1, Banjara Hills', 'Hyderabad 500034', 'Telangana', 'India']}, 'categories': [{'id': '4bf58dd8d48988d1c4941735', 'name': 'Restaurant', 'pluralName': 'Restaurants', 'shortName': 'Restaurant', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/default_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}, 'venuePage': {'id': '78766474'}}, 'referralId': 'e-0-4bb613462f70c9b6ccd38430-14'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4d5fadf029ef236a85a09b59', 'name': 'Cafe Niloufer & Bakers', 'location': {'address': 'Red Hills Rd, Opp Hanuman Temple', 'crossStreet': 'Lakdikapul', 'lat': 17.399714984098786, 'lng': 78.46288084900512, 'labeledLatLngs': [{'label': 'display', 'lat': 17.399714984098786, 'lng': 78.46288084900512}], 'distance': 1855, 'postalCode': '500004', 'cc': 'IN', 'city': 'Hyderabad', 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'51cad3dbccda4c2c6eb39816', 'name': 'MOB', 'location': {'address': 'Above Little Italy, Road. No 92,Near Apollo Hospital', 'crossStreet': 'Jubilee Hills', 'lat': 17.41625266131663, 'lng': 78.41690835301482, 'labeledLatLngs': [{'label': 'display', 'lat': 17.41625266131663, 'lng': 78.41690835301482}], 'distance': 3472, 'postalCode': '500033', 'cc': 'IN', 'city': 'Hyderabad', 'state': 'Telangana', 'country': 'India', 'formattedAddress': ['Above Little Italy, Road. No 92,Near Apollo Hospital (Jubilee Hills)', 'Hyderabad 500033', 'Telangana', 'India']}, 'categories': [{'id': '4bf58dd8d48988d117941735', 'name': 'Beer Garden', 'pluralName': 'Beer Gardens', 'shortName': 'Beer Garden', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/nightlife/beergarden_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-51cad3dbccda4c2c6eb39816-99'}]}]}}
# function that extracts the category of the venue
def get_category_type(row):
try:
categories_list = row['categories']
except:
categories_list = row['venue.categories']
if len(categories_list) == 0:
return None
else:
return categories_list[0]['name']
from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe
restaurants = result['response']['groups'][0]['items']
nearby_restaurants = json_normalize(restaurants) # flatten JSON
# filter columns
filtered_columns = ['venue.name','venue.categories', 'venue.location.lat', 'venue.location.lng']
nearby_restaurants = nearby_restaurants.loc[:, filtered_columns]
# filter the category for each row
nearby_restaurants['venue.categories'] = nearby_restaurants.apply(get_category_type, axis=1)
# clean columns
nearby_restaurants.columns = [col.split(".")[-1] for col in nearby_restaurants.columns]
nearby_restaurants.head()
name | categories | lat | lng | |
---|---|---|---|---|
0 | Spice 6 | Bistro | 17.409007 | 78.450559 |
1 | Lamakaan | Performing Arts Venue | 17.421017 | 78.447474 |
2 | Bikanervala | Chaat Place | 17.410793 | 78.449793 |
3 | Barbeque Nation | BBQ Joint | 17.415006 | 78.449869 |
4 | GVK One | Shopping Mall | 17.419411 | 78.448069 |
# getting ratings of restaurants from zomato
rating=[] #list to store rating
zomato_key = {'user-key':'enter your key'} # zomato api key
count = 1 # counter
print('Processing restaurant no: ')
for name,lat,lng in zip(nearby_restaurants['name'],nearby_restaurants['lat'],nearby_restaurants['lng']):
print(count,end=" ")
url = ('https://developers.zomato.com/api/v2.1/search?q={}&start=0&count=1&lat={}&lon={}').format(name,lat,lng)
result = requests.get(url, headers = zomato_key)
if(result.status_code == 200):
try:
result = result.json()
rating.append(result['restaurants'][0]['restaurant']['user_rating']['aggregate_rating'])
except:
rating.append('NA')
else:
rating.append('NA')
count+=1
print('\n restaurants ratings are : ')
print(rating)
Processing restaurant no: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 restaurants ratings are : ['4.3', '4.6', '3.8', '4.4', '3.6', '4.5', '3.9', '4.2', '4.4', '3.9', '4.6', '3.3', '4.3', '3.2', '4.1', '4.5', '4.3', '4.2', '4.2', '4.4', '4.1', '4.6', 'NA', '3.1', '4.0', '4.1', '4.2', '4.4', '4.3', '4.1', '4.0', '4.1', '4.4', '4.2', '4.2', '3.6', '4.6', '4.2', '4.1', '4.6', '4.4', '4.1', 0, '4.6', '3.8', '4.5', '4.1', '4.8', '4.4', '3.7', '3.8', '3.2', '4.3', '4.3', '4.3', '3.7', '3.2', '3.2', '4.3', '4.0', '4.5', '4.1', '3.2', '4.2', '4.5', '3.7', '4.3', '4.0', '4.2', '3.9', '3.6', '3.9', '4.3', '4.0', '4.2', '4.5', '4.2', '4.0', '4.3', '4.4', 'NA', '4.2', '3.3', '3.6', '4.3', '3.8', '4.2', '4.3', '4.1', '4.1', '4.5', '3.2', '4.1', '4.1', '4.0', '4.3', '4.0', '3.7', '4.0', '4.3']
# adding ratings
nearby_restaurants['zomato rating'] = rating
nearby_restaurants.head()
name | categories | lat | lng | zomato rating | |
---|---|---|---|---|---|
0 | Spice 6 | Bistro | 17.409007 | 78.450559 | 4.3 |
1 | Lamakaan | Performing Arts Venue | 17.421017 | 78.447474 | 4.6 |
2 | Bikanervala | Chaat Place | 17.410793 | 78.449793 | 3.8 |
3 | Barbeque Nation | BBQ Joint | 17.415006 | 78.449869 | 4.4 |
4 | GVK One | Shopping Mall | 17.419411 | 78.448069 | 3.6 |
# checking restaurants who have no ratings
nearby_restaurants[nearby_restaurants['zomato rating']=='NA'].index
Int64Index([22, 80], dtype='int64')
# droping the restaurants from list
nearby_restaurants.drop([22,80],axis=0,inplace=True)
nearby_restaurants.head()
name | categories | lat | lng | zomato rating | |
---|---|---|---|---|---|
0 | Spice 6 | Bistro | 17.409007 | 78.450559 | 4.3 |
1 | Lamakaan | Performing Arts Venue | 17.421017 | 78.447474 | 4.6 |
2 | Bikanervala | Chaat Place | 17.410793 | 78.449793 | 3.8 |
3 | Barbeque Nation | BBQ Joint | 17.415006 | 78.449869 | 4.4 |
4 | GVK One | Shopping Mall | 17.419411 | 78.448069 | 3.6 |
# getting travel distance and travel time
from datetime import datetime
start_time = datetime.now(tz=None)
time=[] # time unit: minutes
distance=[] # distance unit: km
key = 'enter your key' # Bing Map Api Key.
count = 1 # counter.
print('Processing restaurant no: ')
for name,lat,lng in zip(nearby_restaurants['name'],nearby_restaurants['lat'],nearby_restaurants['lng']):
url = 'https://dev.virtualearth.net/REST/v1/Routes/DistanceMatrix?origins={},{}&destinations={},{}&travelMode=driving&startTime={}&key={}'.format(
H_lat,
H_long,
lat,
lng,
start_time,
key)
print(count,end=" ")
result = requests.get(url).json()
time.append(result['resourceSets'][0]['resources'][0]['results'][0]['travelDuration'])
distance.append(result['resourceSets'][0]['resources'][0]['results'][0]['travelDistance'])
count+=1
print('\n time list : ')
print(time)
print('\n distance list')
print(distance)
Processing restaurant no: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 time list : [5.583, 6.383, 5, 4.367, 6.767, 7.767, 7, 8.467, 8.7, 2.317, 7.7, 3.983, 6.767, 10.383, 2.883, 16.283, 5.05, 6.05, 17.45, 7.717, 17.467, 10.95, 6.783, 6.317, 6.133, 8.683, 10.7, 13.617, 11.867, 17.4, 17.833, 18.033, 13.483, 13.233, 14.4, 8.867, 17.083, 8.867, 18.15, 20.683, 13.817, 11.617, 17.783, 17.55, 15.95, 7.583, 16.683, 20.567, 15.533, 9.467, 21.367, 15, 17.933, 26.483, 9.317, 11.633, 17.083, 14.283, 14.017, 22.033, 20.617, 26.15, 9.917, 17.467, 12.367, 28.917, 10.133, 28.45, 11.65, 22.933, 24.783, 27.433, 14.8, 22.4, 18.05, 13.533, 11.433, 25.7, 21.533, 26.733, 23.083, 18.683, 22.083, 24.917, 15.817, 22.133, 24.283, 20.367, 22.617, 15.3, 15, 16.983, 15.1, 28.367, 21.3, 15.45, 40.517, 15.633] distance list [1.487, 1.627, 1.271, 0.79, 1.562, 1.607, 1.925, 1.695, 1.721, 0.449, 1.575, 0.941, 1.562, 3.037, 0.677, 4.063, 1.293, 1.333, 4.644, 1.582, 4.32, 3.324, 1.563, 1.493, 1.339, 2.484, 3.11, 4.35, 2.687, 5.108, 4.454, 4.442, 4.593, 4.261, 3.814, 1.587, 4.398, 1.567, 4.268, 5.894, 3.985, 3.087, 4.844, 4.518, 4.215, 1.949, 3.379, 6.339, 3.763, 2.768, 4.718, 4.098, 4.428, 7.607, 2.527, 4.039, 4.398, 2.954, 4.916, 5.951, 5.745, 7.083, 2.388, 4.321, 4.452, 6.784, 3.491, 5.931, 3.449, 4.89, 7.285, 7.289, 5.095, 6.898, 4.445, 4.337, 4.466, 7.036, 6.142, 7.139, 6.173, 5.239, 5.69, 6.929, 4.17, 5.982, 5.875, 5.965, 6.517, 4.256, 4.099, 4.086, 4.231, 7.348, 5.69, 4.27, 9.387, 4.142]
# adding time, distance and serving biryani data to our dataframe
nearby_restaurants['travel time minutes'] = time
nearby_restaurants['travel distance km'] = distance
serving_biryani = [1,0,0,1,1,0,1,0,0,1,1,0,0,1,
1,0,0,1,1,0,1,0,1,0,0,0,1,1,
0,1,1,0,0,1,0,0,0,0,0,0,0,0,
0,1,0,1,0,0,0,0,0,0,0,0,0,1,
0,0,1,1,1,0,0,0,0,0,0,0,0,0,
1,0,0,0,0,1,1,0,0,0,0,0,0,1,
0,0,0,0,0,0,0,0,0,0,0,0,0,0] # menually generated list from online searching -> 1: yes, 0: no
nearby_restaurants['serves biryani'] = serving_biryani
nearby_restaurants.head()
name | categories | lat | lng | zomato rating | travel time minutes | travel distance km | serves biryani | |
---|---|---|---|---|---|---|---|---|
0 | Spice 6 | Bistro | 17.409007 | 78.450559 | 4.3 | 5.583 | 1.487 | 1 |
1 | Lamakaan | Performing Arts Venue | 17.421017 | 78.447474 | 4.6 | 6.383 | 1.627 | 0 |
2 | Bikanervala | Chaat Place | 17.410793 | 78.449793 | 3.8 | 5.000 | 1.271 | 0 |
3 | Barbeque Nation | BBQ Joint | 17.415006 | 78.449869 | 4.4 | 4.367 | 0.790 | 1 |
4 | GVK One | Shopping Mall | 17.419411 | 78.448069 | 3.6 | 6.767 | 1.562 | 1 |
# getting those restaurants who serves biryani
biryani_points = nearby_restaurants[nearby_restaurants['serves biryani']==1].reset_index(drop=True)
biryani_points.head()
name | categories | lat | lng | zomato rating | travel time minutes | travel distance km | serves biryani | |
---|---|---|---|---|---|---|---|---|
0 | Spice 6 | Bistro | 17.409007 | 78.450559 | 4.3 | 5.583 | 1.487 | 1 |
1 | Barbeque Nation | BBQ Joint | 17.415006 | 78.449869 | 4.4 | 4.367 | 0.790 | 1 |
2 | GVK One | Shopping Mall | 17.419411 | 78.448069 | 3.6 | 6.767 | 1.562 | 1 |
3 | DineHill | Indian Restaurant | 17.405256 | 78.451674 | 3.9 | 7.000 | 1.925 | 1 |
4 | Sarvi | Indian Restaurant | 17.412698 | 78.449704 | 3.9 | 2.317 | 0.449 | 1 |
biryani_points.shape
(27, 8)
There are 27 restaurants who serves biryani near by user's location
import folium
Hyd_map = folium.Map( location=[Hyd_latitude,Hyd_longitude], zoom_start=12)
Hyd_map
Hyd_map = folium.Map(location=[H_lat,H_long],zoom_start=20)
folium.Marker(location=[H_lat,H_long],tooltip='Taj Banjara Hotel').add_to(Hyd_map)
Hyd_map
Hyd_map = folium.Map(location=[H_lat,H_long],zoom_start=13)
folium.Marker(location=[H_lat,H_long],tooltip='Taj Banjara Hotel').add_to(Hyd_map)
for name,lat,lng in zip(biryani_points['name'],biryani_points['lat'],biryani_points['lng']):
folium.CircleMarker(
location=[lat,lng],
tooltip=name,
color='blue',
radius=6,
fill=True,
fill_color='red',
fill_opacity=0.8).add_to(Hyd_map)
Hyd_map
To find the best biryani serving restaurant near the user's location, First I have to check those restaurants with highest rating so need to sort our data in descending order according to rating to get highest rating restaurants on top of the list then I will consider only those restaurants having high value rating then after I will sort restaurants list according to travel time in ascending order to get a restaurant where user can reach in short time and for result I will select the first restaurant in the list after doing all anaysis.
To solve this problem I don't require any machine learning algorithm.
biryani_points['zomato rating'] = biryani_points['zomato rating'].astype(float)
# Sorting data Frame according to zomato ratings in descending order.
biryani_points.sort_values(by='zomato rating',ascending=False,inplace=True)
biryani_points.reset_index(drop=True,inplace=True)
biryani_points.head()
name | categories | lat | lng | zomato rating | travel time minutes | travel distance km | serves biryani | |
---|---|---|---|---|---|---|---|---|
0 | Zafraan Exotica | Indian Restaurant | 17.410325 | 78.437299 | 4.6 | 7.700 | 1.575 | 1 |
1 | Cafe Bahar | Indian Restaurant | 17.399595 | 78.478566 | 4.5 | 22.033 | 5.951 | 1 |
2 | Barbeque Nation | BBQ Joint | 17.415006 | 78.449869 | 4.4 | 4.367 | 0.790 | 1 |
3 | Meridian Resturant | Fast Food Restaurant | 17.427355 | 78.452159 | 4.4 | 10.700 | 3.110 | 1 |
4 | Spice 6 | Bistro | 17.409007 | 78.450559 | 4.3 | 5.583 | 1.487 | 1 |
# Getting list of highest rating restaurants.
top_points = biryani_points[biryani_points['zomato rating']==biryani_points['zomato rating'][0]]
top_points
name | categories | lat | lng | zomato rating | travel time minutes | travel distance km | serves biryani | |
---|---|---|---|---|---|---|---|---|
0 | Zafraan Exotica | Indian Restaurant | 17.410325 | 78.437299 | 4.6 | 7.7 | 1.575 | 1 |
For this situation I got only one restaurant with rating 4.6. Next step is for the situation if I get more than one restaurant with rating 4.6 so there is need to choose one and for this I am choosing that restaurant where user can reach in short time.
# sorting the dataframe according to travel time in ascending order..
top_points.sort_values(by=['travel time minutes'],inplace=True) # sorting list according to time.
top_points.reset_index(drop=True, inplace=True)
top_points = top_points[top_points['travel distance km']<=5].reset_index(drop=True) # considering only restaurants those are in 5 km range.
top_points
name | categories | lat | lng | zomato rating | travel time minutes | travel distance km | serves biryani | |
---|---|---|---|---|---|---|---|---|
0 | Zafraan Exotica | Indian Restaurant | 17.410325 | 78.437299 | 4.6 | 7.7 | 1.575 | 1 |
Let's do some analysis now..
Doing some additional analysis on fetched data..
Let's check how many different catagories of restaurants are near by the user's location
len(nearby_restaurants['categories'].unique())
47
There are 47 different catagories of restaurants.
Let's check how many restaurants are there in each catagory..
category = nearby_restaurants['categories'].unique().tolist()
category[0:10]
['Bistro', 'Performing Arts Venue', 'Chaat Place', 'BBQ Joint', 'Shopping Mall', 'American Restaurant', 'Indian Restaurant', 'Hookah Bar', 'Deli / Bodega', 'Café']
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(10,20))
ax = sns.countplot(y='categories', data=nearby_restaurants)
y_count=0.1
for p in ax.patches:
ax.annotate(str(p.get_width()), (p.get_width()+0.05,y_count),color='blue')
y_count+=1
plt.title('No. of restaurants in each category',size=15)
plt.show()
Mostly there are one or two restaurant in each catagory except Indian Restaurants, South Indian, Bistro, cafe and Bakery.
Let's check different catagories of restaurants those serve biryani
biryani_points.categories.unique()
array(['Indian Restaurant', 'BBQ Joint', 'Fast Food Restaurant', 'Bistro', 'Hotel', 'Scenic Lookout', 'Nightclub', 'Restaurant', 'Lounge', 'Burger Joint', 'Cupcake Shop', 'Shopping Mall', 'Vegetarian / Vegan Restaurant', 'Hyderabadi Restaurant', 'Bookstore'], dtype=object)
plt.figure(figsize=(10,10))
ax = sns.countplot(y='categories', data=biryani_points)
y_count=0.1
for p in ax.patches:
ax.annotate(str(p.get_width()), (p.get_width()+0.05,y_count),color='blue')
y_count+=1
plt.title('No. of restaurants in each category those sereve biryani',size=15)
plt.show()
Let's look out on ratings
nearby_restaurants['zomato rating'] = nearby_restaurants['zomato rating'].astype(float)
plt.figure(figsize=(20,10))
ax = sns.countplot(x='zomato rating', data=nearby_restaurants)
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x()+0.3,p.get_height()+0.1),color='blue')
plt.title('No. restaurants in each uniue rating value', size=15)
plt.show()
Mostly restaurants have ratings between 4 and 4.6. There is only one restaurant with rating of 4.8
Let's checkout those restaurant's ratings who serve biryani
plt.figure(figsize=(15,10))
ax = sns.countplot(x='zomato rating', data=biryani_points)
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x()+0.3,p.get_height()+0.1),color='blue')
plt.title('No. of restaurants in each unique rating value those serve biryani', size=15)
plt.show()
There is only one restaurant with rating of 4.6...
In methodology section I have already computed the list of highest rating restaurants those serves biryani so for the result it's better to select the restaurant having highest rating as well as less time is require to travel from user's location to restaurant.
print('best biryani points with in 5 km is ',top_points.iloc[0]['name'])
print('Distance =',top_points.iloc[0]['travel distance km'],'KM')
print('Travel Time =',round(top_points.iloc[0]['travel time minutes']),'Minutes')
best biryani points with in 5 km is Zafraan Exotica Distance = 1.575 KM Travel Time = 8.0 Minutes
# getting coordinate's location of best biryani point
BP_lat = top_points.iloc[0]['lat']
BP_lng = top_points.iloc[0]['lng']
print('latitude =',BP_lat,' longitude =',BP_lng)
latitude = 17.41032532519568 longitude = 78.4372992545256
# generating url to fectch path data from user's location to retaurant'S location
bing_key = 'enter your key'
url='http://dev.virtualearth.net/REST/V1/Routes/Driving?wp.0={},{}&wp.1={},{}&optmz=distance&routeAttributes=routePath&key={}'.format(H_lat,
H_long,
BP_lat,
BP_lng,
bing_key)
print(url)
http://dev.virtualearth.net/REST/V1/Routes/Driving?wp.0=17.4096457,78.4488565&wp.1=17.41032532519568,78.4372992545256&optmz=distance&routeAttributes=routePath&key=AhMf2auzd03l1cHTLdfAHn0vMvgodS6A77hx-BWfWlUpqGu3fqR1j_5_CiOGXAVe
# fetching data
result = requests.get(url).json()
result
{'authenticationResultCode': 'ValidCredentials', 'brandLogoUri': 'http://dev.virtualearth.net/Branding/logo_powered_by.png', 'copyright': 'Copyright © 2020 Microsoft and its suppliers. All rights reserved. This API cannot be accessed and the content and any results may not be used, reproduced or transmitted in any manner without express written permission from Microsoft Corporation.', 'resourceSets': [{'estimatedTotal': 1, 'resources': [{'__type': 'Route:http://schemas.microsoft.com/search/local/ws/rest/v1', 'bbox': [17.40894, 78.437464, 17.410393, 78.448913], 'id': 'v69,h-594548436,i0,a0,cen-US,dAAAAAAAAAAA1,y0,s1,m1,o2,t4,wcHJkit5oMUA2Bp0QupxTQA2~BBscojKRqo8iAAHgAUv2Yz4B0~cm9hZA2~~~~v11,wnm-cFAtpMUArMAO2_JtTQA2~BBscojKJvY4iAH_gAQWtQT8B0~QmFuamFyYSBIaWxscyBObyAxMiBSb2Fk0~~~~v11,k1,qatt:1', 'distanceUnit': 'Kilometer', 'durationUnit': 'Second', 'routeLegs': [{'actualEnd': {'type': 'Point', 'coordinates': [17.410393, 78.437464]}, 'actualStart': {'type': 'Point', 'coordinates': [17.409488, 78.448913]}, 'alternateVias': [], 'cost': 0, 'description': 'Banjara Hills No. 13 Road, Banjara Hills No 12 Road', 'itineraryItems': [{'compassDirection': 'west', 'details': [{'compassDegrees': 250, 'endPathIndices': [3], 'maneuverType': 'DepartStart', 'mode': 'Driving', 'roadType': 'Street', 'startPathIndices': [0]}], 'exit': '', 'iconType': 'Auto', 'instruction': {'formattedText': None, 'maneuverType': 'DepartStart', 'text': 'Depart toward Banjara Hills No. 13 Road'}, 'isRealTimeTransit': False, 'maneuverPoint': {'type': 'Point', 'coordinates': [17.409488, 78.448913]}, 'realTimeTransitDelay': 0, 'sideOfStreet': 'Unknown', 'tollZone': '', 'towardsRoadName': 'Banjara Hills No. 13 Road', 'transitTerminus': '', 'travelDistance': 0.076, 'travelDuration': 24, 'travelMode': 'Driving', 'warnings': [{'severity': 'None', 'text': 'Private Road', 'warningType': 'PrivateRoad'}]}, {'compassDirection': 'west', 'details': [{'compassDegrees': 279, 'endPathIndices': [13], 'maneuverType': 'TurnRight', 'mode': 'Driving', 'names': ['Banjara Hills No. 13 Road'], 'roadType': 'Street', 'startPathIndices': [3]}], 'exit': '', 'iconType': 'Auto', 'instruction': {'formattedText': None, 'maneuverType': 'TurnRight', 'text': 'Turn right onto Banjara Hills No. 13 Road'}, 'isRealTimeTransit': False, 'maneuverPoint': {'type': 'Point', 'coordinates': [17.40895, 78.4485]}, 'realTimeTransitDelay': 0, 'sideOfStreet': 'Unknown', 'tollZone': '', 'transitTerminus': '', 'travelDistance': 1.038, 'travelDuration': 315, 'travelMode': 'Driving'}, {'compassDirection': 'southwest', 'details': [{'compassDegrees': 227, 'endPathIndices': [15], 'maneuverType': 'TurnLeft', 'mode': 'Driving', 'names': ['Ministers Quarters Lane'], 'roadType': 'Street', 'startPathIndices': [13]}], 'exit': '', 'iconType': 'Auto', 'instruction': {'formattedText': None, 'maneuverType': 'TurnLeft', 'text': 'Turn left onto Ministers Quarters Lane'}, 'isRealTimeTransit': False, 'maneuverPoint': {'type': 'Point', 'coordinates': [17.4102, 78.43918]}, 'realTimeTransitDelay': 0, 'sideOfStreet': 'Unknown', 'tollZone': '', 'transitTerminus': '', 'travelDistance': 0.156, 'travelDuration': 84, 'travelMode': 'Driving'}, {'compassDirection': 'northwest', 'details': [{'compassDegrees': 325, 'endPathIndices': [19], 'locationCodes': ['503+03915'], 'maneuverType': 'TurnRight', 'mode': 'Driving', 'names': ['Banjara Hills No 12 Road'], 'roadType': 'MajorRoad', 'startPathIndices': [15]}], 'exit': '', 'iconType': 'Auto', 'instruction': {'formattedText': None, 'maneuverType': 'TurnRight', 'text': 'Turn right onto Banjara Hills No 12 Road'}, 'isRealTimeTransit': False, 'maneuverPoint': {'type': 'Point', 'coordinates': [17.40932, 78.43805]}, 'realTimeTransitDelay': 0, 'sideOfStreet': 'Unknown', 'tollZone': '', 'transitTerminus': '', 'travelDistance': 0.135, 'travelDuration': 58, 'travelMode': 'Driving'}, {'compassDirection': 'northwest', 'details': [{'compassDegrees': 334, 'endPathIndices': [19], 'locationCodes': ['503+03915'], 'maneuverType': 'ArriveFinish', 'mode': 'Driving', 'names': ['Banjara Hills No 12 Road'], 'roadType': 'MajorRoad', 'startPathIndices': [19]}], 'exit': '', 'hints': [{'hintType': 'PreviousIntersection', 'text': 'The last intersection is Road No 12'}], 'iconType': 'Auto', 'instruction': {'formattedText': None, 'maneuverType': 'ArriveFinish', 'text': 'Arrive at Banjara Hills No 12 Road on the left'}, 'isRealTimeTransit': False, 'maneuverPoint': {'type': 'Point', 'coordinates': [17.410393, 78.437464]}, 'realTimeTransitDelay': 0, 'sideOfStreet': 'Left', 'tollZone': '', 'transitTerminus': '', 'travelDistance': 0, 'travelDuration': 0, 'travelMode': 'Driving'}], 'routeRegion': 'WR', 'routeSubLegs': [{'endWaypoint': {'type': 'Point', 'coordinates': [17.410393, 78.437464], 'description': 'Banjara Hills No 12 Road', 'isVia': False, 'locationIdentifier': '4|27|28|162|50|137|189|142|34|0|127|224|1|5|173|65|63|1|17.410393,78.437464', 'routePathIndex': 19}, 'startWaypoint': {'type': 'Point', 'coordinates': [17.409488, 78.448913], 'description': 'road', 'isVia': False, 'locationIdentifier': '4|27|28|162|50|145|170|143|34|0|1|224|1|75|246|99|62|1|17.409488,78.448913', 'routePathIndex': 0}, 'travelDistance': 1.405, 'travelDuration': 483}], 'travelDistance': 1.405, 'travelDuration': 483}], 'routePath': {'generalizations': [], 'line': {'type': 'LineString', 'coordinates': [[17.409488, 78.448912], [17.40948, 78.44889], [17.40939, 78.44874], [17.40895, 78.4485], [17.40896, 78.44844], [17.40897, 78.448], [17.40936, 78.44742], [17.40993, 78.44685], [17.41012, 78.44594], [17.41002, 78.44491], [17.41, 78.44295], [17.41014, 78.44151], [17.4101, 78.43964], [17.4102, 78.43918], [17.41001, 78.43897], [17.40932, 78.43805], [17.40952, 78.43791], [17.40979, 78.43772], [17.40994, 78.43765], [17.410392, 78.437463]]}}, 'trafficCongestion': 'Heavy', 'trafficDataUsed': 'None', 'travelDistance': 1.405, 'travelDuration': 483, 'travelDurationTraffic': 728, 'travelMode': 'Driving'}]}], 'statusCode': 200, 'statusDescription': 'OK', 'traceId': 'cf5c25cfeff3425392a4372d27dcedbf|HK00000C14|0.0.0.0|HK0000082F, HK00000A3B'}
# getting coordinate points to draw path on map..
points = result['resourceSets'][0]['resources'][0]['routePath']['line']['coordinates']
points
[[17.409488, 78.448912], [17.40948, 78.44889], [17.40939, 78.44874], [17.40895, 78.4485], [17.40896, 78.44844], [17.40897, 78.448], [17.40936, 78.44742], [17.40993, 78.44685], [17.41012, 78.44594], [17.41002, 78.44491], [17.41, 78.44295], [17.41014, 78.44151], [17.4101, 78.43964], [17.4102, 78.43918], [17.41001, 78.43897], [17.40932, 78.43805], [17.40952, 78.43791], [17.40979, 78.43772], [17.40994, 78.43765], [17.410392, 78.437463]]
# drwaing path on map
Hyd_map = folium.Map(location=[(H_lat+BP_lat)/2,(H_long+BP_lng)/2],zoom_start=16)
folium.Marker(location=[H_lat,H_long],icon=folium.Icon(color='green',icon='fas fa-h-square'),tooltip='Taj Banjara Hotel').add_to(Hyd_map)
folium.Marker(location=[BP_lat,BP_lng],icon=folium.Icon(color='red',icon='fas fa-h-square'),tooltip=top_points.iloc[0]['name']).add_to(Hyd_map)
distance = top_points.iloc[0]['travel distance km']
time = round(top_points.iloc[0]['travel time minutes'])
folium.PolyLine(points,tooltip='distance = '+str(distance)+' KM and time = '+str(time)+' Minutes').add_to(Hyd_map)
Hyd_map
Best Biryani Point near user's location is Zafraan Exotica which is 1.5 Km away from user's location and it will take 8-9 minutes to reach there from user's location and also user can follow above map to reach restaurant.
The Above map is highly interactive, You can Hover it for more information and also you can zoom in and out.
Purpose of this project to find best biryani point with in 5 km range from user's location and also facitilate the user by providing travel time and path direction on map to rech there and Hence Project has secessfully completed the user's requirement by processing restaurants data collected from varius plateforms.