Rec_a_Business_final_code.ipynb

{"cells":[{"metadata":{},"cell_type":"markdown","source":"# ** Rec a Business **"},{"metadata":{},"cell_type":"markdown","source":"This project aims to provide new and upcoming business enthusiasts better options to leverage their capital in. This project uses DelhiNeighborhood dataset incorporated into kaggle by Kumar Shaswat, under the license CC BY-NC-SA 4.0. "},{"metadata":{},"cell_type":"markdown","source":"Importing dependencies"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib as plt\nimport seaborn as sns\nimport matplotlib.cm as cm\nimport matplotlib.colors as colors\nimport json\nimport requests\nimport ipywidgets as widgets\nfrom pandas.io.json import json_normalize\nfrom geopy.distance import great_circle\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":54,"outputs":[{"output_type":"stream","text":"/kaggle/input/delhi-neighborhood-data/restaurant_dataSet.csv\n/kaggle/input/delhi-neighborhood-data/delhi_dataSet.csv\n/kaggle/input/neighborhood/Sorted Neighborhood dataset.csv\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## Setting up the background data"},{"metadata":{},"cell_type":"markdown","source":"Reading the data"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"df_delhi = pd.read_csv('/kaggle/input/delhi-neighborhood-data/delhi_dataSet.csv',index_col = 'Unnamed: 0')","execution_count":55,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_delhi.head()","execution_count":56,"outputs":[{"output_type":"execute_result","execution_count":56,"data":{"text/plain":"            Borough  Neighborhood   latitude  longitude\n0  North West Delhi  Adarsh Nagar  28.614192  77.071541\n1  North West Delhi   Ashok Vihar  28.699453  77.184826\n2  North West Delhi       Azadpur  28.707657  77.175547\n3  North West Delhi        Bawana  28.799660  77.032885\n4  North West Delhi     Begum Pur        NaN        NaN","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>latitude</th>\n      <th>longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>North West Delhi</td>\n      <td>Adarsh Nagar</td>\n      <td>28.614192</td>\n      <td>77.071541</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>North West Delhi</td>\n      <td>Ashok Vihar</td>\n      <td>28.699453</td>\n      <td>77.184826</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>North West Delhi</td>\n      <td>Azadpur</td>\n      <td>28.707657</td>\n      <td>77.175547</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>North West Delhi</td>\n      <td>Bawana</td>\n      <td>28.799660</td>\n      <td>77.032885</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>North West Delhi</td>\n      <td>Begum Pur</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Exploring the data to find missing values"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_delhi.shape","execution_count":57,"outputs":[{"output_type":"execute_result","execution_count":57,"data":{"text/plain":"(185, 4)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_delhi.isnull().sum()","execution_count":58,"outputs":[{"output_type":"execute_result","execution_count":58,"data":{"text/plain":"Borough          0\nNeighborhood     0\nlatitude        22\nlongitude       22\ndtype: int64"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"There are 22 neighborhoods without latitude and longitude."},{"metadata":{},"cell_type":"markdown","source":"--------"},{"metadata":{},"cell_type":"markdown","source":"Segregating the neighborhood without latitude and longitude data into a new dataframe and the rest into another dataframe"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_present = df_delhi.where(df_delhi['latitude'].isnull() == False)\ndf_present.dropna(subset = ['Borough','Neighborhood'],inplace=True)\ndf_present.reset_index(inplace=True)\ndf_present.drop(['index'], axis=1,inplace=True)\ndf_present.head()","execution_count":59,"outputs":[{"output_type":"execute_result","execution_count":59,"data":{"text/plain":"            Borough  Neighborhood   latitude  longitude\n0  North West Delhi  Adarsh Nagar  28.614192  77.071541\n1  North West Delhi   Ashok Vihar  28.699453  77.184826\n2  North West Delhi       Azadpur  28.707657  77.175547\n3  North West Delhi        Bawana  28.799660  77.032885\n4  North West Delhi         Dhaka  39.031714 -90.261223","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>latitude</th>\n      <th>longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>North West Delhi</td>\n      <td>Adarsh Nagar</td>\n      <td>28.614192</td>\n      <td>77.071541</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>North West Delhi</td>\n      <td>Ashok Vihar</td>\n      <td>28.699453</td>\n      <td>77.184826</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>North West Delhi</td>\n      <td>Azadpur</td>\n      <td>28.707657</td>\n      <td>77.175547</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>North West Delhi</td>\n      <td>Bawana</td>\n      <td>28.799660</td>\n      <td>77.032885</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>North West Delhi</td>\n      <td>Dhaka</td>\n      <td>39.031714</td>\n      <td>-90.261223</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_missing = df_delhi.where(df_delhi['latitude'].isnull() == True)\ndf_missing.dropna(subset = ['Borough','Neighborhood'],inplace=True)\ndf_missing.reset_index(inplace=True)\ndf_missing.drop(['index'], axis=1,inplace=True)\ndf_missing.head()","execution_count":60,"outputs":[{"output_type":"execute_result","execution_count":60,"data":{"text/plain":"            Borough     Neighborhood  latitude  longitude\n0  North West Delhi        Begum Pur       NaN        NaN\n1  North West Delhi  Rohini Sub City       NaN        NaN\n2       North Delhi       Ghantewala       NaN        NaN\n3       North Delhi      Gulabi Bagh       NaN        NaN\n4       North Delhi     Sadar Bazaar       NaN        NaN","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>latitude</th>\n      <th>longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>North West Delhi</td>\n      <td>Begum Pur</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>North West Delhi</td>\n      <td>Rohini Sub City</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>North Delhi</td>\n      <td>Ghantewala</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>North Delhi</td>\n      <td>Gulabi Bagh</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>North Delhi</td>\n      <td>Sadar Bazaar</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"There is a unwanted symbol set ( [1] ) in the 15th row. \nCleaning the data"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_missing['Neighborhood'].replace('Sundar Nagar[1]' ,'Sundar Nagar',inplace=True)\ndf_missing.head()","execution_count":62,"outputs":[{"output_type":"execute_result","execution_count":62,"data":{"text/plain":"            Borough     Neighborhood  latitude  longitude\n0  North West Delhi        Begum Pur       NaN        NaN\n1  North West Delhi  Rohini Sub City       NaN        NaN\n2       North Delhi       Ghantewala       NaN        NaN\n3       North Delhi      Gulabi Bagh       NaN        NaN\n4       North Delhi     Sadar Bazaar       NaN        NaN","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>latitude</th>\n      <th>longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>North West Delhi</td>\n      <td>Begum Pur</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>North West Delhi</td>\n      <td>Rohini Sub City</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>North Delhi</td>\n      <td>Ghantewala</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>North Delhi</td>\n      <td>Gulabi Bagh</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>North Delhi</td>\n      <td>Sadar Bazaar</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Manually using google maps and Open Street Maps to find the latitude and longitude of the places.\nThis process is being done manually because geopy doesn't do very well with locations of India. There are instances of incorrect data, so cleaning them is also required"},{"metadata":{"trusted":true},"cell_type":"code","source":"# defining series lat and lng for assigning the columns\nlat = pd.Series([],dtype=float)\nlng = pd.Series([],dtype=float)\n\n#Assigning the data\n\n#Bugum Pur\nlat[0], lng[0] = 28.727248, 77.064975 \n\n#Rohini Sub City\nlat[1],lng[1] = 28.741073, 77.082574\n\n#Ghantewala is a famous sweet shop, that got closed, so I am dropping this data\ndf_missing.drop([2],inplace=True)\n\n#Gulabi Bagh\nlat[3],lng[3] = 28.672190, 77.191620\n\n#Sadar Bazaar \nlat[4],lng[4] = 28.659395, 77.212782\n\n#Tees Hazari\nlat[5],lng[5] = 28.665682, 77.216413\n\n#New Usmanpur\nlat[6],lng[6] = 28.677737, 77.256637\n\n#Sadatpur\nlat[7],lng[7] = 28.726746, 77.261097\n\n#Rajender Nagar\nlat[8],lng[8] = 28.641024, 77.185038\n\n#Sadar Bazaar\nlat[9],lng[9] = 28.657305, 77.212750\n\n#Laxmibai Nagar\nlat[10],lng[10] = 28.575276, 77.209630\n\n#Silampur\nlat[11],lng[11] = 28.664181, 77.270916\n\n#Jamroodpur Village\nlat[12],lng[12] = 28.557592, 77.237061\n\n#Kotla Mubarakpur\nlat[13],lng[13] = 28.575783, 77.227396\n\n#Pulpehaladpur\nlat[14],lng[14] = 28.499831, 77.290347\n\n#Sundar Nagar\nlat[15],lng[15] = 28.601985, 77.243725\n\n#Dabri\nlat[16],lng[16] = 28.611823, 77.087268\n\n#Dwarka Sub City\nlat[17],lng[17] = 28.582154, 77.049576\n\n#Sagar Pur\nlat[18],lng[18] = 28.605670, 77.099189\n\n#Partap Nagar\ndf_missing.drop([19],inplace=True)\n\n#Tihar Village\nlat[20],lng[20] = 28.634353, 77.107331\n\n#Uttam Nagar\nlat[21],lng[21] = 28.619573, 77.054916","execution_count":63,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_missing['latitude_mod'] = lat\ndf_missing['longitude_mod'] = lng","execution_count":64,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Arranging the data"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_missing.reset_index(inplace=True)\ndf_missing.drop(columns=['latitude', 'longitude','index'],inplace=True)\ndf_missing.rename(columns={'latitude_mod' : 'latitude' , 'longitude_mod' : 'longitude' }, inplace=True)\ndf_missing.head()","execution_count":65,"outputs":[{"output_type":"execute_result","execution_count":65,"data":{"text/plain":"            Borough     Neighborhood   latitude  longitude\n0  North West Delhi        Begum Pur  28.727248  77.064975\n1  North West Delhi  Rohini Sub City  28.741073  77.082574\n2       North Delhi      Gulabi Bagh  28.672190  77.191620\n3       North Delhi     Sadar Bazaar  28.659395  77.212782\n4       North Delhi      Tees Hazari  28.665682  77.216413","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>latitude</th>\n      <th>longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>North West Delhi</td>\n      <td>Begum Pur</td>\n      <td>28.727248</td>\n      <td>77.064975</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>North West Delhi</td>\n      <td>Rohini Sub City</td>\n      <td>28.741073</td>\n      <td>77.082574</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>North Delhi</td>\n      <td>Gulabi Bagh</td>\n      <td>28.672190</td>\n      <td>77.191620</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>North Delhi</td>\n      <td>Sadar Bazaar</td>\n      <td>28.659395</td>\n      <td>77.212782</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>North Delhi</td>\n      <td>Tees Hazari</td>\n      <td>28.665682</td>\n      <td>77.216413</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Combining both the dataframes into a single dataframe."},{"metadata":{"trusted":true},"cell_type":"code","source":"frames = [df_missing,df_present]\ndf = pd.concat(frames)\ndf.head()","execution_count":66,"outputs":[{"output_type":"execute_result","execution_count":66,"data":{"text/plain":"            Borough     Neighborhood   latitude  longitude\n0  North West Delhi        Begum Pur  28.727248  77.064975\n1  North West Delhi  Rohini Sub City  28.741073  77.082574\n2       North Delhi      Gulabi Bagh  28.672190  77.191620\n3       North Delhi     Sadar Bazaar  28.659395  77.212782\n4       North Delhi      Tees Hazari  28.665682  77.216413","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>latitude</th>\n      <th>longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>North West Delhi</td>\n      <td>Begum Pur</td>\n      <td>28.727248</td>\n      <td>77.064975</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>North West Delhi</td>\n      <td>Rohini Sub City</td>\n      <td>28.741073</td>\n      <td>77.082574</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>North Delhi</td>\n      <td>Gulabi Bagh</td>\n      <td>28.672190</td>\n      <td>77.191620</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>North Delhi</td>\n      <td>Sadar Bazaar</td>\n      <td>28.659395</td>\n      <td>77.212782</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>North Delhi</td>\n      <td>Tees Hazari</td>\n      <td>28.665682</td>\n      <td>77.216413</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"--------"},{"metadata":{},"cell_type":"markdown","source":"The dataframe contains some incorrect values. Correcting them with the help of Google maps"},{"metadata":{},"cell_type":"markdown","source":"Dividing the dataframe into two parts - correct and incorrect"},{"metadata":{"trusted":true},"cell_type":"code","source":"df2 = df[df['latitude'] < 30]","execution_count":68,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df1 = df[df['latitude']>30]\ndf1.reset_index(inplace = True)\ndf1.drop(['index'],axis=1,inplace=True)","execution_count":70,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Correcting the incorrect data"},{"metadata":{"trusted":true},"cell_type":"code","source":"cor_lat = pd.Series([],dtype = float)\ncor_lng = pd.Series([],dtype = float)\n\n#Assigning the data\n\n#Dhaka\ncor_lat[0], cor_lng[0] = 28.709278, 77.206249\n\n#Model Town\ncor_lat[1],cor_lng[1] = 28.720445, 77.192263\n\n#Pul Bangash\ncor_lat[2], cor_lng[2] = 28.666822, 77.206229\n\n#Shastri Nagar , ND\ncor_lat[3],cor_lng[3] = 28.675370, 77.181173\n\n#Gandhi Nagar\ncor_lat[4], cor_lng[4] = 28.666734, 77.274094 \n\n#Shastri Nagar , ED\ncor_lat[5],cor_lng[5] = 28.647588, 77.273977\n\n#Govindpuri\ncor_lat[6], cor_lng[6] = 28.535112, 77.263185 \n\n#Kalkaji\ncor_lat[7],cor_lng[7] = 28.540973, 77.259523\n\n#Ashok Nagar\ncor_lat[8], cor_lng[8] = 28.637112, 77.102459\n\n#Nehru Nagar\ncor_lat[9],cor_lng[9] = 28.663061, 77.169519","execution_count":71,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true},"cell_type":"code","source":"df1['latitude_mod'] = cor_lat\ndf1['longitude_mod'] = cor_lng\ndf1.drop(['latitude','longitude'],axis=1,inplace=True)\ndf1.rename(columns={'latitude_mod' : 'latitude' , 'longitude_mod' : 'longitude' }, inplace=True)\ndf1.head()","execution_count":73,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  \"\"\"Entry point for launching an IPython kernel.\n/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  \n/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py:3997: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  errors=errors,\n/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py:4133: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  errors=errors,\n","name":"stderr"},{"output_type":"execute_result","execution_count":73,"data":{"text/plain":"            Borough   Neighborhood   latitude  longitude\n0  North West Delhi          Dhaka  28.709278  77.206249\n1  North West Delhi     Model Town  28.720445  77.192263\n2       North Delhi    Pul Bangash  28.666822  77.206229\n3       North Delhi  Shastri Nagar  28.675370  77.181173\n4        East Delhi   Gandhi Nagar  28.666734  77.274094","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>latitude</th>\n      <th>longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>North West Delhi</td>\n      <td>Dhaka</td>\n      <td>28.709278</td>\n      <td>77.206249</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>North West Delhi</td>\n      <td>Model Town</td>\n      <td>28.720445</td>\n      <td>77.192263</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>North Delhi</td>\n      <td>Pul Bangash</td>\n      <td>28.666822</td>\n      <td>77.206229</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>North Delhi</td>\n      <td>Shastri Nagar</td>\n      <td>28.675370</td>\n      <td>77.181173</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>East Delhi</td>\n      <td>Gandhi Nagar</td>\n      <td>28.666734</td>\n      <td>77.274094</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Merging both the dataset together"},{"metadata":{"trusted":true},"cell_type":"code","source":"frames = [df1,df2]\ndf = pd.concat(frames)\ndf.reset_index(inplace=True)\ndf.drop(['index'],axis=1,inplace=True)\ndf.head()","execution_count":75,"outputs":[{"output_type":"execute_result","execution_count":75,"data":{"text/plain":"            Borough   Neighborhood   latitude  longitude\n0  North West Delhi          Dhaka  28.709278  77.206249\n1  North West Delhi     Model Town  28.720445  77.192263\n2       North Delhi    Pul Bangash  28.666822  77.206229\n3       North Delhi  Shastri Nagar  28.675370  77.181173\n4        East Delhi   Gandhi Nagar  28.666734  77.274094","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>latitude</th>\n      <th>longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>North West Delhi</td>\n      <td>Dhaka</td>\n      <td>28.709278</td>\n      <td>77.206249</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>North West Delhi</td>\n      <td>Model Town</td>\n      <td>28.720445</td>\n      <td>77.192263</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>North Delhi</td>\n      <td>Pul Bangash</td>\n      <td>28.666822</td>\n      <td>77.206229</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>North Delhi</td>\n      <td>Shastri Nagar</td>\n      <td>28.675370</td>\n      <td>77.181173</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>East Delhi</td>\n      <td>Gandhi Nagar</td>\n      <td>28.666734</td>\n      <td>77.274094</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Verfying the data integrity of the final dataframe"},{"metadata":{"trusted":true},"cell_type":"code","source":"df.shape\n#the dataframe has 183 rows with 4 columns","execution_count":76,"outputs":[{"output_type":"execute_result","execution_count":76,"data":{"text/plain":"(183, 4)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.columns\n#the column names are in order","execution_count":77,"outputs":[{"output_type":"execute_result","execution_count":77,"data":{"text/plain":"Index(['Borough', 'Neighborhood', 'latitude', 'longitude'], dtype='object')"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.isnull().sum()\n#there are no missing values","execution_count":53,"outputs":[{"output_type":"execute_result","execution_count":53,"data":{"text/plain":"Borough         0\nNeighborhood    0\nlatitude        0\nlongitude       0\ndtype: int64"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"** We are done with the data cleaning, wrangling and preliminary Exploratory Data Analysis. Moving on the Foursquare API usage **"},{"metadata":{},"cell_type":"markdown","source":"-----------"},{"metadata":{},"cell_type":"markdown","source":"## Using Foursquare API"},{"metadata":{},"cell_type":"markdown","source":"Defining Foursquare API credentials"},{"metadata":{"trusted":true},"cell_type":"code","source":"CLIENT_ID = 'HSKSPYIMS3JWAL4IJ3IWQT1MOLT04ITR1UTIPF3OYZ220JXF' # Foursquare ID\nCLIENT_SECRET = 'YS1P0XFBMDIERG42KRRFGQHG4AYWYC4LBNIKTWLFXBLEKDKN' # Foursquare Secret\nVERSION = '20200515' # Foursquare API version\n\nprint('Your credentails:')\nprint('CLIENT_ID: ' + CLIENT_ID)\nprint('CLIENT_SECRET:' + CLIENT_SECRET)","execution_count":78,"outputs":[{"output_type":"stream","text":"Your credentails:\nCLIENT_ID: HSKSPYIMS3JWAL4IJ3IWQT1MOLT04ITR1UTIPF3OYZ220JXF\nCLIENT_SECRET:YS1P0XFBMDIERG42KRRFGQHG4AYWYC4LBNIKTWLFXBLEKDKN\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"Defining a function the get the category of the venue"},{"metadata":{"trusted":true},"cell_type":"code","source":"def get_category_type(row):\n    try:\n        categories_list = row['categories']\n    except:\n        categories_list = row['venue.categories']\n        \n    if len(categories_list) == 0:\n        return None\n    else:\n        return categories_list[0]['name']","execution_count":79,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Iterating through the data to get the set of all venue categories and storing it in a new dataframe"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_venues = pd.DataFrame(columns = ['Venue Category'])","execution_count":90,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for j in range(0,183):\n    \n    try:\n        lat,lng = df.iloc[j][2] , df.iloc[j][3]\n        TIME = 'any'\n        radius = 1000\n        LIMIT = 50\n        url =  'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}&time={}&day={}'.format(\n            CLIENT_ID, \n            CLIENT_SECRET, \n            VERSION, \n            lat, \n            lng, \n            radius, \n            LIMIT,\n            TIME,\n            TIME)\n\n        results = requests.get(url).json()\n        venues = results['response']['groups'][0]['items']\n        nearby_venues = pd.json_normalize(venues)\n        filtered_columns = ['venue.name', 'venue.categories']\n        nearby_venues =nearby_venues.loc[:, filtered_columns]\n        nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)\n        nearby_venues.columns = [col.split(\".\")[-1] for col in nearby_venues.columns]\n        \n        l = nearby_venues.shape[0]    \n        for i in range(0,l):\n            df_venues = df_venues.append({'Venue Category' : nearby_venues.iloc[i][1]} , \n                                           ignore_index=True)\n    \n    except:\n        \n        continue\n\n","execution_count":91,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_venues['frequency'] = 1\ndf_venues = df_venues.groupby('Venue Category').count()","execution_count":82,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_venues.head()","execution_count":83,"outputs":[{"output_type":"execute_result","execution_count":83,"data":{"text/plain":"                     frequency\nVenue Category                \nATM                         42\nAccessories Store            4\nAirport                      3\nAirport Terminal             2\nAmerican Restaurant         11","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>frequency</th>\n    </tr>\n    <tr>\n      <th>Venue Category</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>ATM</th>\n      <td>42</td>\n    </tr>\n    <tr>\n      <th>Accessories Store</th>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Airport</th>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>Airport Terminal</th>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>American Restaurant</th>\n      <td>11</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Manually dropping rows that are not business and cannot be independently set up"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_venues.drop(['ATM','Airport','Airport Terminal','Bus Station','Campground','College Cafeteria','Cricket Ground',\n                'Farmers Market','Historic Site','History Museum','IT Services','Lake','Light Rail Station',\n                'Molecular Gastronomy Restaurant','Museum','Park','Pool','Road',\n                'Stadium','Temple','Tourist Information Center','Trail','Train Station','University'], axis = 0,inplace = True)","execution_count":84,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_venues.drop(['Metro Station','Other Great Outdoors','Nightlife Spot','Farm','Track','Astrologer','Zoo'], axis= 0,inplace= True)","execution_count":85,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"-------------"},{"metadata":{},"cell_type":"markdown","source":"Sorting the dataset to get the top venues"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_venues.sort_values(['frequency'],axis=0,ascending = False,inplace=True)\ndf_venues.reset_index(inplace=True)\ndf_venues.head(10)","execution_count":86,"outputs":[{"output_type":"execute_result","execution_count":86,"data":{"text/plain":"         Venue Category  frequency\n0     Indian Restaurant        322\n1                  Café        202\n2                 Hotel        180\n3  Fast Food Restaurant        138\n4           Coffee Shop        132\n5           Pizza Place         97\n6            Restaurant         75\n7                Market         69\n8    Chinese Restaurant         68\n9                   Bar         60","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Venue Category</th>\n      <th>frequency</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Indian Restaurant</td>\n      <td>322</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Café</td>\n      <td>202</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Hotel</td>\n      <td>180</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Fast Food Restaurant</td>\n      <td>138</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Coffee Shop</td>\n      <td>132</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Pizza Place</td>\n      <td>97</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Restaurant</td>\n      <td>75</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Market</td>\n      <td>69</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Chinese Restaurant</td>\n      <td>68</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Bar</td>\n      <td>60</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Merging Cafe and Coffee Shop into one and re-sorting the data\n\ndf_venues.replace({'Café' : 'Coffee Shop' , 202 : (202+132)} , inplace = True)\ndf_venues.drop([4],inplace=True)\ndf_venues.sort_values(['frequency'],axis=0,ascending = False,inplace=True)\ndf_venues.reset_index(inplace=True)\ndf_venues.drop(['index'],axis=1,inplace = True)\ndf_venues","execution_count":87,"outputs":[{"output_type":"execute_result","execution_count":87,"data":{"text/plain":"           Venue Category  frequency\n0             Coffee Shop        334\n1       Indian Restaurant        322\n2                   Hotel        180\n3    Fast Food Restaurant        138\n4             Pizza Place         97\n..                    ...        ...\n177        Mattress Store          1\n178      Video Game Store          1\n179            Whisky Bar          1\n180      Spiritual Center          1\n181           Record Shop          1\n\n[182 rows x 2 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Venue Category</th>\n      <th>frequency</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Coffee Shop</td>\n      <td>334</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Indian Restaurant</td>\n      <td>322</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Hotel</td>\n      <td>180</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Fast Food Restaurant</td>\n      <td>138</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Pizza Place</td>\n      <td>97</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>177</th>\n      <td>Mattress Store</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>178</th>\n      <td>Video Game Store</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>179</th>\n      <td>Whisky Bar</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>180</th>\n      <td>Spiritual Center</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>181</th>\n      <td>Record Shop</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>182 rows × 2 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Verifying the data integrity"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_venues.shape\n# The dataframe has 182 rows with 2 columns","execution_count":88,"outputs":[{"output_type":"execute_result","execution_count":88,"data":{"text/plain":"(182, 2)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_venues.isnull().sum().sum()\n# The dataframe contains no missing values","execution_count":89,"outputs":[{"output_type":"execute_result","execution_count":89,"data":{"text/plain":"0"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Setting up the UI/UX "},{"metadata":{},"cell_type":"markdown","source":"------------------------------------------------------------------"},{"metadata":{},"cell_type":"markdown","source":"### Recommend a business according to the place of choice"},{"metadata":{},"cell_type":"markdown","source":"Please select your Borough from the respective drop down list - "},{"metadata":{"trusted":true},"cell_type":"code","source":"bor = 'North West Delhi'\ndrop_down = widgets.Dropdown(options=df['Borough'].unique(),\n                                description='Borough',\n                                disabled=False)\n\ndef dropdown_handler(change):\n    global bor\n    bor = change.new\n\ndrop_down.observe(dropdown_handler, names='value')\ndisplay(drop_down)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Please select the Neighborhood from the drop down"},{"metadata":{"trusted":true},"cell_type":"code","source":"opt = []\nfor i in range(0,173):\n    if (df.iloc[i][0] == bor):\n        opt.append(df.iloc[i][1])\n\nngbor = ''\ndrop_down = widgets.Dropdown(options=opt,\n                                description='Neighborhood',\n                                disabled=False)\n\ndef dropdown_handler(change):\n    global ngbor\n    ngbor = change.new\n\ndrop_down.observe(dropdown_handler, names='value')\ndisplay(drop_down)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"getting the latitude and longitude for the selected neighborhood"},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(0,df.shape[0]):\n    if (df.iloc[i][0] == bor and df.iloc[i][1] == ngbor):\n        ven_lat = df.iloc[i][2]\n        ven_lng = df.iloc[i][3]","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"using Foursquare API to get the existing business in the selected place "},{"metadata":{"trusted":true},"cell_type":"code","source":"try:\n    cnt = 0\n    radius = 1000\n    LIMIT = 50\n    url =  'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n        CLIENT_ID, \n        CLIENT_SECRET, \n        VERSION, \n        ven_lat, \n        ven_lng, \n        radius, \n        LIMIT)\n\n    results = requests.get(url).json()\n    venues = results['response']['groups'][0]['items']\n    nearby_venues = pd.json_normalize(venues)\n    filtered_columns = ['venue.name', 'venue.categories']\n    nearby_venues =nearby_venues.loc[:, filtered_columns]\n    nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)\n    nearby_venues.columns = [col.split(\".\")[-1] for col in nearby_venues.columns]\n\nexcept:\n    \n    cnt = 1","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"comparing with the sorted dataset to find the best recommendations"},{"metadata":{"trusted":true},"cell_type":"code","source":"if(cnt == 0):\n    \n    ven_business = nearby_venues.categories.unique()\n    rcs = []\n    counter = 0\n\n    l = df_venues.shape[0]\n    vl = len(ven_business)\n    for i in range(0,l):\n        for j in range(0,vl):\n            if(df_venues.iloc[i][0] == ven_business[j]):\n                counter = 1 \n                break\n\n        if(counter == 0):\n            rcs.append(df_venues.iloc[i][0])\n        else:\n            counter = 0\n\nelse:\n    \n    cnt = 0\n    rcs = df_venues['Venue Category']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"For the user selected neighborhood at - \" + bor + \", \" + ngbor + \" Following are the 10 best reccomendations for new business ideas that could be set up - \")\nprint()\n\nfor i in range(0,10):\n    print(rcs[i])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"-------------------------------------------------------------------------------------     "},{"metadata":{},"cell_type":"markdown","source":"### Recommend the optimal neighborhood for the business of choice"},{"metadata":{},"cell_type":"markdown","source":"Choose the type of business  "},{"metadata":{"trusted":true},"cell_type":"code","source":"a = 'Popular business ideas'\nb = 'Facny business ideas'\n\nchoice = a\ndrop_down = widgets.Dropdown(options= [a,b],\n                                description='Choose - ',\n                                disabled=False)\n\ndef dropdown_handler(change):\n    global choice\n    choice = change.new\n\ndrop_down.observe(dropdown_handler, names='value')\ndisplay(drop_down)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"if(choice == a):\n    chc = df_venues[\"Venue Category\"][:25]\nelse:\n    chc = df_venues['Venue Category'][:-25:-1]","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Choose the business"},{"metadata":{"trusted":true},"cell_type":"code","source":"b_choice = ''\ndrop_down = widgets.Dropdown(options= chc,\n                                description='Choose - ',\n                                disabled=False)\n\ndef dropdown_handler(change):\n    global b_choice\n    b_choice = change.new\n\ndrop_down.observe(dropdown_handler, names='value')\ndisplay(drop_down)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Select your current borough and neighborhood."},{"metadata":{"trusted":true},"cell_type":"code","source":"bor = 'North West Delhi'\ndrop_down = widgets.Dropdown(options=df['Borough'].unique(),\n                                description='Borough',\n                                disabled=False)\n\ndef dropdown_handler(change):\n    global bor\n    bor = change.new\n\ndrop_down.observe(dropdown_handler, names='value')\ndisplay(drop_down)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"opt = []\nfor i in range(0,df.shape[0]):\n    if (df.iloc[i][0] == bor):\n        opt.append(df.iloc[i][1])\n\nngbor = ''\ndrop_down = widgets.Dropdown(options=opt,\n                                description='Neighborhood',\n                                disabled=False)\n\ndef dropdown_handler(change):\n    global ngbor\n    ngbor = change.new\n\ndrop_down.observe(dropdown_handler, names='value')\ndisplay(drop_down)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_dis = pd.DataFrame(columns = ['Neighborhood','Distance in km' , 'latitude' , 'longitude'])\n\nfor i in range(0,df.shape[0]):\n    if (df.iloc[i][0] == bor and df.iloc[i][1] == ngbor):\n        ven_lat = df.iloc[i][2]\n        ven_lng = df.iloc[i][3]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"dis = []\nlat = []\nlng = []\nneighbor = pd.Series(opt,dtype = object)\ndf_dis['Neighborhood'] = neighbor\n\nfor i in range(0,df.shape[0]):\n    if(df.iloc[i][0] == bor):\n        it_neighborhood = (df.iloc[i][2] , df.iloc[i][3])\n        lat.append(df.iloc[i][2])\n        lng.append(df.iloc[i][3])\n        selected_neighborhood = (ven_lat, ven_lng) \n        dist = great_circle(it_neighborhood,selected_neighborhood).km\n        dist = format(dist, '.3f')\n        dis.append(dist)\n\ngeo_dist = pd.Series(dis,dtype = float)\ndf_dis['Distance in km'] = geo_dist\n\ndf_dis['latitude'] = pd.Series(lat,dtype=float)\ndf_dis['longitude'] = pd.Series(lng,dtype=float)\n        \ndf_dis.sort_values(['Distance in km'],axis = 0,ascending = True , inplace = True)\ndf_dis.reset_index(inplace = True)\ndf_dis.drop(['index'],axis=1,inplace=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for j in range(0,df_dis.shape[0]):\n    \n    try:\n        counter = 0\n        radius = 1000\n        LIMIT = 50\n        ven_lat = df_dis.iloc[j][2]\n        ven_lng = df_dis.iloc[j][3]\n        url =  'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n            CLIENT_ID, \n            CLIENT_SECRET, \n            VERSION, \n            ven_lat, \n            ven_lng, \n            radius, \n            LIMIT)\n\n        results = requests.get(url).json()\n        venues = results['response']['groups'][0]['items']\n        nearby_venues = pd.json_normalize(venues)\n        filtered_columns = ['venue.name', 'venue.categories']\n        nearby_venues =nearby_venues.loc[:, filtered_columns]\n        nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)\n        nearby_venues.columns = [col.split(\".\")[-1] for col in nearby_venues.columns]\n        \n        ven_buss = pd.Series( (nearby_venues['categories'].unique()) ,dtype=object )\n        v_length = ven_buss.shape[0]\n        \n        for k in range(0,v_length):\n            if(b_choice == ven_buss[k]):\n                counter = 1\n                break\n        \n        if(counter == 0):\n            \n            print(\"The optimal place to start your business, \" + b_choice + \" is in - \" + df_dis.iloc[j][0] + \", \" + bor)\n            break\n        \n        \n    except:\n        \n        print(\"The optimal place to start your business, \" + b_choice + \" is in - \" + df_dis.iloc[j][0] + \", \" + bor)\n    ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"markdown","source":"**************************"}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}