nalehc / gala_flyer_locations Goto Github PK

README

• Associated Files: 1_MTA_Exploration.ipynb and Clean_and_Process.ipynb
• Put the data into pandas data frame
• Explore the data through different angles (duplicates, exits distribution, etc.)
• Clean data for New Jersey stations
• Format the data and only leave applicable columns
• Analyse and handle outliers
• Plot the data to understand the trends in the data
• Return a list of top stations sorted descending by number of exits
• Generate MTA score associated with each identified subway station

• Associated Files:
• Utilized web-scraping to generate list of 21 most valuable tech companies' in New York
• Utilized Google Maps API to get geo-locations of the identified tech companies
• Generated tech company score associated with each identified subway station

• Associated Files: Starbucks_and_Census_Alan.ipynb
• Utilized Google Maps API to find the geo-locations of the Starbucks surrounding each identified subway station
• Generated Starbucks score associated with each identified subway station

• Associated Files: Starbucks_and_Census_Alan.ipynb, community_districts.geojson, totpop_singage_sex2010_cd.xlsx
• Incorporated US Census 2010 data and geospatial data for the NYC Community Districts (CDs) to assign a gender score to each CD
• Generated gender score based on which CD each identified subway station was located in

• Associated Files:
• Use geopandas
• Colour code the stations based on different criteria

• Associated Files: Final_Presentation
• Draw conclusions
• Write up recommendation(s)
• Build a presentation (6 min)
• Divide up the presentation topics between team members