We start as below —As you can see 4 coordinates are completely wrong (Bunkyo, Koto, Ota, Edogawa), which is due to the names of the districts are written little different than the way they are in this data-frame (ex.
Hongō — Hongo), so, I had to replace these coordinates with values acquired from google search.
Usually the profit margin for a decent restaurant lie within 15−20% range but, it can even go high enough to 35%, as discussed here.
The core of Tokyo is made of 23 wards (municipalities) but, I will later concentrate on 5 most busiest business wards of Tokyo — Chiyoda (千代田区), Chuo (中央区), Shinjuku (新宿区), Shibuya (渋谷区) and Shinagawa (品川区), to target daily office workers.
After little more playing around with pandas, I could get one well-arranged data-frame as below —Another factor that can guide us later for deciding which district would be best to open a restaurant is, the average land price of 23 wards.
I get this information from scrapping ‘land market value area in Tokyo’ web-page, similarly to the Wiki page before.MSDS '18 students Jack Prominski and Pragati Shah, led by faculty advisor and DSI professor Rafael Alvarado, undertook a capstone project to improve an automated system that matches PLOS article manuscripts with appropriate editors.A person walking quietly through a park might never be heard by others, but if a flock of birds flies away as the person passes it is revealed that someone is there.Project topics are unique for each instance of the course.Topics are driven by real needs of companies, communities and research groups.We will go through each step of this project and address them separately.I first outline the initial data preparation and describe future steps to start the battle of neighborhoods in Tokyo.The project’s findings are that offline internet has considerable potential to bridge information gaps, especially in rural, low-resource settings.The team’s recommendations are being incorporated into a pilot project to field-test the device during summer 2017.Master of Science in Data Science students Sean Mullane, Ruoyan Chen and Sri Vaishnavi Vemulapalli undertook a project to apply data science tools and techniques to understanding 3D protein structures, and see if protein structures can be quantitatively described, compared and otherwise analyzed in a more robust, efficient and automated manner.For their 2019 capstone project, DSI Master of Science in Data Science students Charu Rawat, Arnab Sarkar, and Sameer Singh proposed a framework to understand and detect online harassment in the English Wikipedia community.