Capstone Project - The Battle of
Neighborhoods
January 03, 2020
Author: Mohammad Mijanur Rahman
1. Introduction Section: ⁃ The “business problem” to be solved by this
project and who may be interested
2. Data Section: ⁃ Describe Data requirements and Sources
needed to solve the problem
3. Methodology section: ⁃ Main component of the report - Execute data
processing, describe/discuss any exploratory data analysis and/or inferential
statistical testing performed, and/or machine learnings used.
4. Analysis & Results section: ⁃ Discussion of the results and finding of
answer
5. Discussion section: ⁃ Discussion of observations noted and any
recommendations
6. Conclusion section: ⁃ Answer chosen and conclusions.
1.0 Introduction
1.1 Scenario and Background: I am currently living in Singapore, within walking distance to Downtown
"Telok Ayer MRT metro station" . I also enjoy great venues and
attractions, such as international cuisine, entertainment and shopping. I have
an offer to move to work to Manhattan NY and I would like to move if I can find
a place to live similar with similar venues.
1.2 Problem to be resolved: How to find an apartment in Manhattan with the following conditions: •
Apartment with min 2 bedrooms • Monthly rent not to exceed US$7000/month •
Located within walking distance (<=1.0 mile, 1.6 km) from a subway metro
station in Manhattan • Venues and amenities as in my current residence.
1.3 Interested Audience: I believe the methodology, tools and strategy used in this project is
relevant for a person or entity considering moving to a major city in US,
Europe or Asia. Europe, US or Asia, Likewise, it can be helpful approach to
explore the opening of a new business. The use of FourSquare data and mapping
techniques combined with data analysis will help resolve the key questions
arisen. Lastly, this project is a good practical case for a person developing
Data Science skills.
2.0 Data Section
2.1 Data Requirements - Geodata for current residence in Singapore with venues established using
Foursquare. - List of Manhattan (MH) neighborhoods with clustered venues
established via Foursquare (as in Course Lab).
https://en.wikipedia.org/wiki/List_of_Manhattan_neighborhoods#Midtown_neighborhoods
- List of subway metro stations in Manhattan with addresses and geo data
(lat,long):
https://en.wikipedia.org/wiki/List_of_New_York_City_Subway_stations_in_Manhattan)
,
(https://www.google.com/maps/search/manhattan+subway+metro+stations/@40.7837297,-74.1033043,11z/data=!3m1!4b1)
- List of apartments for rent in Manhattan area with information on
neighborhood location, address, number of beds, area size, monthly rent price
and complemented with geo data via Nominatim.
http://
www.rentmanhattan.com/index.cfm?page=search&state=results
https://www.nestpick.com/search? city=new- - Place to work in Manhattan (Park
Avenue and 53rd St) for reference
2.2 Data Sources, Data
Processing and Tools used - Singapore data and map is to be created with use of
Nominatim , Foursquare and Folium mapping - Manhattan neighbourhoods were
obtained from Wikipedia and organized by Neighbourhoods with geodata via
Nominatim for mapping with Folium. - List of Subway stations was obtained via
Wikipedia, NY Transit web site and Google map, - List of apartments for rent
was consolidated from web-scraping real estate sites for MH. The geolocation
(lat,long) data was found with algorithm coding and using Nominatim. - Folium
map was the basis of mapping with various features to consolidate all data in
ONE map where one can visualize all details needed to make a selection of
apartment
3.0 Methodology
The
Strategy to find the answer:
The
strategy is based on mapping the described data in section 2.0, in order to
facilitate the choice of at least two candidate places for rent. The
information will be consolidated in ONE MAP where one can see the details of
the apartment, the cluster of venues in the neighborhood and the relative location
from a subway station and from work place. A measurement tool icon will also be
provided. The popups on the map items will display rent price, location and
cluster of venues applicable.
The
Tools:
Web-scraping
of sites is used to consolidate data-frame information which was saved as csv
files for convenience and to simply the report. Geodata was obtained by coding
a program to use Nominatim to get latitude and longitude of subway stations and
also for each of (144 units) the apartments for rent listed. Geopy_distance and
Nominatim were used to establish relative distances. Seaborn graphic was used
for general statistics on rental data. Maps with popups labels allow quick
identification of location, price and feature, thus making the selection very
easy
4.0 Analysis & Results
Using
the "one map" above, I was able to explore all possibilities since
the popups provide the information needed for a good decision.
Apartment
1 rent cost is US7500 slightly above the US7000 budget. Apt 1 is located 400
meters from subway station at 59th Street and work place ( Park Ave and 53rd)
is another 600 meters way. I can walk to work place and use subway for other
places around. Venues for this apt are as of Cluster 2 and it is located in a
fine district in the East side of Manhattan.
Apartment
2 rent cost is US6935, just under the US7000 budget. Apt 2 is located 60 meters
from subway station at Fulton Street, but I will have to ride the subway daily
to work , possibly 40-60 min ride. Venues for this apt are as of Cluster 3
Based
on current Singapore venues, I feel that Cluster 2 type of venues is a closer
resemblance to my current place. That means that APARTMENT 1 is a better choice
since the extra monthly rent is worth the conveniences it provides.
5.0
Discussion
·
In general, I am positively impressed with
the overall organization, content and lab works presented during the Coursera
IBM Certification Course
·
I feel this Capstone project presented me a
great opportunity to practice and apply the Data Science tools and
methodologies learned.
·
I have created a good project that I can
present as an example to show my potential.
6.0
Conclusions
·
I feel rewarded with the efforts, time and
money spent. I believe this course with all the topics covered is well worthy
of appreciation.
·
This project has shown me a practical
application to resolve a real situation that has impacting personal and
financial impact using Data Science tools.
·
The mapping with Folium is a very powerful technique to consolidate information and make the analysis and decision
thoroughly and with confidence. I would recommend for use in similar
situations.
·
One must keep abreast of new tools for DS
that continue to appear for application in several business fields.
End
