1.
Introduction:
The purpose
of this Capstone Project is to help people in exploring better facilities
around their neighborhood. It will help people making smart and efficient
decision on selecting great neighborhood out of numbers of other neighborhoods
in Scarborough, Toranto.
Lots of
people are migrating to various states of Canada and needed lots of research
for good housing prices and reputated schools for their children. This project
is for those people who are looking for better neighborhoods. For ease of
accessing to Cafe, School, Super market, medical shops, grocery shops, mall, theater, hospital, like-minded people, etc.
This Capstone
Project aim to create an analysis of features for a people migrating to
Scarborough to search a best neighborhood as a comparative analysis between
neighborhoods. The features include median housing price and better school
according to ratings, crime rates of that particular area, road connectivity,
weather conditions, good management for emergency, water resources both fresh
and waste water and excrement conveyed in sewers and recreational facilities.
It will help
people to get awareness of the area and neighborhood before moving to a new
city, state, country or place for their work or to start a new fresh life.
2.
Data Section
Will use
Scarborough dataset which we scrapped from Wikipedia in Week 3 Assignment.
Dataset consisting of latitude and longitude, zip codes.
Foursquare
API Data:
We will need data about different venues in different neighborhoods of that specific borough.
In order to gain that information we will use “Foursquare” locational information. Foursquare is a location data provider with information about all manner of venues and events within an area of interest. Such information includes venue names, locations, menus and even photos. As such, the foursquare location platform will be used as the sole data source since all the stated required information can be obtained through the API.
After finding
the list of neighborhoods, we then connect to the Foursquare API to gather
information about venues inside each and every neighborhood. For each
neighborhood, we have chosen the radius to be 100 meter.
The data
retrieved from Foursquare contained information of venues within a specified
distance of the longitude and latitude of the postcodes. The information
obtained per venue as follows:
- Neighborhood
- Neighborhood Latitude
- Neighborhood Longitude
- Venue
- Name of the venue e.g. the name of a store or restaurant
- Venue Latitude
- Venue Longitude
- Venue Category
Map of Scarborough:
3.
Methodology Section
Clustering
Approach:
To compare
the similarities of two cities, we decided to explore neighborhoods, segment
them, and group them into clusters to find similar neighborhoods in a big city
like New York and Toronto. To be able to do that, we need to cluster data which
is a form of unsupervised machine learning: k-means clustering algorithm.
Using K-Means Clustering Approach -
Most Common Venue
Most Common Venues near Neighborhood -
Using Clustering
Work
Flow:
Using
credentials of Foursquare API features of near-by places of the neighborhoods
would be mined. Due to http request limitations the number of places per
neighborhood parameter would reasonably be set to 100 and the radius parameter
would be set to 500.
4. Results Section
Map of Clusters in Scarborough:
Average Housing Price by Clusters in Scarborough:
School Ratings by Clusters in Scarborough:
The
Location:
Scarborough
is a popular destination for new immigrants in Canada to reside. As a result,
it is one of the most diverse and multicultural areas in the Greater Toronto
Area, being home to various religious groups and places of worship. Although
immigration has become a hot topic over the past few years with more
governments seeking more restrictions on immigrants and refugees, the general
trend of immigration into Canada has been one of on the rise.
Foursquare
API:
This Capstone
project have used Four-square API as its prime data gathering source as it has
a database of millions of places, especially their places API which provides
the ability to perform location search, location sharing and details about a
business.
5.
Discussion Section
Problem
Which Tried to Solve:
The major
purpose of this project, is to suggest a better neighborhood in a new city for
the person who are shifting there. Social presence in society in terms of like
minded people. Connectivity to the airport, bus stand, city center, markets and
other daily needs things nearby.
- Sorted list of house in terms of
housing prices in a ascending or descending order
- Sorted list of schools in terms
of location, fees, rating and reviews
6.
Conclusion Section
In this
Capstone project, using k-means cluster algorithm I separated the neighborhood
into 10(Ten) different clusters and for 103 different latitude and longitude
from dataset, which have very-similar neighborhoods around them. Using the
charts above results presented to a particular neighborhood based on average
house prices and school rating have been made.
I feel
rewarded with the efforts and 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 better with confidence.
The mapping with Folium is a very powerful technique to consolidate information and make the analysis and decision better with confidence.
Future
Works:
This Capstone
project can be continued for making it more precise in terms to find best house
in Scarborough. Best means on the basis of all required things (daily needs or
things we need to live a better life) around and also in terms of cost
effective.
Libraries
which are used to develop the Project:
Pandas:
For creating and manipulating dataframes.
Folium:
Python visualization library would be used to visualize the neighborhoods
cluster distribution of using interactive leaflet map.
Scikit
Learn: For importing k-means clustering.
JSON:
Library to handle JSON files.
XML: To
separate data from presentation and XML stores data in plain text format.
Geocoder:
To retrieve Location Data.
Beautiful
Soup and Requests: To scrap and library to handle http requests.
Matplotlib:
Python Plotting Module.
