Inspiration
The purpose of this Project is to help people in exploring better facilities in & around their neighborhoods. It will help them in making smart and efficient decisions when selecting their preferred neighborhood out of all the neighborhoods in Scarborough, Toranto. Many people are migrating to Scarborough, Toronto and require a lot of research to find housing prices in their range and good schools for their children. This project will help such people. This Project aims to create an analysis of features for people migrating to Scarborough to search best neighborhoods by comparing neighborhoods with each other. The features include median housing prices and schools according to ratings, crime rates of that particular area, road connectivity, weather conditions, 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.
What it does
I have used Four-square API for gathering data, 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. 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 Latitude Neighborhood Longitude Venue Name of the venue e.g. the name of a store or restaurant Venue Latitude Venue Longitude Venue Category
How we built it
I have used Four-square API for gathering data, 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. 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. Clustering Techniques To compare the similarities of two cities, I decided to explore neighborhoods, segment them, and group them into clusters to find similar neighborhoods in 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. K-Means Clustering Approach- Most Common Venues Most common venues near neighborhoods. Note- Using credentials of Foursquare API features of nearby places of the neighborhoods would be mined. Due to HTTP request limitation, the number of places per neighborhood parameter was set to 100 and the radius parameter was set to 500.
Challenges we ran into
collecting the data and cleaning it since it was scraped from the web.
Accomplishments that we're proud of
I was able to use API like foursquare and along with that learn how to tackle noisy data.
What we learned
I was able to use API like foursquare and along with that learn how to tackle noisy data.
What's next for The-Battle-of-Neighbourhoods
This project can further be fine-tuned to show precise and best choice housing by incorporating more independent variables during the clustering stage.
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