Neighbourhood Classification with clustering data points in machine learning
A brief description of the Problem In this final project we were invited to explore a hypothetical situation. We live in Toronto and love our neighborhood mainly because have everything we need to feel like “home”. Great amenities and other types of venues such as gourmet fast food joints, pharmacies, parks, grad schools, we have all. But “sadly” we receive a job offer from our dream company on the other side of the city with great career prospects and is not feasible to keep living on our current place, and we need to move if we decide to accept the offer. So, the challenge is to discover similar neighborhoods on the other side of the city! And the objective of this Capstone project is to analyze and select the most likely neighborhoods in the city of Toronto to move in if we accept changing job
Data Toronto City data containing the neighborhoods and boroughs will be obtained from the open data source: http://cocl.us/Geospatial_data. After it, we will get the geographical coordinates of the neighborhoods (postal code, latitude and longitude).