1. Introduction (Business Problem)
Toronto is the commercial center/hub of Canada and the capital of the province of Ontario. It is a metropolitan city with booming economy that offers exciting and promising opportunities for both intra-Canadian migrants and immigrants into Canada from other parts of the world.
Moving to Toronto is not an easy task, Toronto is a major commercial hub with lots of activities which make it difficult to find residential neighborhoods that are calm and without the hassles of living in a big city like Toronto. One of the major challenges faced by intending migrants to Toronto especially people moving from a calm city/neighborhood is finding good residential locations in Toronto that are similar to where they currently live.
In this analysis, we will focus on migration from Ottawa to Toronto. The specific case study used is “migration from Barrhaven Ottawa to Toronto”. I attempt to apply machine learning to determine Toronto neighborhoods with similarities to Barrhaven in Ottawa – the model will also work perfectly well for any other cities of consideration in any part of the world.
This analysis aims to determine clusters of neighborhoods in Ottawa and compare them with clusters of neighborhoods in Toronto. We will determine neighborhoods of Ottawa that are in similar clusters as Toronto neighborhoods.
There are two major data requirements for this analysis viz:
a. List of neighborhoods in Toronto and Ottawa with corresponding longitude and latitude coordinates of each neighborhood. This data was not readily available, alternative source of getting the data was identified and utilized. List of neighborhoods in Ottawa and Toronto were scraped from the internet and corresponding coordinates (longitude & latitude) were obtained using the geopy geodecoder python library. This may not give a 100% accuracy but very good level of accuracy to achieve the desired objective of this project. Below is a sample of the neighborhood data after acquisition and wrangling: