Capstone Data Science project as part of the "IBM Data Science Professional Certificate".

Imperial College London, being situated in the heart of South Kensington and lacking in physical space, does not guarantee accommodation for students from their second year onwards. South Kensington lacks student accommodation and has soaring rent prices. This leads to many students moving into flats in areas away from Imperial, leading to longer commute times and lower safety due to increased crime rates.

A student should be able to find an area that suits their needs best: they may enjoy a long commute, but only if they are able to cycle to university. They may prefer living in areas with a high density of restaurants, whereas others may prefer a vibrant nightlife, all while living in a safe and (relatively) crime-free area. The current systems that exist in place for finding accommodation (such as zoopla.co.uk and rightmove.co.uk) do not provide any insight into the areas where the properties are listed.

This project explores the first part of a multistage project to develop a program that can give students an answer for what is the best neighbourhood to live in, based on their preferences. Here, a first attempt to cluster neighbourhoods based on their suitability for students using their distance from university, their average weekly rent, the availability of amenities such as restaurants and grocery stores in the area, and the transport duration to university using different travel modes (bus, tube, walk and cycle) is conducted.

- NOTE: as of right now, the other stages of the project have not yet been started
@@ Next steps include refining the code written here and creating importable functions for later use @@

Capstone Notebooks: This includes the notebooks used for the capstone only. Capstone1.ipynb and Capstone2.ipynb are there because they were required for the IBM course, they are actually not relevant for the project.

Data: This includes data sets used for the project, as well as any relevant json files. This directory is further divided into subdirecties that give meaning to the files stored. It's important to note that there is no file for the crime data as a CSV, because of a very high file size.

Images: Includes any images used for the report.

Labs: Includes labs that were conducted as part of the "IBM Data Science Capstone" course on Coursera (this is the 9th course from the specialisation). These were my own attempts at doing the labs provided by the IBM course, so they include a mixture of original code and IBM code. This is specified within.

- NOTE: Lab 3 note complete

Presentation: Includes the final presentation for the Capstone course.

Report: Includes the report in multiple formats.