/IBM_Data_Science_Capstone

Identifying the most musical neighborhoods in New York City using Foursquare's Places API and machine learning.

Primary LanguageJupyter Notebook

"Tuning-In To NYC’s Musical Neighborhoods"

Overview

Deliverables

Why

The purpose of this repository is to highlight my Capstone Project for IBM's Data Science Professional Certificate that I took through Coursera.

Abstract

Machine learning allows for the creation of computational models capable of identifying patterns in multidimensional datasets. This project aims to leverage Foursquare location data and machine learning algorithms to identify the most musical neighborhoods in New York City.

Background

Music is a form of art that has, and probably always will be, deeply embedded within the cultural activity of cities, communities, and groups of people more generally. Music is a means of communication, expression, and sometimes even protest with the power to peacefully bring together large amounts of like-minded people, influence popular culture, and hypnotize you with a memorable lyric that you end up singing in the shower subliminally for weeks on end even after consciously being disappointed in yourself for doing so…..I digress....

Problem

Cities are, in part, composed of musical entities such as record shops, instrument vendors, concert halls, amphitheaters, and more, that not only provide to the music needs of local citizens but also to tourists from around the world. For bigger cities, music entities can be spread apart, resulting in an ecosystem of hip niche neighborhoods that evolve and change over time. This ecosystem is often learned by humans looking for a cool music scene through either natural life experience (wandering/flaneur) or recommendations in the form of internet reviews, comments, and conversations with people in-real-life.

This project aims to quantify and monitor the state of neighborhoods in a major metropolitan city, New York City, and identify clusters of similar music scenes.

Stakeholders

Different parties may be interested in a model that is able to quantify neighborhood similarity based on music venue frequency and thus group neighborhoods of similar music profiles. Such a model would be able to inform renters and home buyers who prefer to live where the music is happening that they’re next home is properly located. Future music venue start-ups can utilize the model to identify neighborhoods lacking live music venues and ensure they are investing in an area that is not saturated. Future music retail vendors, sellers of things like records and instruments, can similarly utilize the model to ensure they are launching a business where competition is in their favor.