***** Created by Nelson Dsouza ***** ***** January@2017 *****
Description
The main question I was trying to answer through this project was - "Can in-store music experience be quantified and visualzed?". The data for this analysis was provided by PlayNetwork which is an organization which curates music for stores. The data considered was for the zip code 98105 (University District, Seattle) for the month July 2016.
Using Principal Component Analysis and Factor Analysis it is possible to represent the genre information in 2 dimensions such that the majority of the variance is accounted for. The latent factor loadings are the quantitiative scores that represent the genres. Since we have 2 latent factors, a 2-dimensional 'Music Map' can be constructed. I have also conducted hierarchial clustering to represent the genre distance as a dendrogram.
*** Note***
- These predictions are ONLY valid in the context of the datasets I have analyzed here and SHOULD NOT be extended the entire domain.
- I have done the work listed here as part of course work for Business INtelligence (Winter 2016) at the University of Washington.
*** Acknowledgement ***
My professor from the University of Washington, Justin Blaney(blaney@me.com) for guidance and support.
*** Contact ***
For any queries or bug reports feel free to contact me @ nelsonds@uw.edu / nelson.dsouza.3@gmail.com / skype handle - nelson.dsouza.here