implicit data

According to a new report released by Nielsen Music, on an average, Americans now spend just slightly more than 32 hours a week listening to music. This is a staggering 36% increase in 2 years. With such a tremendous growth in the music industry, it becomes crucial to deliver personalized music recommendation to the listeners. This piqued our curiosity to understand the process that goes behind the music recommendation engine and led us to work on this project. We achieved a ~90% AUC for ~40,000 users and ~100,000 artists.

Traffic is always a painful problem for both authorities and commuters. Recently bike sharing has evolved as a viable alternative to both reduce traffic and pollution.In this project, we have performed exploratory data analysis to understand the different aspects that affect bike sharing and recommendations for bike rebalancing