Data Science Project: Music Recommender System on KKBox Dataset in Python. The idea is to come up with a music recommendation system that involves the use of machine learning techniques to improve user engagement and to ease music discovery on the KKBox platform, which happens to be one of Asia’s largest streaming services.
This is accompanied by user- and song-specific metadata, including user_ID, song_ID, song_genre, and other constraints on user interaction, all in Train.csv. It also has Songs.csv and members.csv, representing song and user account information, respectively.
Data preprocessing involves handling outliers, anomalies, and missing values in data so that uniformity and accuracy can be achieved. Outlier detection, missing value imputation, and label encoding are some of the techniques used with the help of specific libraries such as pandas, NumPy, and sci-kit learn.
Some models assessed for this project are Logistic Regression for its simplicity, Decision Trees for structured decision-making, and finally, the Random Forests for ensemble learning for the prediction of user-song interaction. These models would be implemented and the system would be allowed to make personalized music recommendations that maximize satisfaction.
