Data Analytics Project for Spotify: Enhancing Song Recommendation Systems

In a comprehensive project for Spotify, I undertook the task of improving the company's song recommendation system by predicting song popularity through data preprocessing and model development. By enhancing Decision Tree, Logistic Regression, and k-Nearest Neighbor models, I optimized accuracy and conducted a cost-benefit analysis to guide strategic decisions for revenue growth. The inclusion of clustering analysis and genre-specific logistic regression models revealed valuable insights, showcased in a detailed report highlighting analytical capabilities.

Collaborating effectively within a team, we met project deadlines and presented findings utilizing tools like Google Colab to ensure robust and reproducible results. This experience underscored my technical skills, analytical prowess, and ability to drive actionable insights from data, aligning with Spotify's strategic objectives effectively.