Comprehensive Analysis of Music Streaming Behavior Using Spotify Data and Insights

Abstract

The rapid expansion of digital music streaming platforms has produced vast amounts of user interaction data that can be leveraged to understand listener behavior and forecast music popularity trends. This study introduces a comprehensive analytical framework for examining music streaming behavior using Spotify datasets combined with machine learning approaches. The proposed system employs regression-based predictive models, including Linear Regression, Decision Tree Regressor, and Random Forest Regressor, to estimate track popularity using audio features such as danceability, energy, loudness, speechiness, acousticness, valence, and tempo. Data preprocessing techniques, including normalization, feature scaling, and dataset cleaning, are applied to improve model performance and ensure reliable predictions. A Flask-based web application is implemented to provide user authentication, model training, interactive visualization dashboards, and real-time popularity prediction capabilities. Visualization techniques such as histograms, scatter plots, correlation heatmaps, and trend analysis graphs are used to interpret streaming patterns and listener engagement behavior. Experimental results indicate that ensemble learning models effectively capture complex relationships among features and outperform traditional regression approaches in prediction accuracy. The proposed framework supports data-driven decision-making for music streaming platforms, artists, and marketers by identifying key musical attributes and emerging popularity trends. The study demonstrates the effectiveness of machine learning in enhancing recommendation systems, improving user experience, and optimizing strategic planning within modern music streaming ecosystems.