Unveiling the Secrets of Hit Songs: Predicting Popularity Using Spotify Data

In the modern music landscape, understanding what makes a song successful is a puzzle many artists and industry professionals strive to solve. Leveraging the vast trove of data available from platforms like Spotify, this study delves into the realm of machine learning to predict the popularity of songs. By analyzing Spotify’s rich datasets encompassing both musical features and artist-related factors, we unravel the intricate dynamics that contribute to a song’s success. Our findings not only offer valuable insights for musicians and producers but also shed light on the evolving landscape of the music industry.

Introduction
With the advent of digital music streaming platforms like Spotify, the way we consume music has undergone a revolution. Spotify’s extensive library, coupled with its sophisticated algorithms, provides a fertile ground for exploring the factors that influence a song’s popularity. By scrutinizing attributes such as track length, energy, key, and artist-related metrics like followers and popularity, we aim to uncover the underlying patterns that dictate a song’s trajectory in the competitive music landscape.

Data Analysis and Questions
Our study draws upon datasets sourced from the Kaggle repository, comprising detailed information about tracks and artists obtained from Spotify. Through meticulous data preprocessing and feature engineering, we set out to answer pivotal questions such as the influence of audio features and artist-related factors on song popularity. Additionally, we explore the impact of temporal factors and delve into the differences between highly popular and less popular songs.

Analysis
Data preparation involves merging and cleaning datasets, handling missing values, and removing outliers. Feature engineering is performed to select relevant attributes for predictive modeling. Two supervised machine learning algorithms, namely Random Forest and Logistic Regression, are employed to construct predictive models. The performance of these models is evaluated based on metrics like accuracy, precision, recall, and F1 score.

Findings, Reflections & Further Work
Our analysis unveils compelling insights into the relationship between various features and song popularity. Key findings include the significant influence of artist-related factors, such as popularity and followers, on a song’s success. Furthermore, our study highlights the superior performance of the Random Forest algorithm in predicting song popularity. We discuss avenues for future research, including the incorporation of additional data features and the exploration of user behavior and industry trends.

Conclusion
In conclusion, our study underscores the power of data-driven approaches in deciphering the enigma of song popularity. By leveraging Spotify’s wealth of data and employing advanced machine learning techniques, we offer a glimpse into the underlying dynamics of the music industry. Armed with these insights, musicians, producers, and industry stakeholders can navigate the ever-changing landscape of popular music with greater confidence and precision.