This personal project provides an in-depth analysis of song popularity trends on Spotify, leveraging historical data, seasonal patterns, and machine learning models to uncover key factors driving song popularity.
Project Overview The goal of this project was to understand how song popularity has evolved over the decades and identify the key attributes that contribute to a song's success on Spotify. By analyzing both historical trends and specific song features, we aimed to provide actionable insights for artists, producers, and music enthusiasts.
Key Insights and Findings
1)Long-term Popularity Trends: The analysis revealed a 2% decline in the average popularity of songs from 2023 to 2024, suggesting a recent shift in listener preferences. Conversely, there was a significant 30% increase in average song popularity from the 1960s to 2020, highlighting a broader trend towards more popular music over time.
2)Seasonal Variations: The study identified a 15% drop in median song popularity during the months of June and July, indicating that certain times of the year might influence listening habits and song engagement.
3)Predictive Modeling: A decision tree model was developed to predict song popularity, achieving an average deviation of just 5 points from actual popularity scores. This model provides a reliable method for forecasting song success based on various audio features.
4)Impact of Danceability: The analysis showed that songs with danceability scores between 0.6 and 0.8 tend to have 20% higher average popularity and are 15% more likely to reach high popularity levels. This suggests that danceability is a significant predictor of a song's potential to be well-received by listeners.