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Billboard Top Songs Analysis
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About
Unlocking the science behind chart-topping music, this dataset offers 5,000 entries designed specifically for predictive modelling. It merges authentic chart data from platforms like Spotify with synthetic records to present a rich blend of musical attributes, performance metrics, and popularity indicators. The primary function of this collection is to enable the creation of machine learning models capable of predicting a song's eventual peak position on major music charts.
Columns
- Song: The official title of the track.
- Artist: The name of the performing musician or band.
- Streams: The total lifetime stream count achieved across streaming platforms.
- Daily Streams: The average number of streams the song registers per day.
- Genre: The musical category, such as Pop, Hip-Hop, or Rock.
- Release Year: The year the track was officially released.
- Peak Position: The highest chart rank the song has attained (scored 1 to 100).
- Weeks on Chart: The overall duration, measured in weeks, the song maintained a position on the charts.
- Lyrics Sentiment: A score reflecting the emotional tone of the lyrics, ranging from -1 (negative) to +1 (positive).
- TikTok Virality: A score between 0 and 100 indicating the track’s popularity and engagement within TikTok trends.
- Danceability: A measure (0-1) of how suitable the song is for dancing.
- Acousticness: The degree of acoustic elements present in the recording (0-1).
- Energy: The overall intensity and energy level of the song (0-1).
Distribution
The dataset is provided in a CSV file format named
music_dataset.csv and has a file size of 404.73 kB. It contains 13 distinct columns. There are 4,850 valid records ready for analysis. The data is static, with an expected update frequency of never.Usage
This collection is highly optimised for machine learning projects and data visualisation tasks. Ideal applications include:
- Developing predictive algorithms to forecast a song's likely peak chart rank.
- Analyzing the influence of modern metrics, such as TikTok virality, on charting success.
- Exploring longitudinal musical trends across different genres and release years.
Coverage
The scope covers various musical attributes and metrics tied to chart performance on major platforms like Billboard and Spotify. The data time range spans release years from 1990 through to 2025. It integrates metrics like streams, energy, and acoustic properties to provide a detailed view of song characteristics influencing popularity.
License
CC0: Public Domain
Who Can Use It
The dataset is intended for data scientists, machine learning practitioners, and music industry analysts. Users focused on predictive modeling can leverage the attributes to train effective ranking and classification systems. Researchers interested in popular culture and music evolution can utilize the genre and time-based metrics for trend analysis.
Dataset Name Suggestions
- Hit Predictor 5000
- Music Chart Prediction Metrics
- Billboard Top Songs Analysis
- Streaming Success Predictor
Attributes
Original Data Source: Billboard Top Songs Analysis
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