Download the dataset below to solve this Data Science case study on Statistical Modelling.
The given dataset is based on music tracks on Spotify and contains following features:
- Track Name, Artists, Album Name: These are categorical variables related to the music track.
- Popularity: Numerical value representing the track’s popularity.
- Release Date: The date on which the track was released.
- Duration (ms): The length of the track in milliseconds.
- Explicit: Boolean indicating whether the track contains explicit content.
- Danceability, Energy, Loudness: Attributes that describe the musical qualities of the track.
- Speechiness, Acousticness, Instrumentalness, Liveness, Valence, Tempo: Other attributes describing more dimensions of the track.
The task is to perform statistical modelling to find what determines the popularity of a music track. Here are some tasks you need to perform:
- Quantitative Analysis of Musical Features: Systematically analyze how different features such as danceability, energy, loudness, and valence correlate with and predict the popularity of music tracks.
- Development of Predictive Statistical Models: Construct and refine multiple statistical models to accurately predict music track popularity based on quantitative data.
- Statistical Insight Generation: Utilize the results from the statistical models to provide quantifiable insights and recommendations for enhancing track popularity.