• By harnessing comprehensive datasets encompassing diverse musical genres, artist attributes, track features, and user behaviors, this project embarks on a robust analysis. Employing methodologies rooted in probability theory and statistical inference, we delve into feature engineering, exploratory data analysis, and regression modeling to discern patterns and correlations that influence a song's resonance with listeners. • This academic project focuses on employing advanced probability and statistical methodologies to predict the popularity of songs within music streaming platforms such as Spotify and Deezer.
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CMP26-Projects/Song-Popularity-Prediction
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A trained model using inferential statistics and machine learning algorithms for your Spotify
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