Spotify song analysis and machine learning model to curate a playlist of danceable 50 songs based on energy and loudness
Our objective is to construct and implement a machine learning model for forecasting a song's danceability, drawing insights from our data briefing. Danceability hinges on various elements, including tempo, rhythm stability, beat strength, and regularity. Given that not all of these factors are present in our dataset, our approach involves a thorough analysis and visualization. These efforts aim to identify the dataset variables that closely align with the characteristics relevant to danceability. By doing so, we intend to bridge the gap between available data and the comprehensive understanding needed to make accurate danceability predictions through our machine learning model. This explory data analysis is carried out to find which variables best fit as factors of danceability.