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Exploratory data analysis of the popularity of songs derived from the last 7 decades (1950 - 2010) with various regression and classification problems.
The original source of the data is derived from this kaggle repository created by N. Carbone.
The data was modified (e.g. merging and cleaning the CSV files provided) in such a way for the construction of a class competition at Strathclyde University in the module of CS985 Machine Learning (academic year 2019-20), leading to two problems:
The aim was the identification of the most important attributes that the most popular songs share and have in common.
In the regression problem, various models were built in order to predict the popularity of the songs, whereas the classification problem led to the construction of models that predicted their music genre.