![]() ![]() Create a dataframe that includes only the variables of interest.Only a minority of variables seem to be highly correlated.īased on the findings from the exploratory section, below are the actions implemented to preprocess the data:.Anxiety and depression are the most common disorders.The dataset seems complete for the most part, with the exception of the BPM variable which has a higher degree of missingness.The variables can be roughly divided in three blocks: Demographics/behaviors, music genres, and mental health.The full dataset can also be accessed online for free here:įrom the exploratory section, we can conclude that: The survey was conducted online from August to November 2022, it was mostly distributed through online channels (such as Reddit), and did not limit participants by age or location.īelow is a snippet of the raw dataset as well as some graphics exploring missingness in the data, the distribution of some of the main (numerical) columns, and the correlation among variables. This project relies on the programming language, Python, to explore and transform the MxMT (Music x Mental Health Traits) dataset, which is a survey dataset that contains responses from over 700 individuals about their music preferences and mental health traits. Therefore, this project aims to explore whether machine learning algorithms can predict depression based on music preferences and streaming behaviors. While the causes of mental disorders are complex and multifaceted, researchers have long recognized that environmental factors, such as social and cultural influences, can play a role in their development. It’s well known that mental disorders affect millions worldwide, and their impact on individuals and society is significant. ![]() Having a background in psychology and having played guitar for over 15 years, music and mental health are two topics near and dear to me. This project develops machine learning models to attempt to predict depression based on a free survey dataset with over 700 responses ( Music x Mental Health Traits).įive classification models are explored: K-Nearest Neighbors, Naive Bayes, Decision Tree, Support Vector Machine, and Linear Discriminant Analysis.Īll models achieve a moderate performance, with K-Nearest Neighbors being the best in terms of performance, interpretability, and flexibility.Įven though all models perform decently, mental health is a complex topic, the assumptions underlying the models are simplistic, and implementing them would require evaluating the ethical implications.
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