Impact of Music on Brain and Mental Health: Classification using Machine Learning Algorithms

Music creates powerful effects on human psychological conditions while it affects brain cognitive performance and brain functioning patterns. This research investigates the connection between different music choices and ways people listen to music together with depression and anxiety and insomnia conditions. The research uses data from a music and mental health survey to execute extensive exploratory data analysis (EDA) which reveals patterns and relationships. A set of preprocessing methods proves essential for maintaining data excellence before analysis through removal of outliers and treatment of missing values. Multiple visual representations show how people use music along with showing how their preferences align with mental health indicators. An analysis using machine learning algorithms helps identify mental health conditions through examining user listening behavior. Predictive models are developed by investigating three important features involving genres of choice together with how long listeners spend listening and musical impact on emotions. The research investigates both mental state transformations through music and determines if future listening habits reveal indications of psychological problems. The research enhances understanding of music-based mental health research while promoting the creation of specific music therapy practices.