Abstract
The study aims to investigate the relationship between mental health issues and academic performance among students using data analytics and machine learning. The study was conducted among students aged 18-24 in various courses, with CGPA ranging up to 5. The data was collected through a survey questionnaire that included questions related to mental health issues such as depression, anxiety, and panic attacks, along with demographic and academic performance-related questions. The collected data was cleaned, explored, and modeled using various machine learning algorithms such as logistic regression, decision tree, random forest, and support vector classifier. The random forest model was found to be the most accurate among all the models. The study found that students with mental health issues have lower academic performance than their peers, and female students are more prone to mental health issues than male students. Keywords: Data Analytics, Students, mental health, modelling, classifier, depression, anxiety
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