This is a business analytics model solely designed for understanding the causes behind rising mental health issues among people employed in tech-based companies and providing solutions for dealing with them.
End-to-end Machine Learning pipeline and EDA analyzing university students' academic stressors and depression levels using Scikit-Learn (Classification, PCA, K-Means).
A Pattern Recognition system to predict student depression using Random Forest and Logistic Regression.
This project focuses on Predictive Modeling and Clustering of Student Stress Using Machine Learning. The aim is to explore, analyze, and model stress levels among students based on behavioral, academic, and physiological factors, using various machine learning techniques.
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