This project focuses on predicting depression among students using various machine learning models. It explores relationships between key factors like sleep duration, gender, financial stress, work/study hours, and academic pressure with depression. The study leverages EDA and multiple ML algorithms to achieve high prediction accuracy.
random-forest machine-learning-algorithms eda logistic-regression decision-trees gradient-boosting bagging knn-classification depression-analysis depression-detection xgboost-classifier adaboostclassifier student-depression-prediction
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Updated
Dec 15, 2025 - Jupyter Notebook