Student Stress Prediction Using Machine Learning

Developed a machine learning project to predict student stress levels based on factors such as sleep hours, study hours, attendance, anxiety level, academic pressure, social support, and physical activity

The project includes data preprocessing, handling missing values, encoding categorical data, exploratory data visualization, and training a Random Forest classification model. Model performance is evaluated using accuracy, classification reports, a confusion matrix, and an ROC curve

Technologies: Python, Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn