Prediction of Student Anxiety

This research focuses on predicting student anxiety using advanced machine learning algorithms, aiming to enhance prediction accuracy and robustness. Anxiety is a prevalent issue among students, significantly impacting their academic performance and mental well-being. Traditional methods for identifying anxiety, such as self-reported surveys, lack real-time analysis and can be subjective. To address this, the proposed project leverages various machine learning techniques to develop more reliable predictive models. These models utilize diverse datasets, including academic performance, behavioral data, and socio-emotional factors, providing a comprehensive approach to anxiety prediction.The study explores traditional algorithms like Support Vector Machines (SVM) and Decision Trees (DT) alongside advanced methods such as AdaBoost, Gradient Boosting Machines (GBM), and deep learning models like Artificial Neural Networks (ANN). A comparative analysis will be conducted to evaluate the effectiveness of each model, measuring key performance metrics such as accuracy, precision, and recall.