Application of Regression Analysis of Student Failure Rate

The education sector has been rapidly growing and is currently facing several challenges. One such challenge is identifying students who are at risk of failing, as this can help educators provide targeted interventions to improve student performance. Machine learning models have been developed to predict the probability of student failure based on various student performance metrics to address this issue. In this paper, we present a regression-based model that predicts the probability of student failure using student performance metrics such as attendance, previous academic performance, and demographic information. The model was trained on a dataset of students and achieved high accuracy in predicting the probability of student failure. While the model performs well in predicting the probability of student failure, there is always room for improvement. Possible enhancements to the model include feature engineering, ensemble learning, hyperparameter tuning, deep learning, and interpretability. These enhancements can improve the model’s accuracy, stability, and transparency, leading to better predictions and targeted interventions for at-risk students.