Evaluation of Machine Learning Methods for Prediction Student Performance

Abstract

Abstract: Significant findings and useful insights have emerged from the study of using machine learning techniques to predict student performance. used large datasets with a wide variety of demographic, socioeconomic, and academic performance data to conduct in-depth evaluations of several machine learning methods. Our study highlighted the importance of careful data preprocessing, which involves fundamental steps like classifying student performance and doing in-depth exploratory data analysis (EDA). To ensure the validity of our model assessments, we meticulously split the dataset into training and testing subsets. To effectively apply machine learning models, it is necessary to convert categorical data into numerical representations; we did this by using strategic label encoding and one-hot encoding techniques. Metrics on the effectiveness of the recommended machine learning models are presented, with a spotlight on the XG Boost algorithm's remarkable 97% accuracy. The MLP's performance indicators are as follows: 95% accuracy, 98% recall, 95% precision, and 96% F1 score. When compared to these, ADA Boost has a precision of 83%, recall of 90%, F1 score of 86%, and accuracy rate of 81%. XGBoost, on the other hand, claims rates of 98% precision, 98% recall, 98% F1 score, and 97% accuracy, making it stand out from the crowd. Based on these findings, the XG Boost algorithm is the best option among the machine learning models considered for this research.