Journal Press India^®

Author Details ( * ) denotes Corresponding author

The integration of the machine learning (ML) model in education has brought a revolution in the way the student’s performance is analysed, predicted and improved. This research examines the application of supervised teaching algorithms including random forests, naive bayes, and recurrent neural network (RNN) to predict student results based on major educational and behavioural indicators. Facilities such as subject-wise marks, attendance percentage, extra-curricular participation and behavioural evaluation score are used to develop future-stolen models. The comparative analysis of these ML models shows significant changes in accuracy, accuracy, and memories, highlighting the strength and limitations of various approaches. Random One Classifier provides high accuracy in structured data scenarios, while naive bayes provide efficiency with classified features. Meanwhile, RNN-based deep learning models capture sequential patterns in student behaviour, increasing predicting reliability. The study further examines the effect of convenience selection on model accuracy and data preprocessing, emphasizing the requirement of balanced dataset and bias mitigation. By taking advantage of machine learning to predict student performance, this research contributes to data-making decision making in educational institutions, enabling active intervention and individual teaching passage. Conclusions underline the ability of AI-engaging analytics to increase educational success and support educational strategies.

Keywords

Student performance prediction; Machine learning algorithms; AI in education; Adaptive learning; Model accuracy analysis

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