STUDENT BEHAVIOURAL DATA ANALYSIS IDENTIFY THE EDUCATIONAL FACTORS WHICH IMPACTS STUDENTS’ ACADEMIC PERFORMANCE USING ML TECHNIQUES

Machine Learning

Identifying the various elements that have an impact on a student's academic performance and learning style. In the modern educational system, analysing students' mental health problems and poor academic performance is a challenging challenge. It is challenging to monitor student behavior and characteristics under the existing system. No technology or tool exists that forecasts or provides guidance on how to adjust a student's academic performance. In the modern educational environment, determining the relationship between the variables influencing students' performance and their academic outcomes is essential. The "Association Learning" data science approach is used by the system to identify patterns. The "apriori algorithm," "apriori TID algorithm," or "Eclat algorithm" are the methods we employ to identify patterns. The suggested system is designed to be a real-time tool that helps instructors and universities understand the behavioral patterns of their students. The system also attempts to forecast each person's performance. The "naive Bayes" machine learning method is used by the system to forecast each student's performance independently.

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