COMPREHENSIVE STUDENT PERFORMANCE ANALYTICS USING ETL AND NEO4J

This paper provides how Extract, Transform, Load (ETL), Neo4j database, and machine learning can be used to get insights about student performance at higher educational institutions.By collecting data from many sources like student demographics and academic records, the ETL process makes sures that data consistency is maintained using JSON schema.Neo4j is used for storage and analysis of data.Machine learning models like linear regression, logistic regression, and time series forecasting are used to predict student results.In addition, the research compares performance results among urban and rural students, as well as male and female students.The results indicate the significance of combining data processing, graph databases, and machine learning in the field of education.