MACHINE LEARNING BASED STUDENT PERFORMANCE ANALYSIS SYSTEM

The academic output of the student is normally stored in various formats administration system (files, documents, records, photographs and other formats). These data can be collected for valuable knowledge from students. However, it difficult to analyze increasing amount students through conventional statistical techniques database management tools. For universities gather information, a tool therefore required. This helpful used predict success Leistungs learning results framework that aims areas interest at levels dimensions. paper proposes complete structure as rule-based recommendation method not only analyzing forecasting but also presenting their reasons. proposed analyzes demographic details pupils, studies psychological features so students, teachers parents collect all possible knowledge. To seek maximum accuracy predictions across range powerful mining. successfully recognizes limitations makes adequate recommendations. practical case study on 200 indicates excellent performance compared current framework.