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MACHINE LEARNING BASED STUDENT PERFORMANCE ANALYSIS SYSTEM

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