Intelligent Learning Outcome Prediction Using Machine Learning Techniques

Abstract

Student Performance Prediction Is An Important Application Of Educational Data Mining That Helps Institutions Identify Students Who May Require Academic Support At An Early Stage. This Study Proposes A Classification-based Approach To Predict Student Performance Using Historical Academic And Demographic Data. Various Factors Such As Attendance, Internal Assessment Marks, Study Habits, Previous Academic Records, And Participation In Extracurricular Activities Are Considered As Input Attributes. Classification Algorithms Such As Decision Tree, Random Forest, Naive Bayes, And Support Vector Machine (SVM) Are Employed To Categorize Students Into Performance Classes Such As Excellent, Good, Average, And Poor. The Dataset Is Preprocessed Through Data Cleaning, Feature Selection, And Normalization To Improve Prediction Accuracy. Experimental Results Demonstrate That Machine Learning Classification Techniques Can Effectively Predict Student Outcomes And Assist Educators In Making Informed Decisions. The Proposed Model Enables Timely Intervention, Improves Academic Planning, And Contributes To Enhancing Overall Student Success Rates In Educational Institutions.

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