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Intelligent Student Academic Performance Prediction Using Machine Learning
Academic success is influenced by several factors, including students’ learning habits, attendance, participation, and overall academic engagement. This project presents a machine learningbased approach to predict student academic performance using historical educational data. The dataset is first preprocessed by removing inconsistencies, handling missing values, and preparing the features for model training. Multiple machine learning algorithms, including Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), Logistic Regression, Gradient Boosting, and XGBoost, are trained and evaluated using performance measures such as accuracy, precision, recall, and F1-score. The experimental results indicate that XGBoost achieves the best prediction accuracy compared to the other models. The proposed system also provides a simple graphical user interface that allows users to load datasets, train models, predict student performance, and visualize results through graphs. This intelligent prediction system can help educators identify students who may require additional academic support, enabling timely interventions and improving overall educational outcomes.
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