2024

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A Comparative Analysis of Machine Learning Algorithms for Price Prediction of Educational Supplies
(UMT, Lahore, 2025) Muhammad Usman Ameer
This study conducts a comprehensive comparison of Linear Regression, Decision Tree, and Random Forest algorithms to predict educational supply prices, evaluated using key performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the R² score. Among these, the Random Forest algorithm demonstrated a slight advantage in predictive accuracy. However, the results suggest that all models possess significant potential for enhancement. Future research should delve into the integration of additional features, such as market trends and economic indicators, and explore the adoption of more sophisticated algorithms, including hybrid models, to further refine predictive performance. These findings offer critical insights and guidance for improving financial planning processes within educational institutions.