Predicting Student Performance using Linear Regression - Max Kennedy Kassy (PT. Visi Media Network, 2025)
This study explores how to measure and predict student performance using various machine learning algorithms to determine the model that produces the best predictions. The collected data is obtained from the Kaggle data science and machine learning community website, obtaining a dataset with 6 attributes, namely: (1) Hours Studied, (2) Previous Scores, (3) Extracurricular Activities, (4) Sleep Hours, (5) Sample Question Papers Practiced, and (6) Performance Index. The data was cleaned and explored using Microsoft Excel, Google Colab and Tableau. Model development using RapidMiner and Google Colab. The algorithms used for the study were: k-NN, SVM, Linear Regression, Generalized Linear Model, Deep Learning. The Root Mean Squared Error (RMSE) results obtained by the algorithm were 2,455 (k-NN), 2,072 (SVM), 2,013 (Linear Regression), 2,030 (Generalized Linear Model), 2,364 (Deep Learning). From the RMSE it can be seen that the algorithm that gets the best results is Linear Regression, after being retested, Linear Regression gets an RMSE of 2.015, and Root Squared (R2) of 0.989, meaning the Linear Regression algorithm has an accuracy of 98.9%.