Regression and Ensemble Models for Analyzing Students Key Performance Factors

Predicting student performance can help identify when students need extra support and tailor interventions to improve outcomes. In this study, we use machine learning to explore the factors influencing student success and build the models that can predict. We apply several algorithms , including Linear Regression , “Random Forest Regression” , “ Gradient Boosting Regression”, “ Support Vector Regression”, “ K-Nearest Neighbors Regression” , each chosen for their ability to capture different patterns in data , whether linear or complex . The models are trained on factors such as attendance, socioeconomic status, parental education , and academic history to understand how these impact student achievement and learning . Our findings show that ensemble techniques like Gradient Boosting and Random Forest tend to provide more accurate predictions than traditional regression models. By comparing these models, we aim to offer insights into how machine learning can help predict and support student performance, helping educators make more informed decisions about learning. This also helps personalized decision-making to support learners effectively.