Predicting Students' Performance Using Machine Learning Algorithms using Python

Abstract

The prediction of student academic performance is essential for improving educational   outcomes. It allows for timely, data-driven interventions. This study examines how machine learning (ML) techniques can be used to create a predictive model for student performance. We used a dataset that includes key factors such as demographic information, previous academic records, social and extracurricular activities, and engagement metrics. Several ML algorithms were trained and evaluated. The study specifically looks at the effectiveness of models like Decision Trees, Random Forest, Support Vector Machines (SVM), and Logistic Regression.
The research follows a structured process involving data pre-processing, feature engineering, model training, and hyperparameter optimization to improve predictive accuracy. The results show that ensemble methods, especially Random Forest, provided the best performance by accurately identifying students at risk of underperforming. The most significant predictive features were previous grades and attendance records, which emphasize the importance of historical academic data.
This work concludes that machine learning offers a strong and reliable way to predict student performance. The model created can act as an early warning system for educators and institutions. It helps to set up proactive support systems that can improve student retention and success. The findings highlight the potential of using predictive analytics in educational management systems to create a more personalized and effective learning environment.