Student Performance Prediction Using Machine Learning

Student academic performance prediction has become an important application of machine learning in the field of educational data mining. Educational institutions collect large amounts of student data such as attendance records, assignment scores, previous academic results, and study behavior. Analyzing this data can help identify patterns that influence student success. This study proposes a machine learning based approach to predict student academic performance using historical academic and behavioral data. The dataset is first preprocessed by handling missing values, cleaning data, and transforming features. Exploratory Data Analysis (EDA) is performed to understand relationships between different variables. Machine learning algorithms such as Random Forest, Support Vector Machine (SVM), and XGBoost are applied to build predictive models. The trained models are evaluated using performance metrics such as accuracy and precision. The results show that machine learning techniques can effectively predict student performance and help identify weak students at an early stage. This system can assist educators in providing timely support and improving overall student learning outcomes.