Abstract
Abstract: This research aims to predict student academic performance using historical data and machine learning algorithms. The dataset includes parental, and academic information about students. The study focuses on three machine learning algorithms: Logistic Regression, Decision Tree, and Support Vector Machine (SVM). To begin, we conducted data analysis to understand the distribution and relationships within the data. Visualizations such as homogeneity analysis of parental education, race, and gender, as well as count plots for gender according to parental education and race, were created to identify patterns and insights. The data was then pre-processed and used to train the three models. Each model's performance was evaluated based on accuracy, precision, recall, and F1 score. Confusion matrices and ROC curves were also generated to provide a comprehensive evaluation of each model's predictive power.
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