To develop a machine learning model that can effectively detect and prevent credit card fraud in educational transactions.
To analyze the current trends and challenges in credit card fraud within the context of the education sector.
To evaluate the performance of various machine learning algorithms in detecting fraudulent activities in educational credit card transactions.
To propose recommendations for improving fraud detection and prevention strategies in educational institutions using machine learning techniques.
Conduct a literature review on credit card fraud detection and prevention methods in the education sector.
Collect and preprocess a dataset of credit card transactions from educational institutions.
Implement and evaluate different machine learning algorithms for fraud detection, such as logistic regression, decision trees, and neural networks.
Develop a predictive model that can accurately classify fraudulent and legitimate transactions.
Compare the performance of the machine learning model with existing fraud detection methods in the education sector.
