To develop a machine learning model that can accurately detect fraudulent claims in the education sector using historical data.
To analyze the effectiveness of different machine learning algorithms in predicting and preventing fraud in claims.
To provide recommendations for improving fraud detection processes in educational institutions through the implementation of machine learning technology.
Collect and preprocess historical data on claims in the education sector.
Build and train machine learning models using various algorithms such as logistic regression, random forests, and support vector machines.
Evaluate the performance of the models in terms of accuracy, precision, recall, and F1-score.
Conduct a comparative analysis of the different models to determine the most effective approach for fraud detection.
Develop a set of recommendations for educational institutions to enhance their fraud detection processes based on the findings of the study.
