It is a course project of course "Pattern Recoginition and Machine Learning".
Developed a project to address imbalanced data in the Credit Card Fraud Dataset, employing variour Machine Learning algorithms for under-sampling, over-sampling, and a combination of both techniques.
Employed additional strategies such as cost-sensitive learning and ensemble methods to further enhance the accuracy and effectiveness of the models in detecting credit card fraud, ensuring robust performance across both majority and minority classes.