CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING

The detection of credit card fraud is a widespread concern in the current era.(This is due to the rise in both online transactions and e-commerce platforms.)Unauthorized persons generally use stolen credit cards or employ credit card information for unauthorized purposes, resulting in credit card fraud.Due to the increase in cybercrime, the credit card fraud detection system was introduced to detect fraudulent users.In this project, a model is built using machine learning classification algorithms such as logistic regression, random forest, decision tree, naive bayes, and passive aggressive algorithms for detecting credit card fraud.The credit card dataset is collected from the Kaggle website, uploaded, and examined for fraud and non-fraud transactions.The model is trained and tested on the dataset, classified using different machine learning algorithms, and its performance is measured using accuracy.The comparison between the different algorithms is performed, and the best algorithm is specified.