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It is vital that credit card companies are able to identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Such problems can be tackled with Data Science and its importance, along with Machine Learning, cannot be overstated. This project intends to illustrate the modelling of a data set using machine learning with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem includes modelling past credit card transactions with the data of the ones that turned out to be fraud. This model is then used to recognize whether a new transaction is fraudulent or not. Our objective here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. Credit Card Fraud Detection is a typical sample of classification. In this process, we have focused on analysing and pre-processing data sets as well as the deployment of XG Boost on Credit Card Transaction data. We use grid search to avoid over-fitting and compare the performance of both XGBoost and P-XGBoost and other classical machine learning methods. It turns out that P-XGBoost outperforms XGBoost in fraud detection, which provides a new perspective to detecting the fraud behaviour while protecting clients’ privacy.


Dimensionality Reduction technique :

Kernel PCA

ALgorithm :


Course Curriculum

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