• Vaibhav Dilip Takle
• Vallabh Nandal
• Chetan Ujade
• Priyanka Rupanawar
• Shraddha Khonde

Financial fraud is a rising problem in the financial industry with long-term ramifications, and while numerous strategies have been developed to address this issue, To automate the evaluation of enormous volumes of sophisticated data, data mining has been successfully used in financial databases. Data mining has also played a crucial role in the identification of credit card fraud in online transactions. Credit card fraud detection is a data mining challenge. It becomes difficult for two reasons: For starters, regular and fraudulent behavior patterns differ a lot, and second, credit card fraud data sets are heavily skewed. On severely skewed and unbalanced credit card fraud data, this study explores and compares the performance of Logistic Regression, XGBoost, with several sampling methodologies such as under-sampling, over-sampling, SMOTE, and Decision Tree. European cardholders provided a credit card transaction dataset with 284,786 transactions. Both raw and pre-processed data are used in these procedures. The methods' accuracy, sensitivity, precision, and recall are utilized to assess their performance. The results demonstrate that the most accurate classifiers are Logistic Regression, XGBoost, and Decision Tree.

"CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 4, page no.c438-c445, April-2022, Available :http://www.jetir.org/papers/JETIR2204262.pdf

"CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 4, page no. ppc438-c445, April-2022, Available at : http://www.jetir.org/papers/JETIR2204262.pdf