Fraud Detection in Credit Card Data Using Supervised Machine Learning Based Scheme

bstract: In fraud detection, Decision Trees, Random Forest, and XG Boost are utilized for their effectiveness in classifying transactions. Decision Trees create a model that splits data based on featurevalues, forminganintuitivetreestructure thatleadstofinalclassifications.RandomForest improves uponthis byusing multiple DecisionTrees withrandomdata subsets, aggregating their predictions to enhance accuracy and reduce overfitting. XG Boost employs a gradient boosting approach, building trees sequentially and optimizing performance through techniques like regularizationandparallelprocessing. Together,thesealgorithmsformarobustsystemcapableof adapting to complex transaction patterns while minimizing false positives and negatives. The algorithmcreates manydecision trees during training, each trained with a specific random noise. The algorithm then uses the results from all the trees to make a prediction