credit card fraud detection using random forest algorithm
hardik jethava, Feon Jaison
Dataset, Credit card, Random Forest Algorithm.
Financial fraud is a constant threat to the financial industry, with far-reaching effects. In order to detect credit card fraud in internet transactions, data mining was essential. Due to two fundamental causes, detecting credit card fraud, which is a data mining challenge, gets difficult: first, genuine and fraudulent behaviours profiles are constantly changing, and second, credit card fraud data sources are extremely skewed. The methodology used to gather the dataset, the parameters used, and the method(s) utilised to identify fraud in credit card transactions all have an influence on the accuracy of fraud detection. The performance of several classification algorithms on significantly unbalanced credit card fraud data is investigated in this study. This study collected a total of 284,807 credit card transactions from European consumers. On the skewed statistics, a hybrid strategy of under and up sampling is used. The raw and transformed data are subjected to the one approach. To finish the task, Python is used. The techniques' accuracy tested.
Article Details
Unique Paper ID: 154440

Publication Volume & Issue: Volume 8, Issue 11

Page(s): 414 - 419
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