Abstract
Since the growth in online shopping, an increasing number of people are paying online using modes of payment like credit cards to pay their bills, and like every other online interface, Credit cards are also subject to hacking. online businesses and financial companies need to detect these fraudulent activities to save their clients from getting charged for items they didn’t purchase. For this purpose, banks and payment companies can implement algorithms to detect fraudulent behavior. The Credit Card Fraud Detection System includes taking into consideration past credit card transactions with the data of the ones that were fraud. This system can be used to identify a fraudulent transaction beforehand. Our objective is to identify all of the fraudulent transactions while also minimizing the incorrect fraud classifications. In this technique we first examine and pre-process the Data set to address unbalanced data, then we train our model using a Logistic regression algorithm to detect fraud.
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