Credit card companies must have the ability to identify fraudulent credit card transactions in order to stop customers from being charged for goods they did not purchase. These problems may be resolved with data science, and when combined with machine learning, it is extremely important. This study seeks to show how machine learning may be used to model a data set using credit card fraud detection. The Credit Card Fraud Detection Problem includes modelling prior credit card transactions using data from those that turned out to be fraudulent. Then, this model is used to analyse a new transaction to determine whether or not it is fraudulent. The objective is to detect 100% of the fraudulent transactions while minimising erroneous fraud categories. Due to the E-Commerce sector`s recent explosive expansion, fraudulent credit card transactions have cost incredibly significant sums of money. An effective method to stop these fraudulent transactions is to use a strong model based on cutting-edge machine learning algorithms that can handle massive volumes of data while still producing precise findings. In this study, the effectiveness of decision trees, random forests, and linear regression for identifying credit card fraud is compared.

Outliers, Decision Tree, Confusion Matrix, Isolation Forest, Logistic Regression, Naive Bayes Classifier, Credit Card Fraud

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