'Correction to: Identifying Oil Spill Areas and Causes Using a Deep Learning Model' published in 'New Trends in Information and Communications Technology Applications'

Online payment methods for e-commerce and many websites in various fields have increased significantly. Therefore, credit card frauds are easy targets, and their rate is on the rise which poses a major problem for online payments. The basic concept is to examine consumers' purchasing histories to extrapolate their typical behavior patterns, classify cardholders into different groups, and then attempt to detect credit card fraud. Credit card fraud detection based on a machine learning model uses a combination of supervised and unsupervised learning techniques such as Random Forest, Decision Tree, Logistic Regression, and Extreme Gradient Boost. We used the synthetic minority oversampling (SMOTE) technique to balance the dataset. The model is trained on a large set of data related to credit card transactions and uses features such as transaction amount, transaction location, and time of day to identify patterns and anomalies in the data that indicate fraudulent activity. Our goal is to build a model based on machine learning technology that detects and analyzes online shopping fraud Detecting fraud in credit card systems is crucial to protecting consumers from financial losses and maintaining the integrity of the financial ecosystem after collecting Creditcard.csv data. With the help of several algorithms, including Random Forest (RF) algorithm accuracy reached 99%, Logistic Regression (LR) algorithm accuracy reached 97%, and Decision Tree (DT) algorithm accuracy reached 99% Researchers provide a comprehensive method for identifying fraud in credit card transactions Precision Recall F1-score. The proposed system includes four main steps: pre-processing, classification using the algorithm, and checking whether the transaction is fraudulent or not. Fig. 8 describes each stage in context. Index Terms— Detecting fraud, machine learning, Decision Tree, Random Forest, Logistic Regression.