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The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.\n\nIt contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.\n\nGiven the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.\n\nIf you want to downloads **Credit Card Fraud Detection** Dataset [Click Here](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanas436%2Fcredit-card-fraud-detection-using-machine-learning-with-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanas436%2Fcredit-card-fraud-detection-using-machine-learning-with-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanas436%2Fcredit-card-fraud-detection-using-machine-learning-with-python/lists"}