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A machine-learning project on detection of fraudulent credit card transactions.

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Credit Card Fraud Detection

DSML (ECS308/DSE302)

The given dataset contains transactions made by credit card users in September 2013 by European cardholders. It presents transactions that occurred in two days, where there were 492 frauds out of 284,807 transactions. The dataset is highly unbalanced. It contains only numerical input variables which are the result of a PCA transformation.

Unfortunately, due to confidentiality issues, the original features cannot be provided. 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. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.

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