Data:
The train dataset contains transactions made by credit cards over two days in September 2013 by european cardholders. The dataset is highly unbalanced, the positive class (frauds) account for roughly 0.17% of all transactions.
The data contains only numerical input variables which are the result of a PCA transformation. Due to confidentiality issues, we cannot provide the original features and background information about the data (this is common in many neural network problems). 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.
The test dataset is the data that you will score using your model. Submit your predictions for the test dataset for evaluation.