I've been trying the kaggle dataset of Credit card fraud detection Dataset . I've used ANN using keras and tensorflow. You can find the code in the screenshot. The only problem is im getting accuracy to be around 99.9 % , so i think it's surely a case of some false hopes or over fitting. Can you please tell whats wrong with it? And even my test set gave a result of 99.93% accuracy.
This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.
I guess your model learnt nothing at all ;)
You should consider some form of resampling and using metrics that can handle imbalance. This might be a good starting point. You can find bunch of similar threads here as well.