This proejct contains data analysis of famous Credit Card Fraud dataset. Proejct was based on imbalance dataset classification. By performing some tactics to Combat Imbalanced Classes we got 99.9% accuracy.
Classification Report :
precision recall f1-score support
False 1.00 1.00 1.00 56845
True 1.00 0.98 0.99 117
accuracy 1.00 56962
macro avg 1.00 0.99 1.00 56962
weighted avg 1.00 1.00 1.00 56962
- Python 3.5–3.8
- pip 19.0 or later (requires manylinux2010 support)
- Ubuntu 16.04 or later (64-bit)
- macOS 10.12.6 (Sierra) or later (64-bit) (no GPU support)
- Windows 7 or later (64-bit)
- Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019
- GPU support requires a CUDA®-enabled card
- Pip install tensorflow
- Pip install keras
- Pip install seaborn
- Pip install pandas
- Pip install numpy
Graphs of Loss, AUC, Precision, Recall during Training: