I am training and testing the following five models for fraud prediction on a credit card fraud data set (available on Kaggle)
- Logistic regression (regular)
- Artificial Neural Network (regular)
- Cost-sensitive Artificial Neural Network
- Cost-classification Logistic regression
- Cost-classification Artificial Neural Network
All models are evaluated with 5-fold cross-validation in terms of both, F1-score and cost savings
For a detailed description of this project, please refer to the article here
main.py: Train, test and evaluate all models
eval_results.py: Function to evaluate results
ANN.py: Artificial Neural Network with custom loss function (built in Keras)
results.ipynb: Generate plots to visualize results
results folder that contains results generated by running main.py in .npy file format