Online Payment Fraud Detection data from Kaggle
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Completed SHAP analysis for Naive Bayes (KernelExplainer) and Random Forest Classifier (TreeExplainer)
ecb95b3View on GitHubMade some changes to prepare to use SHAP on final two models
1611a19View on GitHubMade small changes to the files as I updated my powerpoint documenting my methodology
628613cView on GitHubFormated the testing data and tested it against the two final models
6f59adeView on GitHubFinal models are ready to be tested against final test data. Next steps are to prepare the test data
60710a1View on GitHubPicked final models for each dataset (tomek, tomek+random undersampling) after tuning them using optuna. Gaussian Naive Bayes worked best for the tomek data, and Random Forest Classifier worked best for the tomek+rus data.
f32ff8aView on GitHubCompleted run through of models on the two datasets (tomek, tomek+rus), and now we are ready for optuna
9dc3533View on GitHubFinished processing data, now we can move to the machine learning portion
8e5afa7View on GitHubFound transformation method that works for numerical data
77cc2cfView on GitHubStarted data preprocessing, and setup code to look at different data transformations
666db71View on GitHub