GitHub - djaym7/CreditCardFraud: The datasets contained transactions made by credit cards in September 2013 by European cardholders. The dataset presented transactions that occurred in two days, where there were 492 frauds out of 284,807 transactions. The dataset was highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. I used Logistic Regression and Random Forest to predict the class after performing oversampling of the minority class using SMOTE (Synthetic Minority Oversampling Technique), which resulted in the equal distribution of both the classes (0 and 1). Used the following modules in Python-Pandas, NumPy, Scikit-Learn, matplotlib, Imbalanced-learn Logistic Regression gave AUC=0.95, but the precision was very low (0.08). So, I tried Random Forests and it gave AUC=0.94 and precision=0.85 (with 'gini' criterion) and AUC=0.92 & precision=0.83 (with 'entropy' criterion). Measured performance metrics-accuracy, precision, recall, AUC and plotted ROC curve Used the following modules in Python-Pandas, NumPy, Scikit-Learn, matplotlib, Imbalanced-learn

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The datasets contained transactions made by credit cards in September 2013 by European cardholders. The dataset presented transactions that occurred in two days, where there were 492 frauds out of 284,807 transactions. The dataset was highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. I used Logistic Regressio…