Design a model to recognize fraudulent credit card transactions.
The dataset is available on Kaggle.
- Instead of actual features, PCA components are available in the dataset along with transaction amount and elapsed time.
- Data-Splits: Train/Dev/Test
- Perform basic data exploration.
- Check for missing values.
- Check for outliers.
- Perform univariate analysis (using t-test).
- Perform bivariate analysis.
- Feature Scaling: RobustScaler is used in logistic regression modeling.
- Feature Selection: Recursive Feature Elimination algorithm is used to determine the best feature combination.
- Data Imbalance: SMOTEENN Hybrid and ClusterCentroid downsampling algorithms have been experimented with to address class imbalance problems.
- Hyperparameter Tuning: GridSearchCV algorithm is used to set the hyperparameter values.
- Area Under Precision-Recall curve is used as the main metric in imbalanced data settings.
- Also, the F1-score is reported.
| Model | F1-score | AUPR |
|---|---|---|
| Logistic Regression | 0.76 | 0.59 |
| Decision Tree | 0.67 | 0.60 |
| Random Forest | 0.72 | 0.63 |
| XGBoost | 0.67 | 0.61 |