This project aims to develop a robust fraud detection model to identify fraudulent transactions within a highly imbalanced dataset. Using advanced machine learning techniques and comprehensive data analysis, we created a model that can effectively distinguish between genuine and fraudulent transactions, providing enhanced security and reliability for financial transactions.

• Data Preprocessing:

  • Applied log transformation to the Amount feature to reduce skewness.
  • Standardized features using RobustScaler for consistent scaling.
  • Addressed class imbalance with SMOTE (Synthetic Minority Over-sampling Technique).

• Exploratory Data Analysis (EDA):

  • Visualizations like histograms, pairplots, and heatmaps demonstrate feature distributions and relationships.
  • Identified key patterns and insights that differentiate fraudulent transactions from non-fraudulent ones.

• Model Selection and Hyperparameter Tuning:

  • Evaluated basic Logistic Regression, Random Forest, Gradient Boosting, and XGBoost models.
  • Used GridSearchCV for hyperparameter tuning with Random Forest and XGBoost, optimizing for average precision.

• Model Performance Evaluation:

  • Compared models based on metrics like precision-recall AUC, and F1-score.
  • Visualized performance using ROC and precision-recall curves.

• Final Model Selection:

  • Selected XGBoost as the final model due to its superior balance of precision and recall, achieving an excellent Precision-Recall AUC of 0.89 and the best F1 score among tuned models.

• High Accuracy and Reliability: The XGBoost classifier is highly accurate and reliable for detecting fraudulent transactions. This ensures that genuine transactions are processed smoothly while effectively catching most fraud cases.
• Customer Confidence: Provides customers with a reliable system that minimizes interruptions to legitimate transactions.
• Continuous Improvement: Emphasizes the need for ongoing monitoring and enhancement to adapt to new fraud patterns.

• Tuning Enhancement: Further tuning and evaluating additional hyperparameters.
• Ensemble Methods: Exploring combinations of multiple models for better results.