Fraud-Transaction-Detection

Detect fraudulent transactions using a provided, real-world dataset of 150,000 financial records. This project focuses on applying exploratory data analysis (EDA), feature engineering, and advanced classification models to accurately identify fraudulent behavior.

📊 Project Overview

Objective:
Detect fraudulent transactions in a provided, real-world dataset of 150,000 financial records. This project focuses on applying exploratory data analysis (EDA), feature engineering, and advanced classification models to accurately identify fraudulent behavior and reduce financial risk.

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Tools & Techniques

• Python (Pandas, NumPy, Scikit-learn, Imbalanced-learn)
• Jupyter Notebook
• Data visualization (Matplotlib, Seaborn)
• Classification models (Logistic Regression, Random Forest, XGBoost)
• Evaluation metrics (Precision, Recall, F1-score)

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Key Steps

• Analyzed transaction patterns and identified distinguishing fraud features
• Engineered new features to improve model learning
• Applied multiple classification algorithms and compared performance
• Handled severe class imbalance using resampling and weighting techniques
• Evaluated models using precision, recall, F1-score, and ROC-AUC
• Visualized results with ROC curves, confusion matrices, and feature importance plots

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Key Insights from Model

• Model_Performance:
  Achieved high recall on fraudulent cases, balancing sensitivity and precision to minimize both false positives and false negatives, critical in fraud detection.

• Feature_Impact:
  Identified key transaction characteristics (such as amount patterns, frequency, and anomalies) most indicative of fraud, improving model interpretability and actionable value.

• Business_Insight:
  Highlighted behavioral and transactional trends distinguishing fraudulent activity, offering practical insights for improving fraud monitoring systems.

• Visual_Takeaways:
  ROC curves and confusion matrices provided clear, interpretable evaluations of model performance, guiding optimization choices and model selection.

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Project Files

• fraud_detection_model.ipynb → Main Jupyter notebook with full analysis and modeling
• fraud_data/ → Dataset files
• plots/ → Visual outputs (ROC curves, confusion matrices, feature importance)

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Outcome

Demonstrated the ability to tackle high-stakes, real-world problems by detecting rare fraudulent transactions in a large, imbalanced dataset. Combined strong technical modeling with critical thinking to balance precision and recall, ensuring the solution was both technically sound and business-relevant. Delivered clear, interpretable insights that could help stakeholders reduce financial losses and improve operational decision-making.