💳 Credit Card Fraud Detection

This project demonstrates Credit Card Fraud Detection using a publicly available dataset, showcasing the complete pipeline from data exploration to model deployment.

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📌 Project Overview

The goal of this project is to build a model that can accurately identify fraudulent credit card transactions.
We use the Credit Card Fraud Detection dataset and employ a Logistic Regression model, while addressing the significant class imbalance present in the data.

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📂 Dataset

• Dataset: Credit Card Fraud Detection (via fetch_openml)
• Features:
  • V1–V28 → anonymized transaction features (PCA transformed)
  • Amount → transaction amount
  • Class → target variable (0 = non-fraud, 1 = fraud)

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🛠 Steps Taken

1️⃣ Data Loading

• Loaded into a pandas DataFrame for analysis.

2️⃣ Data Exploration

• Checked data structure, missing values, and target variable imbalance.

3️⃣ Data Preprocessing

• Applied StandardScaler for feature scaling.
• Split into train/test sets with stratification to preserve class ratios.
• Applied SMOTE (Synthetic Minority Over-sampling Technique) to handle severe class imbalance.

4️⃣ Model Training

• Trained a Logistic Regression model on the SMOTE-resampled training set.

5️⃣ Model Evaluation

• Evaluated on the unseen test set using multiple metrics:
  • ✅ Precision
  • ✅ Recall
  • ✅ F1-Score
  • ✅ Confusion Matrix
  • ✅ ROC AUC Score

6️⃣ Model Deployment

• Saved the trained model for future predictions on new transactions.

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📊 Results

Key findings from model evaluation:

• Recall (Fraudulent Class): 0.92 ✅ (captures most fraud cases)
• Precision (Fraudulent Class): 0.06 ⚠️ (high false positives)
• ROC AUC Score: 0.9707 🚀 (strong discriminative ability)

🔎 Interpretation:
The model is excellent at catching frauds (high recall), but suffers from low precision (too many false alarms). Further optimization (e.g., advanced models, threshold tuning, ensemble methods) is needed.

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📦 Tech Stack

• Python 🐍
• Pandas, NumPy → Data handling
• Scikit-learn → Preprocessing, modeling, evaluation
• Imbalanced-learn (SMOTE) → Handling class imbalance

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🚀 Future Improvements

• Experiment with tree-based models (Random Forest, XGBoost, LightGBM).
• Apply threshold tuning for better precision-recall tradeoff.
• Use anomaly detection methods for fraud detection.
• Build a real-time detection system with streaming data.

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📑 References

• Dataset: Credit Card Fraud Detection on OpenML
• Imbalanced-learn Documentation: SMOTE

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🏁 Conclusion

This project provides a solid baseline for fraud detection with Logistic Regression and SMOTE.
It highlights the challenges of imbalanced data and the trade-off between recall vs. precision in financial fraud detection.