Credit Card Fraud Detection using XGBoost

Overview

This project demonstrates a fraud detection system using XGBoost, Pandas, Seaborn, and Scikit-learn. The dataset consists of anonymized credit card transactions, where the goal is to classify transactions as fraudulent (1) or non-fraudulent (0).

Dataset

• The dataset used is creditcard.csv, which contains transactions with fraud labels.
• Due to class imbalance, only ~0.17% of transactions are fraudulent.

Project Steps

1️⃣ Data Exploration & Visualization

• Check for missing values and class imbalance.
• Visualizations: Class distribution, transaction amount distribution, correlation heatmap, and boxplot.

2️⃣ Data Preprocessing

• Normalize transaction amounts using StandardScaler().
• Remove unnecessary columns (Time, Amount).
• Split dataset into training (80%) and testing (20%).

3️⃣ Model Training with XGBoost

• Train an XGBoost classifier with logloss as the evaluation metric.
• Predict fraudulent transactions.

4️⃣ Model Evaluation

• Generate classification report (Precision, Recall, F1-score, Accuracy).
• Display confusion matrix as a heatmap.
• Plot the AUC-ROC curve to measure fraud detection effectiveness.

Installation & Usage

1. Install required dependencies:

   pip install pandas numpy seaborn matplotlib scikit-learn xgboost

Results

• Fraudulent transactions are successfully classified with high recall.
• XGBoost performs well in handling class imbalance and optimizing fraud detection.
• AUC-ROC Curve helps visualize the model's predictive power.

Contributions

Feel free to fork, improve, and submit pull requests! 🚀

License

This project is open-source and available under the MIT License.