💳 Credit-Card-Fraud-Detection-With-Machine-Learning-Algorithms 🔐🤖

Credit-Card-Fraud-Detection-With-Machine-Learning-Algorithms is a data science project focused on building predictive models to detect fraudulent credit card transactions. Using supervised learning algorithms, it analyzes transaction patterns and identifies anomalies to reduce financial fraud risks.

✨ Key Features

📂 Dataset Handling – Preprocessing highly imbalanced credit card datasets

⚖️ Imbalance Techniques – SMOTE, undersampling, oversampling for fair training

🤖 Machine Learning Models – Logistic Regression, Random Forest, Decision Trees, XGBoost

📈 Model Evaluation – Precision, Recall, F1-score, ROC-AUC, Confusion Matrix

🔍 Feature Engineering – Transaction amount scaling, PCA for dimensionality reduction

📊 Data Visualization – Fraud vs. non-fraud transaction analysis with plots & heatmaps

🧪 Experimentation – Compare multiple ML algorithms for best fraud detection accuracy

🌐 Optional Web App – Streamlit/Flask app for real-time fraud detection simulation

🧰 Tech Stack

Programming: Python 🐍

Libraries: Pandas, NumPy, Matplotlib, Seaborn

Machine Learning: Scikit-learn, XGBoost, LightGBM

Data Processing: Imbalanced-learn, PCA

Deployment (Optional): Streamlit / Flask

📁 Project Structure 📁 data/ # Dataset (public credit card fraud dataset) 📁 notebooks/ # Jupyter notebooks for preprocessing & ML models 📁 src/ # Training scripts & utility functions 📁 results/ # Metrics, confusion matrices, and visualizations 📁 app/ # (Optional) Web app for fraud detection

🚀 Getting Started git clone https://github.com/yourusername/Credit-Card-Fraud-Detection-With-Machine-Learning-Algorithms.git cd Credit-Card-Fraud-Detection-With-Machine-Learning-Algorithms pip install -r requirements.txt jupyter notebook

📌 Use Cases

🏦 Banks & Financial Institutions – Secure transactions by detecting fraud in real time

💳 Payment Gateways – Integrate ML-based fraud detection systems

📊 Data Science Research – Explore imbalance learning and anomaly detection techniques

🎓 Education – Practice ML on a real-world fraud detection dataset

🤝 Contributing

Contributions are welcome! Improve models, optimize pipelines, or add deep learning approaches and submit a PR.

📜 License

MIT License – Open for research, academic, and personal use.