🚨 Fraud Detection using Machine Learning
This project demonstrates how to build a fraud detection system using Machine Learning techniques. It analyzes transaction data to classify whether a transaction is fraudulent or legitimate. The goal is to help financial institutions, payment gateways, and businesses prevent losses caused by fraudulent activities.
📌 Features
✅ Data preprocessing & cleaning
✅ Exploratory Data Analysis (EDA) with visualizations
✅ Machine Learning model training & evaluation
✅ Fraud vs. Legit transaction classification
✅ Streamlit app for interactive testing (optional)
🛠️ Tech Stack
Programming Language: Python 🐍
Libraries & Tools:
pandas, numpy → Data handling
matplotlib, seaborn → Visualization
scikit-learn → ML algorithms
📂 Project Structure fraud-detection-ml/ │-- data/ # Dataset files (not included in repo for size/sensitivity) │-- notebooks/ # Jupyter notebooks for EDA & model training │-- src/ # Source code │ ├── preprocessing.py │ ├── train_model.py │ ├── predict.py │-- app/ # Streamlit app (if included) │-- requirements.txt # Project dependencies │-- README.md # Project documentation │-- fraud_detection.pkl # Saved ML model
📊 Dataset
The dataset used is a credit card transactions dataset (commonly from Kaggle ), which contains highly imbalanced data with only a small percentage of fraud cases.
🚀 Installation & Usage 1️⃣ Clone the repository git clone https://github.com/workforyou786/Fraud_detection_using_Machine_Learning.git cd fraud-detection-ml
2️⃣ Create a virtual environment & install dependencies python -m venv venv source venv/bin/activate # For Mac/Linux venv\Scripts\activate # For Windows
pip install -r requirements.txt
3️⃣ Train the model python src/train_model.py
4️⃣ Run predictions python src/predict.py --input sample_transaction.json
5️⃣ (Optional) Run the Streamlit App streamlit run app/app.py
📈 Model Performance
Accuracy: ~99%
Precision, Recall, F1-score reported for fraud detection
ROC-AUC curve plotted for model evaluation
📌 Future Improvements
🔹 Integrate Deep Learning models (LSTM / Autoencoders)
🔹 Deploy as an API using FastAPI/Flask
🔹 Real-time fraud detection with Kafka or Spark
🤝 Contributing
Contributions are welcome!
Fork the repo
Create your feature branch (git checkout -b feature-name)
Commit changes (git commit -m 'Add feature')
Push to branch (git push origin feature-name)
Create a Pull Request
🙌 Acknowledgements
Dataset: Kaggle - Credit Card Fraud Detection
Inspiration: Real-world fraud detection systems in banking & fintech
imbalanced-learn (SMOTE) → Handling class imbalance
streamlit → Deployment (optional)