This project is a machine learning-powered web application that detects fraudulent financial transactions. Built using Streamlit, it provides an interactive interface where users can input transaction details and instantly get predictions on whether the transaction is safe or fraudulent.
The model is trained using transaction data and deployed using a pipeline for seamless preprocessing and prediction.
- Interactive web app using Streamlit
- Real-time fraud prediction
- Pre-trained ML pipeline using joblib
- User-friendly input interface
- Instant results with clear visual indicators
- Supports multiple transaction types
- Python
- Pandas
- Numpy
- matplotlib
- Seaborn
- Scikit-learn
- Streamlit
- Joblib
FRAUD_DETECTION/
│── analysis_model.ipynb # Model training & analysis notebook
│── fraud_detection_pipeline.pkl # Saved ML pipeline model
│── fraud_detection.py # Streamlit web app
1️⃣ Clone the Repository
git clone https://github.com/Keerthana-webdev/Fraud-Detection.git
cd fraud-detection
2️⃣ Install Dependencies
pip install -r requirements.txt
*(If requirements.txt not available, install manually:)*
pip install streamlit pandas scikit-learn joblib
3️⃣ Run the App
streamlit run fraud_detection.py
- Select the transaction type
- Enter transaction details like:
- Amount
- Sender balance
- Receiver balance
- Click on Predict
- View result:
- ✅ Safe Transaction
- 🚨 Fraudulent Transaction
https://fraud-detection-qnsjo8ho3tgzxnepurqtrg.streamlit.app/