Fraud Transaction Detection is a machine learning-based project designed to identify and prevent fraudulent transactions. This project leverages a trained model to classify transactions as either legitimate or fraudulent, ensuring secure and reliable financial operations.
- Pre-trained fraud detection model (
fraud_model.pkl) - Easy-to-use interface for transaction classification
- Modular code structure for extensibility
The project utilizes the following technologies and tools:
- Python: The primary programming language used for building the application.
- Pandas: For data manipulation and preprocessing.
- Scikit-learn: For machine learning model training and evaluation.
- Flask and Streamlit: For building the web application interface.
- Pickle: For saving and loading the pre-trained model.
- pip: For managing Python dependencies.
- Virtual Environment: Recommended for isolating project dependencies.
- Git: For version control and collaboration.
- Compatible with Windows, macOS, and Linux.
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Data Preprocessing: The input transaction data is preprocessed using utility functions in utils.py.
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Model Loading: The pre-trained model (fraud_model.pkl) is loaded using the model.py script.
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Prediction: The application takes transaction data as input and uses the model to predict whether the transaction is fraudulent or legitimate.
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Output: The result is displayed to the user, indicating whether the transaction is safe or fraudulent.
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Project Structure : Fraud_Transaction_Detection/
├── .gitignore├── README.md├── app.py├── fraud_model.pkl├── model.py├── requirements.txt└── utils.py
- app.py: Main application script.
- model.py: Script for loading and interacting with the fraud detection model.
- utils.py: Utility functions for data preprocessing.
- fraud_model.pkl: Pre-trained fraud detection model.
- requirements.txt: List of dependencies.