Fraud Detection System

Description

This project is a comprehensive fraud detection system designed to identify fraudulent transactions in a dataset. It combines a user-friendly dashboard for data visualization and analysis with a REST API for predictive modeling. Using machine learning techniques, the system identifies potential fraud and provides interactive insights.

Key Features

• Streamlit Dashboard:
  • Visualize transaction data distribution.
  • Analyze fraudulent transactions by country and type.
  • Interactive world map visualization.
• Machine Learning:
  • Random Forest Classifier for fraud detection.
  • Preprocessing with feature engineering.
• REST API:
  • Predict fraud probability using Flask.

Technologies Used

• Programming Language: Python
• Libraries: Streamlit, Flask, Scikit-learn, Plotly, Pandas, NumPy
• Visualization Tools: Plotly for interactive charts, Streamlit for dashboards
• Model Deployment: Flask for API

Installation

Prerequisites

• Python 3.8 or above
• Required libraries (see requirements.txt)

Steps

1. Clone the repository:

   git clone https://github.com/your-repo/fraud-detection-system.git

2. Navigate to the project directory:

   cd fraud-detection-system

3. Install the dependencies:

   pip install -r requirements.txt

4. Download the dataset and place it in the specified path:
   • Update the data_path variable in fraud_dashboard.py with the correct dataset location.

Usage

Streamlit Dashboard

1. Run the Streamlit application:

   streamlit run fraud_dashboard.py

2. Open the URL provided by Streamlit in your browser (default: http://localhost:8501).
3. Explore data visualizations, analyze fraud distribution, and view model results.

Flask API

1. Start the Flask server:

   python flask_api.py

Use the API to make predictions by sending a POST request to /predict.

Dataset

This project uses a transactional dataset that includes:

• Features:
  • Time_step: Timestamp of the transaction.
  • USD_amount: Transaction amount in USD.
  • Sender_Account, Sender_Country: Sender details.
  • Bene_Country: Beneficiary country.
  • Transaction_Type: Type of transaction.
• Target: Label (1 for fraud, 0 for non-fraud).

Future Improvements

• Implementing advanced machine learning techniques like ensemble methods or deep learning.
• Adding more robust error handling in the Flask API.
• Including real-time transaction monitoring capabilities.