The Futuristic Fraud Detection System is a web application designed to detect fraudulent transactions using advanced machine learning algorithms. It consists of both backend and frontend components, providing a seamless user experience for inputting transaction data and visualizing fraud predictions.

1. Data Preprocessing

   • Load and Preprocess Data: Preprocesses transaction data by loading CSV files, handling missing values, scaling numerical features, and oversampling using SMOTE to address class imbalance.
   • Advanced Feature Engineering: Enhances data by adding advanced features like polynomial features and NLP features to improve model performance.

2. Model Training

   • Gradient Boosting Classifier: Trains a Gradient Boosting Classifier using grid search for hyperparameter tuning.
   • Model Evaluation: Evaluates model performance using metrics like ROC-AUC score and classification report.

3. Flask API

   • RESTful API: Provides a Flask API for making predictions on new transaction data.
   • API Endpoints: Exposes endpoints for predicting fraud probabilities and integrating with the frontend.

1. User Interface

   • Input Transaction Data: Allows users to input transaction data, including date, amount, and various features.
   • Prediction Chart: Visualizes fraud predictions using a line chart to display the probability of fraud over time.

2. Navigation

   • Navbar: Provides navigation links to different pages, including the dashboard, about page, and contact page.

3. Error Handling

   • Not Found Page: Displays a 404 page when users navigate to non-existing routes, providing a user-friendly error message.

• Python
• Flask
• scikit-learn
• imbalanced-learn

• React
• React Router
• Chart.js
• react-datepicker

1. Backend Setup:
   • Install Python dependencies using pip install -r requirements.txt.
   • Run the Flask server using python app.py.
2. Frontend Setup:
   • Install Node.js dependencies using npm install.
   • Start the development server using npm start.
3. Accessing the Application:
   • Open the browser and navigate to http://localhost:3000 to access the frontend dashboard.

• Implement user authentication and authorization for secure access to the application.
• Enhance the frontend UI with additional features like data visualization and user interactions.
• Deploy the application to production servers for real-world usage.

This project is licensed under the MIT License.