“AI-Powered Fake Currency Detection System” aims to leverage machine learning techniques along with the Django web framework to develop an automated, fast, and user-friendly solution for identifying counterfeit currency notes. The system allows users to upload an image of a currency note, which is then processed and evaluated by a trained machine learning model. Based on the extracted features, the model predicts whether the note is Genuine or Fake and provides a confidence score for the prediction.
The Django framework handles user login, signup, profile management, image uploads, and history storage. It offers a clean and interactive web interface that makes the system easy for anyone to use. The combination of ML and Django demonstrates how technology can help solve real-world problems more efficiently.
Key Features of the System
1. Machine Learning–Based Detection
- Uses a trained Random Forest classifier to identify fake or genuine currency.
- Image preprocessing (grayscale, resizing, flattening) ensures accurate predictions.
- Generates prediction confidence score.
2. User-Friendly Web Interface
- Built using Django templates with clean and responsive design.
- Simple upload form for users to submit currency note images.
- Instant display of prediction results.
3. Secure User Authentication
- User registration, login, logout, and session handling.
- Password change and profile editing features.
- Secure password hashing and validation.
4. Prediction History Tracking
- Stores all past predictions with timestamps.
- Allows deletion of individual history records.
- Includes image preview and pagination for easier navigation.
5. Integrated Preprocessing & Model Pipeline
- Images are automatically processed before prediction.
- Model loads efficiently and returns real-time results.
6. Database Management
- SQLite database for storing user details and prediction records.
- Django ORM ensures secure and efficient data handling.
7. Extendable System Architecture
- Easy to upgrade with deep learning models like CNN.
- Can support multiple currencies in future.
- Can integrate mobile camera or real-time detection features.
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