The rapid increase in counterfeit currency circulation poses a significant challenge to financial security and economic stability. Traditional methods of detecting fake currency often rely on manual inspection, which can be time-consuming, inaccurate, and dependent on human expertise. To address this issue, this project presents a Fake Currency Detection System that utilizes Machine Learning techniques integrated with a Django-based web application to automate the process of currency verification.
The system preprocesses uploaded currency images by converting them to grayscale, resizing them to a fixed dimension, and transforming them into numerical feature vectors. A Random Forest Classifier is trained on a dataset containing genuine and fake currency note images to classify new inputs with high efficiency. The model predicts whether a note is Genuine or Fake and provides a corresponding confidence score.
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nbsp;Tech Stack Used
Frontend / Web Interface:
Django (Python Web Framework) – Used to create the web interface for user input, displaying predictions, and managing data
HTML5, CSS3, JavaScript – For rendering and styling web pages
Bootstrap (optional) – For responsive UI components
Django Templates – For dynamic web page rendering
nbsp;Machine Learning / Backend Logic:
scikit-learn – Machine Learning library used to implement algorithms like Logistic Regression, Decision Tree, Random Forest, KNN
NumPy→ For numerical operations and matrix manipulation
Pandas → For handling and preprocessing datasets
joblib → To save and load the trained machine learning model
nbsp;Database:
SQLite – Lightweight relational database used to store user data and predictions
Django ORM (Object Relational Mapper) – Handles interaction between Django models and the SQLite database
nbsp;Tools amp; Environment:
Python 3.x – Core programming language used
PyCharm – IDE for development
Virtualenv / pip – For managing dependencies
nbsp;Key Features
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.
PHP Gurukul
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Website : https://phpgurukul.com
