https://github.com/alihassanml/flaggers-api
https://github.com/alihassanml/flaggers-api
Last synced: 2 months ago
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- Host: GitHub
- URL: https://github.com/alihassanml/flaggers-api
- Owner: alihassanml
- Created: 2024-05-12T08:10:32.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-12T08:18:10.000Z (over 1 year ago)
- Last Synced: 2025-02-21T11:26:47.334Z (8 months ago)
- Language: Jupyter Notebook
- Size: 124 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Banknote Authentication using Machine Learning
This project demonstrates how to authenticate banknotes using machine learning techniques. It provides a Flask-based web application where users can input features related to banknotes, such as variance, skewness, curtosis, and entropy, and get predictions on whether the banknote is authentic or not.
## Usage
To use this application, follow these steps:
1. Clone the repository to your local machine:
```
git clone https://github.com/your-username/banknote-authentication.git
```
2. Install the required dependencies:
```
pip install -r requirements.txt
```
3. Run the Flask application:
```
python app.py
```
4. Open your web browser and navigate to [http://localhost:5000](http://localhost:5000).
5. Input the values for variance, skewness, curtosis, and entropy in the provided fields.
6. Click on the "Authenticate" button to get the prediction.
## Dependencies
- Flask
- scikit-learn
## How it works
The application uses a RandomForestClassifier from the scikit-learn library to make predictions. The classifier is trained on a dataset containing features extracted from banknotes. When a user inputs values for variance, skewness, curtosis, and entropy, the application passes these values to the classifier and returns the prediction as to whether the banknote is authentic or not.
## License
This project is licensed under the MIT License - see the (alihassanml) file for details.
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