https://github.com/amanovishnu/anamoly-detection-using-decision-classifier
the kdd 99 anomaly detection application is a flask web app that predicts anomalies in the kdd 99 dataset using a decision tree classifier. it allows users to input features for prediction and offers a user-friendly interface with real-time predictions and low latency.
https://github.com/amanovishnu/anamoly-detection-using-decision-classifier
anamoly-detection data-preprocessing-and-cleaning decision-tree-classifier flask-application kdd-99-dataset machine-learning machine-learning-algorithms
Last synced: 5 months ago
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the kdd 99 anomaly detection application is a flask web app that predicts anomalies in the kdd 99 dataset using a decision tree classifier. it allows users to input features for prediction and offers a user-friendly interface with real-time predictions and low latency.
- Host: GitHub
- URL: https://github.com/amanovishnu/anamoly-detection-using-decision-classifier
- Owner: amanovishnu
- License: mit
- Archived: true
- Created: 2024-03-23T13:53:45.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-24T12:40:48.000Z (about 1 year ago)
- Last Synced: 2025-01-03T02:57:47.246Z (5 months ago)
- Topics: anamoly-detection, data-preprocessing-and-cleaning, decision-tree-classifier, flask-application, kdd-99-dataset, machine-learning, machine-learning-algorithms
- Language: HTML
- Homepage:
- Size: 7.74 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# KDD 99 Anomaly Detection Application
This is a Flask web application for predicting anomalies in the KDD 99 dataset using Decision Tree Classifier Model.
## Features
- Allows users to input features from the KDD 99 dataset and predict whether an instance is an anomaly or not.
- Provides a user-friendly interface for interacting with the prediction model.
- Utilizes a machine learning model trained on the KDD 99 dataset to make predictions.
- Supports real-time prediction with low latency.
## Installation
1. Clone the repository:
https://github.com/amanovishnu/anamoly_detection.git
2. Install the required dependencies:
pip install -r requirements.txt
## Usage
1. Navigate to the project directory:
cd anamoly_detection
2. Run the Flask application:
python app.py
3. Access the application in your web browser at `http://localhost:5000`.
4. Input the required features from the KDD 99 dataset and submit the form to make predictions.
## Deployment
The application can be deployed using various techniques such as traditional web hosting, containerization (Docker), serverless computing, Platform as a Service (PaaS), or Continuous Integration/Continuous Deployment (CI/CD) pipelines.
## Contributing
Contributions are welcome! If you'd like to contribute to this project, please follow these steps:
1. Fork the repository.
2. Create a new branch (`git checkout -b feature/new-feature`).
3. Make your changes and commit them (`git commit -am 'Add new feature'`).
4. Push to the branch (`git push origin feature/new-feature`).
5. Create a new pull request.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgements
- This project utilizes the KDD 99 dataset for anomaly detection.
- Special thanks to contributors and open-source projects used in this application.