https://github.com/alihassanml/human-emotion-recognition-app
Human-Emotion-Recognition-App-Machine-Learning-Project
https://github.com/alihassanml/human-emotion-recognition-app
Last synced: 8 months ago
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Human-Emotion-Recognition-App-Machine-Learning-Project
- Host: GitHub
- URL: https://github.com/alihassanml/human-emotion-recognition-app
- Owner: alihassanml
- Created: 2024-04-07T19:11:05.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-09T17:41:05.000Z (over 1 year ago)
- Last Synced: 2025-01-01T15:11:50.210Z (9 months ago)
- Language: Jupyter Notebook
- Size: 2.99 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Human Motion Detection App using Machine Learning
This project aims to develop a human motion detection application using machine learning techniques. The application will be able to detect and classify human motions based on input data, such as sensor readings or video feeds.
## Overview
Human motion detection is an essential task in various fields, including security, healthcare, and sports analytics. Traditional methods for motion detection often rely on complex algorithms and heuristics. In contrast, this project utilizes machine learning algorithms to automate the process.
## Libraries Used
- Pandas: For data manipulation and analysis.
- NumPy: For numerical computing.
- Matplotlib and Seaborn: For data visualization.
- NLTK: For natural language processing tasks (if needed).
- WordCloud: For generating word clouds (if needed).
- Scikit-learn: For machine learning algorithms implementation, including:
- Train-test split.
- Feature extraction using TF-IDF vectorization.
- Evaluation metrics such as accuracy score and classification report.
- Algorithms like Random Forest Classifier, Naive Bayes, and Support Vector Machine (SVM).
## Workflow
1. **Data Collection**: Gather data relevant to human motion. This could be sensor data from accelerometers, gyroscopes, or video data.
2. **Data Preprocessing**: Clean the data, handle missing values, and convert it into a format suitable for machine learning models.
3. **Feature Extraction**: Extract relevant features from the data. This could involve techniques such as TF-IDF vectorization for text data or feature engineering for sensor data.
4. **Model Building**: Train machine learning models using the preprocessed data. Experiment with various algorithms like Random Forest, Naive Bayes, and SVM to find the best-performing model.
5. **Model Evaluation**: Evaluate the models using appropriate evaluation metrics such as accuracy, precision, recall, and F1-score.
6. **Deployment**: Deploy the trained model into the application, allowing real-time or batch processing of input data to detect human motions.
## Description
This GitHub repository contains code for a human motion detection application using machine learning. The project demonstrates the end-to-end process of building, training, and deploying machine learning models for motion detection tasks. It includes Jupyter notebooks, Python scripts, and documentation to guide users through the project.
## Getting Started
To get started with the project, follow these steps:
1. Clone the repository to your local machine.
2. Install the required dependencies listed in the `requirements.txt` file.
3. Follow the instructions provided in the documentation to preprocess data, train models, and deploy the application.
4. Experiment with different algorithms and parameters to improve model performance or adapt the application to your specific use case.
## Contributions
Contributions to the project are welcome. Feel free to open issues for bug fixes, feature requests, or submit pull requests with improvements.
## License
This project is licensed under the [MIT License](LICENSE). Feel free to use and modify the code for your own purposes.
## Acknowledgments
- This project was inspired by the need for efficient human motion detection solutions.
- Thanks to the contributors of open-source libraries used in this project.
- Special thanks to the developer community for their support and feedback.
## Contact
For any inquiries or support, please contact [author's email].
**Note**: Replace `[author's email]` with your contact information.
This README provides an overview of the project, its components, and instructions for getting started. Ensure to keep it updated with any changes or additions to the project.