Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/alphacrypto246/student-learning-style-prediction
An interactive web application built with Streamlit that predicts a student's preferred learning style (visual, auditory, or kinesthetic) using machine learning, aiding educators in personalizing teaching strategies.
https://github.com/alphacrypto246/student-learning-style-prediction
machine-learning scikit-learn scikitlearn-machine-learning streamlit
Last synced: about 18 hours ago
JSON representation
An interactive web application built with Streamlit that predicts a student's preferred learning style (visual, auditory, or kinesthetic) using machine learning, aiding educators in personalizing teaching strategies.
- Host: GitHub
- URL: https://github.com/alphacrypto246/student-learning-style-prediction
- Owner: alphacrypto246
- Created: 2024-11-29T09:36:49.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-12-06T09:14:16.000Z (2 months ago)
- Last Synced: 2024-12-18T09:19:18.070Z (about 2 months ago)
- Topics: machine-learning, scikit-learn, scikitlearn-machine-learning, streamlit
- Language: Python
- Homepage:
- Size: 12.7 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Student Learning Style Prediction
This project is designed to predict a student's preferred learning style based on their responses to a set of questions. The application utilizes machine learning techniques to analyze input data and classify students into categories such as visual, auditory, or kinesthetic learners.
## Features
- **User Input**: Collects responses from users via a web interface.
- **Machine Learning Model**: Predicts learning styles based on input data.
- **Interactive Application**: Built using a lightweight framework for ease of use.
- **Customizable**: Easily adaptable to include additional learning styles or parameters.
## Libraries Used
The following libraries are used in this project:
- **Streamlit**: For creating the interactive web application.
- **scikit-learn**: For building and deploying the machine learning model.
- **pandas**: For handling input and structured data.
- **numpy**: For numerical computations.
## Requirements
To run the project, you need the following dependencies:
```bash
flask
scikit-learn
pandas
numpy
```
## Installation
1. Clone the repository:
```bash
git clone https://github.com/alphacrypto246/Student-Learning-Style-Prediction.git
```
2. Navigate to the project directory:
```bash
cd Student-Learning-Style-Prediction
```
3. Run the application:
```bash
streamlit run app.py
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## Usage
1. Run the application:
```bash
python app.py
```
2. Open your web browser and navigate to:
```
http://localhost:5000
```
3. Input the required data and receive the predicted learning style.
## File Structure
- `app.py`: The main script for running the Flask application.
- `model.pkl`: Pre-trained machine learning model for predictions.
- `templates/`: Directory containing HTML templates for the web interface.
- `static/`: Directory for static files like CSS and JavaScript.
- `README.md`: Project documentation.
- `requirements.txt`: List of Python dependencies.
## Dataset
The application requires a dataset of student responses and their corresponding learning styles for training and evaluation. The dataset should include:
- Input features (e.g., questionnaire responses).
- Target labels (e.g., visual, auditory, kinesthetic).
## Results
The application provides accurate predictions of learning styles based on user input. It can help educators tailor their teaching strategies to suit individual student needs.
## Contributing
Contributions are welcome! To contribute:
1. Fork the repository.
2. Create a new branch:
```bash
git checkout -b feature-branch-name
```
3. Make your changes and commit them:
```bash
git commit -m "Description of changes"
```
4. Push to your branch:
```bash
git push origin feature-branch-name
```
5. Open a pull request.
## License
This project is licensed under the MIT License. See the `LICENSE` file for details.
## Acknowledgments
Special thanks to the developers of the libraries used in this project and the open-source community for providing resources to build machine learning applications.
---
Feel free to raise issues or feature requests via the [Issues](https://github.com/alphacrypto246/Student-Learning-Style-Prediction/issues) tab.