This project focuses on using TensorFlow Decision Forest, a machine learning framework, to predict student performance during game-based learning. The dataset used for training the model is one of the largest open datasets of game logs, providing a wealth of information to train and test the model's performance.
To get started with this project, follow the steps below:
- Clone the repository to your local machine using the following command:
git clone https://github.com/yourusername/predict-student-performance.git- Install the necessary dependencies. This project requires TensorFlow Decision Forest, which can be installed using the following command:
pip install tensorflow_decision_forests- Download the dataset from the provided source and place it in the appropriate directory in the project repository.
To use the model for predicting student performance, follow these steps:
- Load the dataset into the model using the provided data preprocessing functions.
- Train the model using the training data.
- Evaluate the model's performance on the test data using appropriate metrics.
- Use the trained model to predict student performance in real-time during game-based learning.
We welcome contributions from the community to help improve this project. If you would like to contribute, please follow these guidelines:
- Fork the repository and create a new branch for your contribution.
- Make your changes and submit a pull request, explaining the purpose and details of your contribution.
- Ensure that your code follows the project's coding standards and conventions.
- Provide appropriate documentation for your contribution, including any changes to the README file.
This project is licensed under the MIT License, which allows for free use, modification, and distribution, subject to the terms and conditions specified in the license file.
For any questions or concerns, please contact the project maintainers. Thank you for your contribution!