This repository is designed for beginners to intermediate level ML/Bioinformatics engineers, students, and enthusiasts. It focuses on creating a simple ensemble classification model to predict if someone has a kidney stone using the dataset provided in the repository.
In this tutorial, we use an ensemble classification model, which combines multiple machine learning models to improve prediction accuracy. Ensemble methods leverage the strengths of different models to produce a more robust and reliable prediction. Specifically, this tutorial guides you through building a model to classify whether an individual has a kidney stone based on various features in the dataset.
To get started, clone the repository and run the tutorial notebook to see the steps and code.
- Required Python libraries (listed in requirements.txt)
You can install them using the following command:
pip install -r requirements.txt
This project is licensed under the MIT License - see the LICENSE file for details.
Contributions are welcome! Please feel free to submit a pull request.