Mohs Hardness Ensemble Prediction

A collaborative project utilizing ensemble models for predicting Mohs hardness. 🚀💎

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Table of Contents

• Description
• Models
• Installation
• Usage
• Contributing
• License

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Description

This project focuses on predicting Mohs hardness using a combination of various machine learning models. The ensemble includes Neural Networks, LGBM, CAT, and XGB, all working together through a Voting Mechanism. The goal is to create a robust and accurate prediction system for Mohs hardness.

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Models

• Neural Networks
• LightGBM (LGBM)
• CatBoost (CAT)
• XGBoost (XGB)

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Installation

# Clone the repository
git clone https://github.com/ThecoderPinar/mohs-hardness-ensemble-prediction.git

# Navigate to the project directory
cd mohs-hardness-ensemble-prediction

# Install dependencies
pip install -r requirements.txt

# Usage
To run the prediction models, follow these steps:

- Open the Jupyter Notebook or Python script.
- Run the cells or execute the script.
- Input the relevant features for prediction.
- Obtain the predicted Mohs hardness.

# Contributing
- Fork the project (https://github.com/ThecoderPinar/mohs-hardness-ensemble-prediction/fork)
- Create your feature branch (git checkout -b feature/AmazingFeature)
- Commit your changes (git commit -am 'Add some AmazingFeature')
- Push to the branch (git push origin feature/AmazingFeature)
Open a pull request

# License
Distributed under the MIT License. See LICENSE for more information.

# Contact
Pinar Topuz - piinartp@gmail.com

Project Link: https://github.com/ThecoderPinar/mohs-hardness-ensemble-prediction