This repository contains machine learning models and data for material science research. The repository is organized into five directories:
- Code: Contains Python scripts and Jupyter notebooks for training and evaluating machine learning models.
- Data: Stores datasets and data preprocessing scripts.
- Images: Includes images, plots, and visualizations generated during analysis.
- License: Provides licensing information for code and data usage.
- pycache: Stores cached Python files generated during code execution (can be safely ignored).
The Code directory contains Python scripts and Jupyter notebooks for training and evaluating machine learning models related to material science. You can explore and run these scripts to understand how the models were developed and to apply them to your own data.
The Data directory stores datasets used in the machine learning models and any data preprocessing scripts. You may find datasets in various formats (e.g., CSV, Excel) along with scripts to clean, transform, or preprocess the data.
The Images directory includes images, plots, and visualizations generated during the analysis and model evaluation process. These images provide insights into the performance of the models and the characteristics of the data.
The License directory provides licensing information for the code and data used in this repository. Please review the licensing terms before using any code or data from this repository.
The pycache directory stores cached Python files generated during the execution of code scripts. These files are automatically generated and can be safely ignored.
This repository is not open-source. You are not free to use, modify, and distribute the code and data within the terms of the license. Attribution is required when using the code or data (refer to the License directory for details).
If you have any questions, suggestions, or contributions, please feel free to reach out or create a pull request.
Laxman Chaudhary [email protected]/[email protected]