Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/ofir-frd/predict-minerals-structure-from-materials-characteristics
Predict materials structures from a list of of 3112 minerals, their chemical composition and properties
https://github.com/ofir-frd/predict-minerals-structure-from-materials-characteristics
data-science decision-tree knn-classifier machine-learning materials-science
Last synced: 4 days ago
JSON representation
Predict materials structures from a list of of 3112 minerals, their chemical composition and properties
- Host: GitHub
- URL: https://github.com/ofir-frd/predict-minerals-structure-from-materials-characteristics
- Owner: ofir-frd
- License: apache-2.0
- Created: 2022-01-05T12:46:47.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2022-02-08T09:28:36.000Z (almost 3 years ago)
- Last Synced: 2023-10-20T06:47:48.035Z (about 1 year ago)
- Topics: data-science, decision-tree, knn-classifier, machine-learning, materials-science
- Language: Jupyter Notebook
- Homepage:
- Size: 884 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Predict Minerals Structure From Materials Characteristics
A Kaggle dataset.
Predict materials structures from a list of 3112 minerals, their chemical composition, and properties.
## About Data Collection Methodology
Data was collected from Wikipedia (https://en.wikipedia.org/wiki/List_of_minerals), The American Mineralogist Crystal Structure Database (http://rruff.geo.arizona.edu/AMS/amcsd.php), and the gemology project website (http://gemologyproject.com/wiki).
### Description of The Data
Directory description:
```
Root Dir/
- minerals-structure-from-materials-characteristics.ipynb
- Minerals_Database.csv
- README.md
- LICENSE.md
- .gitignore
```
Features review:
```
Minerals_Database.csv/
- Labels
- Crystal Structure
- Numerical
- Mohs Hardness
- Diaphaneity
- Specific Gravity
- Optical
- Refractive Index
- count
- Molar Mass
- Molar Volume
- Calculated Density
- 118 features: sum of atoms per molucule in the periodic table
```
### File Formats
Code is written in Python on Jupyter '.ipynb'.
The data provided in this project is a '.csv' file with no separation between train and test datasets.
```
-50,000 3112 minerals, their chemical composition, and properties, format csv.
```
## Online Repository Link
* [Data Repository](https://www.kaggle.com/vinven7/comprehensive-database-of-minerals)
## Author
* [ofir-frd](https://github.com/ofir-frd)
## License
This project is licensed under the Apache License 2.0 - see the [LICENSE.md](https://github.com/ofir-frd/Comprehensive_database_of_Minerals/blob/main/LICENSE) file for details
## Acknowledgments
* [Vineeth Venugopal](https://www.linkedin.com/in/vineeth-venugopal-959781108/), who collected and organized the data, and create the dataset in Kaggle