From where can I get the python based Machine learning codes for material science? As i am not an expert in python programming and can't write the whole code on my own. I need some framework that i can modify for my ML problems. What can be the best source/place to look for that. Kindly suggest.
I can suggest to look on the tasks and solutions from https://www.kaggle.com/.
There are many different solution for different ML tasks.
Dear Ankur Taya
Some the most famous websites for getting codes and scripts for Pythone are as follows:
1 - GitHub
2 - Stack Overflow
3 - Kaggle
You can search these names in google and find these useful websites and download prepared codes by searching in these platforms .
Few resources:
1. Materials Project: The Materials Project is a platform for materials science research that provides data and tools to help researchers discover and design new materials. They have a Python API that allows users to access their database and perform machine learning tasks on materials data. You can find their API documentation and examples on their website.
2. PyMKS: PyMKS is a Python package for generating microstructure datasets and applying machine learning algorithms to them. It provides tools for generating synthetic microstructures and extracting features from them, as well as several pre-trained machine learning models for classification and regression tasks. You can find their documentation and examples on their GitHub page.
3. Matminer: Matminer is a Python package for data mining and analysis of materials data. It provides tools for extracting features from materials data, as well as several pre-trained machine learning models for classification, regression, and clustering tasks. You can find their documentation and examples on their GitHub page.
4. Materials Informatics: The Materials Informatics community on GitHub provides a collection of Python-based machine learning codes and tools for materials science research. You can find their repository and examples on their GitHub page.
5. Scikit-learn: Scikit-learn is a popular Python package for machine learning that provides tools for classification, regression, clustering, and other tasks. While not specifically designed for materials science, it can be used for analyzing materials data and performing machine learning tasks. You can find their documentation and examples on their website.
Good luck
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As requested, some recommendations:
1. Python: Python is a versatile programming language that is widely used for data analysis and processing, as well as for building web applications and scripts. It has a large and active user community, which means there are many libraries and tools available for various tasks. Python is a good choice for urban planners who need to work with large datasets, perform spatial analysis, or build data-driven applications.
2. R: R is a programming language and software environment for statistical computing and graphics. It is particularly well-suited for data analysis, visualization, and machine learning tasks. R has a large and active community of users and developers, and many packages are available for spatial analysis and visualization.
3. SQL: SQL (Structured Query Language) is a standard language for managing and manipulating data stored in relational databases. It is commonly used by urban planners for managing large datasets and performing queries to extract specific information.
4. GIS software: Geographic Information System (GIS) software is specialized software designed for working with spatial data, such as maps, satellite imagery, and other geospatial data. Popular GIS software includes ArcGIS, QGIS, and Google Earth. These tools can be used in combination with programming languages like Python and R to perform spatial analysis and visualization.
Good luck as urban planner
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