"Data_Science_Projects_Jan-Feb_2023" is a repository created for the members of the Sigma Club at NIT-T to contribute their data analytics projects. This repository provides a platform for members to showcase their skills and collaborate on projects related to data analysis, visualization, machine learning, and other data-driven fields. The projects submitted by members are reviewed by heads of the analytics domain of club and merged into the repository, making it a curated collection of high-quality data analytics projects.
Each repository created every two months includes a new set of projects contributed by members of the Sigma Club. The repository is named with the month and year in which it was created to keep track of when it was initiated. Members are encouraged to submit their projects as pull requests and participate in code reviews to ensure the quality and consistency of the projects. With each new repository, the Sigma Club continues to foster a collaborative environment where members can learn from each other and improve their skills in data analytics.
- Fork the repository and create a new branch.
- Add your data analytics project(s) to the appropriate folder(s).
- Create a pull request with a descriptive title and description of your changes.
- Your pull request will be reviewed by other members of the club before being merged into the repository.
The repository is organized into folders that represent different data analytics topics. Each folder contains projects related to that topic. The current folder structure is:
- Data-Analysis/: projects related to data cleaning, exploration, and analysis.
- Data-Visualization/: projects related to visualizing data using various tools and libraries.
(No Coding) - Machine-Learning/: projects related to building and evaluating machine learning models.
- Deep-Learning/: projects related to building and evaluating deep learning models.
- Natural-Language-Processing/: projects related to natural language processing and text analytics.
- Other-Topics/: projects related to data analytics that do not fit into the above categories.
As members of the Sigma Club at NIT-T, we are committed to creating a welcoming and inclusive community for everyone. We value diversity and inclusivity, and we believe that everyone should feel safe and respected in our community.
This repository is licensed under the MIT License. By contributing to this repository, you agree to license your contributions under the same license.