Welcome to Data-Science-Snippets! 🎉 This repository is a collection of handy code snippets and mini-scripts designed to help data scientists, researchers, and enthusiasts with various data science tasks. Whether you're manipulating data, building visualizations, or exploring machine learning algorithms, this repository has you covered!
- Data Manipulation: Pandas, Numpy operations, and data cleaning tips.
- Visualization: Matplotlib, Seaborn, and Plotly snippets for insightful charts and plots.
- Machine Learning: Model building, evaluation, and tuning with Scikit-Learn and more.
- Data Preprocessing: Handling missing values, feature engineering, and scaling.
- Deep Learning: Neural network architectures and training tips using TensorFlow/Keras.
- Miscellaneous: Utility functions and scripts for file handling, logging, and automation.
We are thrilled to announce that Data-Science-Snippets is part of Hacktoberfest 2024! Hacktoberfest is a month-long celebration of open-source software, and we welcome all contributors, from beginners to seasoned developers, to join us and help make this repository even better.
We are actively looking for contributions, be it bug fixes, enhancements, or new snippets! Follow the steps below to get started:
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Fork the Repository
Click the "Fork" button at the top right of this page to create your own copy of the repository. -
Clone the Repository
Clone the forked repository to your local machine:git clone https://github.com/your-username/Data-Science-Snippets.git
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Create a New Branch Create a new branch for your feature or bug fix:
git checkout -b feature/your-feature-name
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Make Your Changes
Add your code snippets, scripts, or modifications to the relevant folder. Please follow the Guidelines for naming conventions and structure.
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Commit and Push Commit your changes with a descriptive message:
git commit -m "Added new snippet for data preprocessing using pandas"Push the changes to your fork:
git push origin feature/your-feature-name -
Submit a Pull Request
Go to the original repository and click on the "New Pull Request" button. Provide a brief description of your changes and submit your pull request!
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Wait for Review
One of the maintainers will review your pull request and provide feedback or merge it into the main branch.
To maintain the quality and consistency of this repository, please follow these guidelines when contributing:
- Use descriptive names for your scripts, e.g.,
data_cleaning_with_pandas.pyorlinear_regression_sklearn.py. - Follow the
snake_casenaming convention for files and folders.
- Include comments to describe the functionality of your code.
- For complex functions or classes, provide a docstring explaining the parameters and return values.
Place your snippets in the appropriate folder, or create a new one if necessary. The folder structure is as follows:
├── data_manipulation/
├── data_visualization/
├── machine_learning/
├── deep_learning/
└── miscellaneous/
Follow the PEP 8 style guide for Python code. This includes using meaningful variable names, adhering to indentation standards, and maintaining readability.
Test your snippets with sample data to ensure they work as expected. Include sample data or instructions on how to generate it if necessary.
If you add a new category or a significant snippet, update the main README.md with a brief description and relevant usage examples.
All contributions must abide by the Hacktoberfest Rules to be considered valid. This includes submitting meaningful pull requests that add value to the project.
If you're new to open-source or want to learn more about contributing, check out these resources:
Join our Discord community to discuss ideas, ask questions, or get help with your contributions. You can also connect with us on Twitter or LinkedIn.
Let's build something amazing together! Happy coding! 💻✨