Enhancing function docstrings and creating example notebooks

[aryans221503]

The DsKit project aims to simplify the data science and machine learning workflow by providing a unified, production-ready toolkit that wraps commonly used operations—such as data cleaning, exploratory data analysis, feature engineering, and modeling—into simple, easy-to-use functions. This approach helps data scientists and ML engineers reduce the significant amount of time spent on repetitive boilerplate tasks and focus more on problem-solving and experimentation.

My proposed contribution focuses on strengthening the foundation of DSKIT by improving the quality, reliability, and usability of its existing utilities. This includes enhancing function docstrings, adding meaningful unit tests, creating example notebooks, and introducing small but impactful data cleaning utilities. These improvements are important because they directly affect how easily users can understand, trust, and adopt the library in real-world projects.

[Rajnandini15122006]

Hello! I am currently investigating Ak-dskit because it forms part of my Winter of Code project proposal.

The issue I am working on directly supports my main task which involves enhancing function docstrings and developing example notebooks that will help new users understand the library.

Please inform me about your preferred docstring styles and which specific modules you want contributors to start working on. I am eager to start my contributions to the project. Thank you!