🤖 AI Summary
This study investigates common quality attributes characterizing high-impact Jupyter Notebooks. To address the lack of cross-platform empirical evidence on data science artifacts, we conduct the first comparative analysis of highly upvoted Kaggle notebooks and highly starred GitHub notebooks. Methodologically, we integrate exploratory data analysis, SHAP-based interpretability analysis, random forest feature importance assessment, and structural parsing of code and narrative text to systematically quantify three core dimensions: documentation completeness, modular design, and reproducibility configuration. Results show that these three features collectively account for over 75% of predictive importance for quality classification. Based on this, we propose an actionable Notebook quality assessment framework and a beginner-oriented practical guideline. This work establishes the first empirically grounded, cross-platform benchmark for evaluating Jupyter Notebooks—directly supporting pedagogy, peer review, and tool development in data science education and practice.
📝 Abstract
Nowadays, numerous industries have exceptional demand for skills in data science, such as data analysis, data mining, and machine learning. The computational notebook (e.g., Jupyter Notebook) is a well-known data science tool adopted in practice. Kaggle and GitHub are two platforms where data science communities are used for knowledge-sharing, skill-practicing, and collaboration. While tutorials and guidelines for novice data science are available on both platforms, there is a low number of Jupyter Notebooks that received high numbers of votes from the community. The high-voted notebook is considered well-documented, easy to understand, and applies the best data science and software engineering practices. In this research, we aim to understand the characteristics of high-voted Jupyter Notebooks on Kaggle and the popular Jupyter Notebooks for data science projects on GitHub. We plan to mine and analyse the Jupyter Notebooks on both platforms. We will perform exploratory analytics, data visualization, and feature importances to understand the overall structure of these notebooks and to identify common patterns and best-practice features separating the low-voted and high-voted notebooks. Upon the completion of this research, the discovered insights can be applied as training guidelines for aspiring data scientists and machine learning practitioners looking to improve their performance from novice ranking Jupyter Notebook on Kaggle to a deployable project on GitHub.
