Analysis to gain votes for a kernel on Kaggle using Meta Kaggle dataset. The analysis is also available as Kaggle kernel and Medium article.
I have recently joined Kaggle and started to create public kernels. My kernels have a lot of views, but no upvotes. So I decided to analyze Meta Kaggle dataset to find out:
- Statistics for kernels, which have votes.
- How different factors affect the number of votes (for example, characteristics of the author, source dataset etc.)?
- And finally, make the recommendations on how to make the kernel useful, so other kagglers would cast upvotes.
Findings and recommendations from this analysis:
- It is hard to create a really helpful kernel, which will be appreciated and upvoted by Kagglers: only 20% of kernels have upvotes and only 4% of kernels have awards (have more than 5 upvotes).
- Views and comments bring upvotes: consider adding a captivating title to the kernel and sharing the link to the kernel with others, the more people will view the kernel - the more people will find it useful.
- Active authors have more votes: try to be an active author and gain visibility, experience in writing kernels and feedback from the others will eventually help to get votes.
- It doesn't really matter what topic the kernel is related to, but it matters how the kernel material is presented: notebooks tend to be more appreciated by Kagglers.
- How to get upvotes for a kernel on Kaggle.ipynb - Jupyter Notebook (Python 3) containing step-by-step analysis.