Kaggle kernel votes analysis

Analysis to gain votes for a kernel on Kaggle using Meta Kaggle dataset. The analysis is also available as Kaggle kernel and Medium article.

Introduction

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.

Key Findings

Findings and recommendations from this analysis:

1. 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).
2. 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.
3. 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.
4. 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.

Repository Contents

1. How to get upvotes for a kernel on Kaggle.ipynb - Jupyter Notebook (Python 3) containing step-by-step analysis.

External Libraries

1. XGBoost for machine learning
2. Plotly for visualization