Notebooks of the Week - Hidden Gems

### Context

On Kaggle, many great Notebooks (aka "Kernels") can be found that have a large number of well-deserved upvotes. I'm publishing a weekly series of "Hidden Gems" in which I aspire to find and showcase underrated Notebooks on Kaggle in an effort to illuminate the diversity and creativity of our community.

This series looks beyond those well-recognised works to find Notebooks which I personally like and which, somehow, didn't receive the attention that I think they deserve. My selections are always highly subjective; reflecting my own views and preferences. I have managed to publish an episode of "Hidden Gems" every week since [the first post](https://www.kaggle.com/general/150603) back in May 2020 and I hope to be able to keep this weekly schedule for the foreseeable future.

Each episode presents 3 Notebooks, which can be recent or older works. These episodes are first posted to the Kaggle Forum (later on Twitter/LinkedIn), and I always encourage readers to contribute any other underestimated Notebooks in the comments of the forum posts. The title and link to the Notebook are accompanied by the author's user name and a brief description on what I like about this work.

This dataset contains all published episodes, with the goal to make the archive of "Hidden Gems" better accessible and to enable meta analysis.

### Content

The file of interest is `kaggle_hidden_gems.csv`, containing the following columns:

- `vol` and `date`: The consecutive number of the Hidden Gems episode and when it was first published.

- `link_forum` and `link_twitter`: The hyperlinks to the Kaggle Forum post and Twitter post for the episode.

- `notebook` and `author`: The hyperlinks to the Notebook itself, as well as to the Kaggle profile of the author.

- `title`: The Notebook title as a string.

- `review`: My brief review of the Notebook.

- `author_name`: The name of the Notebook author as listed on their Kaggle profile at the time the episode was published.

- `author_twitter` and `author_linkedin`: The social media links of the author, if listed on their Kaggle profile.

- `notes`: Notes about special episodes.

### Acknowledgements

I would like to thank all current and future Notebook authors for their inspiring contributions. Your hard work and brilliance is what makes this community so amazing.

Cover image by [Shachar Harshuv](https://i.imgur.com/4VOS6iz.png).

### Inspiration

My starter Notebook contains a number of suggestions on what to explore. Linking this dataset to [Meta Kaggle](https://www.kaggle.com/kaggle/meta-kaggle) will provide even more opportunities for extracting insights. Have fun!

在Kaggle平台上，众多杰出的Notebooks（亦称‘内核’）获得了大量应得的点赞。我正在推出一系列‘隐藏的宝石’，旨在发掘并展示Kaggle平台上那些未被充分重视的Notebooks，以此彰显我们社区在多样性和创造力方面的丰富性。本系列作品超越了那些广为人知的杰作，寻找那些我个人钟爱且某种程度未获得应得关注的内核。我的选择始终极具主观性，反映了我的个人观点和偏好。自2020年5月首次发布于[该帖子](https://www.kaggle.com/general/150603)以来，我已经成功每周发布一集‘隐藏的宝石’，并希望在未来能够保持这一周更的发布节奏。每一集展示3个内核，这些内核可以是近期作品，也可以是较旧的作品。这些剧集首先发布在Kaggle论坛（后来在Twitter/LinkedIn上），我总是鼓励读者在论坛帖子下贡献其他未被充分重视的内核。每个内核的标题和链接都附有作者的昵称以及我对该作品简要的喜爱之评。本数据集包含了所有已发布的剧集，旨在使‘隐藏的宝石’的存档更加易于访问，并便于进行元分析。

### 内容

关注的文件是`kaggle_hidden_gems.csv`，包含以下列：

- `vol`和`date`：隐藏的宝石剧集的连续编号及其首次发布日期。

- `link_forum`和`link_twitter`：剧集在Kaggle论坛和Twitter上的超链接。

- `notebook`和`author`：内核本身的超链接以及作者在Kaggle上的个人资料链接。

- `title`：内核标题作为字符串。

- `review`：我对内核的简要评论。

- `author_name`：在剧集发布时作者在Kaggle个人资料中列出的姓名。

- `author_twitter`和`author_linkedin`：如果作者在Kaggle个人资料中列出，则为作者的社会媒体链接。

- `notes`：关于特殊剧集的备注。

### 致谢

我要感谢所有当前和未来的内核作者们，你们富有启发的贡献使得这个社区如此出色。

封面图片由[Shachar Harshuv](https://i.imgur.com/4VOS6iz.png)提供。

### 灵感

我的起点内核包含了一系列探索建议。将此数据集与[Meta Kaggle](https://www.kaggle.com/kaggle/meta-kaggle)链接将提供更多提取见解的机会。祝您玩得愉快！