![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F19099%2Fd0c8154dc7a5eead4ee2f713301d4102%2F2022-11-09%20%2022.58.16.png?generation=1668024043376755&alt=media\")
Hi fellow Kagglers,
\nI have created the dataset and the example kernel to make some analytics on the kernels, which were created for this competition.
\nIn the kernel you can see several interesting points like:
\nTo make it more interesting to check here are some results.
\nHere we can congrats @ambrosm, @fabiencrom and @xiafire for the 1st, 2nd and 3rd place respectively:
\n
The full kernels graph for this competition looks like (you can see it in bigger size if you click on it in kernel with the right mouse button and open it in another tab):
\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F19099%2F9111782ac729e5802f720c8ade2f14ad%2F2022-11-09%20%2022.37.33.png?generation=1668024116134791&alt=media\")
The closer look for the part of this graph:
\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F19099%2Fe2717cb9ffe6a3a0732378f03da4c2fb%2F2022-11-09%20%2022.30.32.png?generation=1668024209916178&alt=media\")
I have used the kernel IDs in the graph to make it more readable so to find your kernel ID you can check it in the dataset by your name or kernel title.
\nIf we use this graph and calculate the cumulative number of upvotes using the forks, we can receive the table like:
\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F19099%2Fe7fe3a0a73020db7f34f72d6500c9ff3%2F2022-11-09%20%2023.06.01.png?generation=1668024398783560&alt=media\")
The current winner is @fabiencrom with his MSCI Multiome Quickstart w/ Sparse Matrices kernel. @swimmy is on the 2nd place and the 3rd is @alexandervc.
\nLooking forward for your comments, suggestions and insights!
\nAlex
", "messages": [ { "id": 2023529, "postDate": "2022-11-09T20:10:00.090Z", "content": "Hi fellow Kagglers,
\nI have created the dataset and the example kernel to make some analytics on the kernels, which were created for this competition.
\nIn the kernel you can see several interesting points like:
\nTo make it more interesting to check here are some results.
\nHere we can congrats @ambrosm, @fabiencrom and @xiafire for the 1st, 2nd and 3rd place respectively:
\n
The full kernels graph for this competition looks like (you can see it in bigger size if you click on it in kernel with the right mouse button and open it in another tab):
\n
The closer look for the part of this graph:
\n
I have used the kernel IDs in the graph to make it more readable so to find your kernel ID you can check it in the dataset by your name or kernel title.
\nIf we use this graph and calculate the cumulative number of upvotes using the forks, we can receive the table like:
\n
The current winner is @fabiencrom with his MSCI Multiome Quickstart w/ Sparse Matrices kernel. @swimmy is on the 2nd place and the 3rd is @alexandervc.
\nLooking forward for your comments, suggestions and insights!
\nAlex
", "rawMarkdown": "Hi fellow Kagglers,\n\nI have created [the dataset](https://www.kaggle.com/datasets/alexryzhkov/open-problems-multimodal-kernels-stats) and [the example kernel](https://www.kaggle.com/code/alexryzhkov/open-problems-multimodal-kernel-stats) to make some analytics on the kernels, which were created for this competition.\n\nIn the kernel you can see several interesting points like:\n- Top-20 kernel creators\n- Performance Tiers of kernel creators\n- Kernels medals stats\n- Medals for kernels creators (top-20)\n- Kernels languages\n- How many kernels are the forks?\n- How many lines are changed in forks?\n- Does the kernel use GPU or not?\n- Upvotes vs views and Upvotes vs PublicScore\n- Most commented kernels\n- **Kernel forks graph** and tops on it\n\nTo make it more interesting to check here are some results.\n\n### Medals for kernels\n\nHere we can congrats @ambrosm, @fabiencrom and @xiafire for the 1st, 2nd and 3rd place respectively:\n\n\n\n### Forks graph\n\nThe full kernels graph for this competition looks like (you can see it in bigger size if you click on it in kernel with the right mouse button and open it in another tab):\n\n\n\nThe closer look for the part of this graph:\n\n\n\nI have used the kernel IDs in the graph to make it more readable so to find your kernel ID you can check it in the dataset by your name or kernel title.\n\nIf we use this graph and calculate the cumulative number of upvotes using the forks, we can receive the table like:\n\n\n\nThe current winner is @fabiencrom with his [MSCI Multiome Quickstart w/ Sparse Matrices](https://www.kaggle.com/code/fabiencrom/msci-multiome-quickstart-w-sparse-matrices) kernel. @swimmy is on the 2nd place and the 3rd is @alexandervc.\n\nLooking forward for your comments, suggestions and insights!\n\nAlex", "votes": 15 } ], "comments": [], "raw_markdown_by_id": { "2023529": "Hi fellow Kagglers,\n\nI have created [the dataset](https://www.kaggle.com/datasets/alexryzhkov/open-problems-multimodal-kernels-stats) and [the example kernel](https://www.kaggle.com/code/alexryzhkov/open-problems-multimodal-kernel-stats) to make some analytics on the kernels, which were created for this competition.\n\nIn the kernel you can see several interesting points like:\n- Top-20 kernel creators\n- Performance Tiers of kernel creators\n- Kernels medals stats\n- Medals for kernels creators (top-20)\n- Kernels languages\n- How many kernels are the forks?\n- How many lines are changed in forks?\n- Does the kernel use GPU or not?\n- Upvotes vs views and Upvotes vs PublicScore\n- Most commented kernels\n- **Kernel forks graph** and tops on it\n\nTo make it more interesting to check here are some results.\n\n### Medals for kernels\n\nHere we can congrats @ambrosm, @fabiencrom and @xiafire for the 1st, 2nd and 3rd place respectively:\n\n\n\n### Forks graph\n\nThe full kernels graph for this competition looks like (you can see it in bigger size if you click on it in kernel with the right mouse button and open it in another tab):\n\n\n\nThe closer look for the part of this graph:\n\n\n\nI have used the kernel IDs in the graph to make it more readable so to find your kernel ID you can check it in the dataset by your name or kernel title.\n\nIf we use this graph and calculate the cumulative number of upvotes using the forks, we can receive the table like:\n\n\n\nThe current winner is @fabiencrom with his [MSCI Multiome Quickstart w/ Sparse Matrices](https://www.kaggle.com/code/fabiencrom/msci-multiome-quickstart-w-sparse-matrices) kernel. @swimmy is on the 2nd place and the 3rd is @alexandervc.\n\nLooking forward for your comments, suggestions and insights!\n\nAlex" } }