New to machine learning and data science? No question is too basic or too simple. Feel free to start your own thread, or use this thread as a place to post any first-timer clarifying questions for the Kaggle community to help you with!
\nIf you would consider yourself a beginner but don't know where to get started, let other Kagglers help you take your first steps here!
\nNew to Kaggle? Take a look at a few videos our very own Dr. Rachael Tatman has put together to learn a bit more about site etiquette, Kaggle lingo, and how to enter a competition using Kaggle Notebooks.
\nRemember: Kaggle is for everyone. Whether you're teaming up or sharing tips in the competition forum, we expect everyone to follow our Kaggle community guidelines.
", "messages": [ { "id": 1890605, "postDate": "2022-08-08T22:21:39.187Z", "content": "New to machine learning and data science? No question is too basic or too simple. Feel free to start your own thread, or use this thread as a place to post any first-timer clarifying questions for the Kaggle community to help you with!
\nIf you would consider yourself a beginner but don't know where to get started, let other Kagglers help you take your first steps here!
\nNew to Kaggle? Take a look at a few videos our very own Dr. Rachael Tatman has put together to learn a bit more about site etiquette, Kaggle lingo, and how to enter a competition using Kaggle Notebooks.
\nRemember: Kaggle is for everyone. Whether you're teaming up or sharing tips in the competition forum, we expect everyone to follow our Kaggle community guidelines.
", "rawMarkdown": "New to machine learning and data science? No question is too basic or too simple. Feel free to start your own thread, or use this thread as a place to post any first-timer clarifying questions for the Kaggle community to help you with!\n\nIf you would consider yourself a beginner but don't know where to get started, let other Kagglers help you take your first steps here!\n\nNew to Kaggle? Take a look at a few videos our very own Dr. Rachael Tatman has put together to learn a bit more about [site etiquette](https://www.youtube.com/watch?v=aIus8si_Et0), [Kaggle lingo](https://www.youtube.com/watch?v=sEJHyuWKd-s), and [how to enter a competition using Kaggle Notebooks](https://www.youtube.com/watch?&v=GJBOMWpLpTQ).\n\n**Remember:** Kaggle is for everyone. Whether you're teaming up or sharing tips in the competition forum, we expect everyone to follow our [Kaggle community guidelines](https://www.kaggle.com/community-guidelines).", "votes": 13 }, { "id": 1914237, "postDate": "2022-08-25T21:34:25.530Z", "content": "Hey if you are new, type in Kaggle 30 days of ML. They have a great series that walks you through the whole process of how to learn python and build ML models to submit for competition.
\nGood luck!
", "rawMarkdown": "Hey if you are new, type in Kaggle 30 days of ML. They have a great series that walks you through the whole process of how to learn python and build ML models to submit for competition.\n\nGood luck!", "votes": 1 }, { "id": 1916791, "postDate": "2022-08-28T06:45:44.533Z", "content": "Hi kagglers,
\nI am new to Data Science and Kaggle. This is my second competition.
\nRecently finished Amex competition, last 25% π but learned a lot.
\nBefore joining any other competitions, I was hoping to rest and study top rank solutions of Amex competition.
\nBut two notebooks of this competition, @sbunzini memory reduction & @ambrosm EDA, were stimulating for me, so I hit the join button⦠Thank both of you, you are awesome.
\nGood luck to all of us!
", "rawMarkdown": "Hi kagglers,\n\nI am new to Data Science and Kaggle. This is my second competition.\n\nRecently finished Amex competition, last 25% π but learned a lot.\n\nBefore joining any other competitions, I was hoping to rest and study top rank solutions of Amex competition. \n\nBut two notebooks of this competition, @sbunzini memory reduction & @ambrosm EDA, were stimulating for me, so I hit the join button... Thank both of you, you are awesome. \n\nGood luck to all of us!", "votes": 2, "replies": [ { "id": 1916832, "postDate": "2022-08-28T07:36:49.263Z", "content": "Thanks @doktic! I'm glad that you found my notebook helpful π Good luck to you too with this competition!
", "rawMarkdown": "Thanks @doktic! I'm glad that you found my notebook helpful π Good luck to you too with this competition!", "votes": 2 }, { "id": 1916986, "postDate": "2022-08-28T10:14:04.090Z", "content": "Good luck, @doktic!
", "rawMarkdown": "Good luck, @doktic!", "votes": 1 } ] }, { "id": 1934783, "postDate": "2022-09-11T15:37:56.227Z", "content": "Sometimes it's really hard for beginners to learn from the codes shared by the grandmasters. For me, this has been a part of a slow but steady process. Thanks for sharing this, it really is quite helpful.
", "rawMarkdown": "Sometimes it's really hard for beginners to learn from the codes shared by the grandmasters. For me, this has been a part of a slow but steady process. Thanks for sharing this, it really is quite helpful." }, { "id": 1933799, "postDate": "2022-09-10T19:33:32.883Z", "content": "The datasets for this competition are quite large and as a result I'm processing them to reduce their size or make new representations. The result is new data files with my reduced data set or engineered features. Is it possible to save these with my notebook or do I have to recreate them each time? I could potentially save them locally or in Google Drive, but then I would need to upload them to my notebook each time. It may be faster to just recreate them during each session if they cannot be made persistent along with my notebook.
", "rawMarkdown": "The datasets for this competition are quite large and as a result I'm processing them to reduce their size or make new representations. The result is new data files with my reduced data set or engineered features. Is it possible to save these with my notebook or do I have to recreate them each time? I could potentially save them locally or in Google Drive, but then I would need to upload them to my notebook each time. It may be faster to just recreate them during each session if they cannot be made persistent along with my notebook.", "replies": [ { "id": 1935985, "postDate": "2022-09-12T12:55:51.887Z", "content": "@kirkdco If you write your processed files to disk in your notebook and Save Version, you can then load those files into a new notebook with + Add Data -> Your Notebooks in the editor. Basically, you're creating a new dataset with the saved outputs of one notebook that you can attach to other notebooks.
\nHope this helps!
", "rawMarkdown": "@kirkdco If you write your processed files to disk in your notebook and **Save Version**, you can then load those files into a new notebook with **+ Add Data** -> *Your Notebooks* in the editor. Basically, you're creating a new dataset with the saved outputs of one notebook that you can attach to other notebooks.\n\nHope this helps!", "votes": 1 }, { "id": 1951237, "postDate": "2022-09-22T23:21:19.483Z", "content": "I've finally been able to test this and it works great. Thank you!
", "rawMarkdown": "I've finally been able to test this and it works great. Thank you!", "votes": 1 } ] }, { "id": 1922915, "postDate": "2022-09-01T20:19:42.403Z", "content": "Hi,
\nIt's my first challenge, but I have no idea how to start.
\nLooking forward to learning from you all.
Hi,
\nIt's my first challenge, but I have no idea about the team system.
\nIs it better to form (or join) a team in general?
Hi All ,
\nI am also very new to ML/DS
\nIf someone is trying this competition just to gain skills then please take me with him/her
\nSo that I will also be able to learn how to work on real projects
\nMail id : tanishqsaxena2017@gmail.com
Hi
\nI am new to Kaggle competition.
\nTrying this challenge for the first time.
Hi,
\nIt's my first challenge.
\nOnly two weeks to go, but I will do my best!
Hey if you are new, type in Kaggle 30 days of ML. They have a great series that walks you through the whole process of how to learn python and build ML models to submit for competition.
\nGood luck!
", "votes": 1, "replies": [] }, { "id": 1916791, "author_name": "Doktic", "author_url": "", "post_date": "2022-08-28T06:45:44.533000", "content": "Hi kagglers,
\nI am new to Data Science and Kaggle. This is my second competition.
\nRecently finished Amex competition, last 25% π but learned a lot.
\nBefore joining any other competitions, I was hoping to rest and study top rank solutions of Amex competition.
\nBut two notebooks of this competition, @sbunzini memory reduction & @ambrosm EDA, were stimulating for me, so I hit the join button⦠Thank both of you, you are awesome.
\nGood luck to all of us!
", "votes": 2, "replies": [ { "id": 1916832, "author_name": "Sbunzini", "author_url": "", "post_date": "2022-08-28T07:36:49.263000", "content": "Thanks @doktic! I'm glad that you found my notebook helpful π Good luck to you too with this competition!
", "votes": 2, "replies": [] }, { "id": 1916986, "author_name": "AmbrosM", "author_url": "", "post_date": "2022-08-28T10:14:04.090000", "content": "Good luck, @doktic!
", "votes": 1, "replies": [] } ] }, { "id": 1934783, "author_name": "Prashant Upadhyay", "author_url": "", "post_date": "2022-09-11T15:37:56.227000", "content": "Sometimes it's really hard for beginners to learn from the codes shared by the grandmasters. For me, this has been a part of a slow but steady process. Thanks for sharing this, it really is quite helpful.
", "votes": 0, "replies": [] }, { "id": 1933799, "author_name": "KirkDCO", "author_url": "", "post_date": "2022-09-10T19:33:32.883000", "content": "The datasets for this competition are quite large and as a result I'm processing them to reduce their size or make new representations. The result is new data files with my reduced data set or engineered features. Is it possible to save these with my notebook or do I have to recreate them each time? I could potentially save them locally or in Google Drive, but then I would need to upload them to my notebook each time. It may be faster to just recreate them during each session if they cannot be made persistent along with my notebook.
", "votes": 0, "replies": [ { "id": 1935985, "author_name": "Ryan Holbrook", "author_url": "", "post_date": "2022-09-12T12:55:51.887000", "content": "@kirkdco If you write your processed files to disk in your notebook and Save Version, you can then load those files into a new notebook with + Add Data -> Your Notebooks in the editor. Basically, you're creating a new dataset with the saved outputs of one notebook that you can attach to other notebooks.
\nHope this helps!
", "votes": 1, "replies": [] }, { "id": 1951237, "author_name": "KirkDCO", "author_url": "", "post_date": "2022-09-22T23:21:19.483000", "content": "I've finally been able to test this and it works great. Thank you!
", "votes": 1, "replies": [] } ] }, { "id": 1922915, "author_name": "Jose Alex", "author_url": "", "post_date": "2022-09-01T20:19:42.403000", "content": "Hi,
\nIt's my first challenge, but I have no idea how to start.
\nLooking forward to learning from you all.
Hi,
\nIt's my first challenge, but I have no idea about the team system.
\nIs it better to form (or join) a team in general?
Hi All ,
\nI am also very new to ML/DS
\nIf someone is trying this competition just to gain skills then please take me with him/her
\nSo that I will also be able to learn how to work on real projects
\nMail id : tanishqsaxena2017@gmail.com
Hi
\nI am new to Kaggle competition.
\nTrying this challenge for the first time.
Hi,
\nIt's my first challenge.
\nOnly two weeks to go, but I will do my best!