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 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": 2250737, "postDate": "2023-05-08T18:38:43.680Z", "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 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 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": 19 }, { "id": 2324679, "postDate": "2023-06-30T19:08:40.700Z", "content": "Any team to join?
", "rawMarkdown": "Any team to join? ", "votes": 3, "replies": [ { "id": 2325636, "postDate": "2023-07-01T13:30:17.860Z", "content": "Yes, I am ready to join.
", "rawMarkdown": "Yes, I am ready to join. \n", "votes": 1, "replies": [ { "id": 2326684, "postDate": "2023-07-02T10:16:17.683Z", "content": "Me too, you can email me on amlhassanabdelhamid@gmail.com
", "rawMarkdown": "Me too, you can email me on amlhassanabdelhamid@gmail.com " } ] }, { "id": 2351577, "postDate": "2023-07-20T08:44:29.380Z", "content": "I'm also looking for a team to join.
", "rawMarkdown": "I'm also looking for a team to join." } ] }, { "id": 2271037, "postDate": "2023-05-23T15:24:55.680Z", "content": "Hi,
\nfairly new to kaggle. Is there any way that I can access the competition data while using another Notebook provider? The kaggle notebook is running incredibly slow for me and I have access to a Paperspace Gradient subscription, I would like to use that one.
\nSo far the only way I have found out is to download all the data locally, upload it to the paperspace gradient storage and then run the rest. However, this will a) take a lot of time and b) I do not have so much storage over there.
", "rawMarkdown": "Hi,\n\nfairly new to kaggle. Is there any way that I can access the competition data while using another Notebook provider? The kaggle notebook is running incredibly slow for me and I have access to a Paperspace Gradient subscription, I would like to use that one. \n\nSo far the only way I have found out is to download all the data locally, upload it to the paperspace gradient storage and then run the rest. However, this will a) take a lot of time and b) I do not have so much storage over there.", "votes": 1, "replies": [ { "id": 2272642, "postDate": "2023-05-24T16:09:23.837Z", "content": "Hi alex,
\nthere is a Kaggle API that you can access the kaggle features through code.
\nyou just need to install kaggle libarary and have your Kaggle.json.
\nyou can find good tutorial out there. this one is a good one
Kaggle API: Kaggle provides a Python API that allows you to interact with the platform programmatically. This means you can use the API to download competition data directly to your Paperspace Gradient workspace.
\nTo get started, you'll need to install the Kaggle API package if you haven't already. You can do this by running the following command in your Paperspace Gradient notebook:
\n!pip install kaggle
\nThen, you'll need to set up your Kaggle API credentials. Go to your Kaggle account settings, generate an API token, and download the kaggle.json file. Upload this file to your Paperspace Gradient workspace.
In your Paperspace Gradient notebook, you can use the Kaggle API to download competition data using the following command:
\n!kaggle competitions download -c competition-name -p /path/to/save
\nReplace competition-name with the actual name of the competition and /path/to/save with the directory where you want to save the data.
Direct Download: Some Kaggle competitions provide a direct download link for the competition data. This link can be found on the competition's data page. You can use this link in your Paperspace Gradient notebook to directly download the data without going through the Kaggle website.
\nCloud Storage: If you have access to cloud storage like Google Drive or Dropbox, you can upload the data there and access it from your Paperspace Gradient notebook. Many cloud storage services provide APIs that allow you to interact with your storage directly from your notebook.
\nExternal URLs: If the competition data is available on a public URL, you can use libraries like wget or Python's requests to download the data directly to your Paperspace Gradient workspace.
\nBy using one of these methods, you can avoid the need to download all the data locally and then upload it again, saving time and storage space. Just make sure you're adhering to the terms and conditions of the competition and respecting data usage policies.
", "rawMarkdown": "Kaggle API: Kaggle provides a Python API that allows you to interact with the platform programmatically. This means you can use the API to download competition data directly to your Paperspace Gradient workspace.\n\nTo get started, you'll need to install the Kaggle API package if you haven't already. You can do this by running the following command in your Paperspace Gradient notebook:\n\n\n!pip install kaggle\nThen, you'll need to set up your Kaggle API credentials. Go to your Kaggle account settings, generate an API token, and download the kaggle.json file. Upload this file to your Paperspace Gradient workspace.\n\nIn your Paperspace Gradient notebook, you can use the Kaggle API to download competition data using the following command:\n\n\n!kaggle competitions download -c competition-name -p /path/to/save\nReplace competition-name with the actual name of the competition and /path/to/save with the directory where you want to save the data.\n\nDirect Download: Some Kaggle competitions provide a direct download link for the competition data. This link can be found on the competition's data page. You can use this link in your Paperspace Gradient notebook to directly download the data without going through the Kaggle website.\n\nCloud Storage: If you have access to cloud storage like Google Drive or Dropbox, you can upload the data there and access it from your Paperspace Gradient notebook. Many cloud storage services provide APIs that allow you to interact with your storage directly from your notebook.\n\nExternal URLs: If the competition data is available on a public URL, you can use libraries like wget or Python's requests to download the data directly to your Paperspace Gradient workspace.\n\nBy using one of these methods, you can avoid the need to download all the data locally and then upload it again, saving time and storage space. Just make sure you're adhering to the terms and conditions of the competition and respecting data usage policies.", "votes": 2 } ] }, { "id": 2385097, "postDate": "2023-08-11T07:14:10.173Z", "content": "I m very new to DS, ML, and AI even.. looking for team to join
", "rawMarkdown": "I m very new to DS, ML, and AI even.. looking for team to join" }, { "id": 2381658, "postDate": "2023-08-09T10:13:30.603Z", "content": "looking for teammates that have basically the same score.😺
", "rawMarkdown": "looking for teammates that have basically the same score.😺" }, { "id": 2379518, "postDate": "2023-08-08T07:40:56.043Z", "content": "I would like to join a team. I am not so new to Machine Learning but sharing ideas with others is what makes one stronger
", "rawMarkdown": "I would like to join a team. I am not so new to Machine Learning but sharing ideas with others is what makes one stronger", "replies": [ { "id": 2389788, "postDate": "2023-08-14T08:34:00.240Z", "content": "Can we have a team
", "rawMarkdown": "Can we have a team", "replies": [ { "id": 2389852, "postDate": "2023-08-14T09:05:30.310Z", "content": "If you are interested in forming a team or anyone else email me at afrahyasin3@gmail.com
", "rawMarkdown": "If you are interested in forming a team or anyone else email me at afrahyasin3@gmail.com" } ] } ] }, { "id": 2375636, "postDate": "2023-08-05T19:19:28.670Z", "content": "Hello!
\nI am new to Data Science and this is my first Kaggle competition (I've jumped into the ocean and have to learn how to swim). Could someone please tell me specifically which part of the code am I supposed to modify? I was able to install the dependencies and run the entire code on my local machine and it runs perfectly well. I am talking about the code by @gusthema .
\nAll works well. But where and how should I make my contribution so that I can generate and submit my results?
", "rawMarkdown": "Hello! \n\nI am new to Data Science and this is my first Kaggle competition (I've jumped into the ocean and have to learn how to swim). Could someone please tell me specifically which part of the code am I supposed to modify? I was able to install the dependencies and run the entire code on my local machine and it runs perfectly well. I am talking about the code by @gusthema .\n\nAll works well. But where and how should I make my contribution so that I can generate and submit my results?" }, { "id": 2355448, "postDate": "2023-07-23T10:51:03.967Z", "content": "new to kaggle want to join any team ,I love data
", "rawMarkdown": "new to kaggle want to join any team ,I love data " }, { "id": 2351574, "postDate": "2023-07-20T08:43:24.147Z", "content": "Hi Everyone, I'm looking for a team to join.
", "rawMarkdown": "Hi Everyone, I'm looking for a team to join. ", "replies": [ { "id": 2383355, "postDate": "2023-08-10T10:12:52.330Z", "content": "G'day Sara , Would you like we team up? please email me at amir.mahboud@gmail.com
", "rawMarkdown": "G'day Sara , Would you like we team up? please email me at amir.mahboud@gmail.com" } ] }, { "id": 2294319, "postDate": "2023-06-10T01:31:19.423Z", "content": "404 - Not Found Cannot download the dataset. Thanks!
", "rawMarkdown": "404 - Not Found Cannot download the dataset. Thanks!", "replies": [ { "id": 2294320, "postDate": "2023-06-10T01:33:20.087Z", "content": "@ashleychow Thank you so much!
", "rawMarkdown": "@ashleychow Thank you so much!", "replies": [ { "id": 2294493, "postDate": "2023-06-10T06:01:56.553Z", "content": "I also have the same problem I cannot find this competition dataset.
", "rawMarkdown": "I also have the same problem I cannot find this competition dataset.", "votes": 1 } ] }, { "id": 2295908, "postDate": "2023-06-11T11:35:41.540Z", "content": "\n\n404 - Not Found Cannot download the dataset. Thanks!
\n
Now can be downloaded. Thanks!
", "rawMarkdown": "> 404 - Not Found Cannot download the dataset. Thanks!\n\nNow can be downloaded. Thanks!", "votes": 1 } ] }, { "id": 2288601, "postDate": "2023-06-05T14:14:59.900Z", "content": "Hi,
\nI am pretty new to the idea's of deep learning and its usage in real world applications. I was wondering about 2 questions, how I can create a training and validation set and if there are any resources that I should look into considering I am trying to build a CNN and or a RNN for this competition but unfortunately, I just don't know where to start.
", "rawMarkdown": "Hi, \n\nI am pretty new to the idea's of deep learning and its usage in real world applications. I was wondering about 2 questions, how I can create a training and validation set and if there are any resources that I should look into considering I am trying to build a CNN and or a RNN for this competition but unfortunately, I just don't know where to start.", "replies": [ { "id": 2294318, "postDate": "2023-06-10T01:03:00.547Z", "content": "I suggest you to look at the EDA post under a competition, it will tell you what the data looks like, and as expected some will provide you the way to split data or Kaggle could already split them for you. Then you can dive deep into your desired algorithm in your mind by researching over the internet and combine with the data of a competition.
\nGood Luck :)
thanks！！I get a great Help
", "rawMarkdown": "thanks！！I get a great Help" }, { "id": 2254323, "postDate": "2023-05-10T20:58:09.557Z", "content": "thank you!
", "rawMarkdown": "thank you!" } ], "comments": [ { "id": 2324679, "author_name": "Abdellah El iraoui", "author_url": "", "post_date": "2023-06-30T19:08:40.700000", "content": "Any team to join?
", "votes": 3, "replies": [ { "id": 2325636, "author_name": "Aryan Singh Bhadouria", "author_url": "", "post_date": "2023-07-01T13:30:17.860000", "content": "Yes, I am ready to join.
", "votes": 1, "replies": [ { "id": 2326684, "author_name": "Aml Hassan Esmil", "author_url": "", "post_date": "2023-07-02T10:16:17.683000", "content": "Me too, you can email me on amlhassanabdelhamid@gmail.com
", "votes": 0, "replies": [] } ] }, { "id": 2351577, "author_name": "Sara Darvishi", "author_url": "", "post_date": "2023-07-20T08:44:29.380000", "content": "I'm also looking for a team to join.
", "votes": 0, "replies": [] } ] }, { "id": 2271037, "author_name": "Alex A.", "author_url": "", "post_date": "2023-05-23T15:24:55.680000", "content": "Hi,
\nfairly new to kaggle. Is there any way that I can access the competition data while using another Notebook provider? The kaggle notebook is running incredibly slow for me and I have access to a Paperspace Gradient subscription, I would like to use that one.
\nSo far the only way I have found out is to download all the data locally, upload it to the paperspace gradient storage and then run the rest. However, this will a) take a lot of time and b) I do not have so much storage over there.
", "votes": 1, "replies": [ { "id": 2272642, "author_name": "M_Ariyan", "author_url": "", "post_date": "2023-05-24T16:09:23.837000", "content": "Hi alex,
\nthere is a Kaggle API that you can access the kaggle features through code.
\nyou just need to install kaggle libarary and have your Kaggle.json.
\nyou can find good tutorial out there. this one is a good one
Kaggle API: Kaggle provides a Python API that allows you to interact with the platform programmatically. This means you can use the API to download competition data directly to your Paperspace Gradient workspace.
\nTo get started, you'll need to install the Kaggle API package if you haven't already. You can do this by running the following command in your Paperspace Gradient notebook:
\n!pip install kaggle
\nThen, you'll need to set up your Kaggle API credentials. Go to your Kaggle account settings, generate an API token, and download the kaggle.json file. Upload this file to your Paperspace Gradient workspace.
In your Paperspace Gradient notebook, you can use the Kaggle API to download competition data using the following command:
\n!kaggle competitions download -c competition-name -p /path/to/save
\nReplace competition-name with the actual name of the competition and /path/to/save with the directory where you want to save the data.
Direct Download: Some Kaggle competitions provide a direct download link for the competition data. This link can be found on the competition's data page. You can use this link in your Paperspace Gradient notebook to directly download the data without going through the Kaggle website.
\nCloud Storage: If you have access to cloud storage like Google Drive or Dropbox, you can upload the data there and access it from your Paperspace Gradient notebook. Many cloud storage services provide APIs that allow you to interact with your storage directly from your notebook.
\nExternal URLs: If the competition data is available on a public URL, you can use libraries like wget or Python's requests to download the data directly to your Paperspace Gradient workspace.
\nBy using one of these methods, you can avoid the need to download all the data locally and then upload it again, saving time and storage space. Just make sure you're adhering to the terms and conditions of the competition and respecting data usage policies.
", "votes": 2, "replies": [] } ] }, { "id": 2385097, "author_name": "Nisha Patodia", "author_url": "", "post_date": "2023-08-11T07:14:10.173000", "content": "I m very new to DS, ML, and AI even.. looking for team to join
", "votes": 0, "replies": [] }, { "id": 2381658, "author_name": "SEVEN", "author_url": "", "post_date": "2023-08-09T10:13:30.603000", "content": "looking for teammates that have basically the same score.😺
", "votes": 0, "replies": [] }, { "id": 2379518, "author_name": "AY", "author_url": "", "post_date": "2023-08-08T07:40:56.043000", "content": "I would like to join a team. I am not so new to Machine Learning but sharing ideas with others is what makes one stronger
", "votes": 0, "replies": [ { "id": 2389788, "author_name": "Sudarshan Sutar", "author_url": "", "post_date": "2023-08-14T08:34:00.240000", "content": "Can we have a team
", "votes": 0, "replies": [ { "id": 2389852, "author_name": "AY", "author_url": "", "post_date": "2023-08-14T09:05:30.310000", "content": "If you are interested in forming a team or anyone else email me at afrahyasin3@gmail.com
", "votes": 0, "replies": [] } ] } ] }, { "id": 2375636, "author_name": "Akarsh Sahay", "author_url": "", "post_date": "2023-08-05T19:19:28.670000", "content": "Hello!
\nI am new to Data Science and this is my first Kaggle competition (I've jumped into the ocean and have to learn how to swim). Could someone please tell me specifically which part of the code am I supposed to modify? I was able to install the dependencies and run the entire code on my local machine and it runs perfectly well. I am talking about the code by @gusthema .
\nAll works well. But where and how should I make my contribution so that I can generate and submit my results?
", "votes": 0, "replies": [] }, { "id": 2355448, "author_name": "muhammad bilal 07", "author_url": "", "post_date": "2023-07-23T10:51:03.967000", "content": "new to kaggle want to join any team ,I love data
", "votes": 0, "replies": [] }, { "id": 2351574, "author_name": "Sara Darvishi", "author_url": "", "post_date": "2023-07-20T08:43:24.147000", "content": "Hi Everyone, I'm looking for a team to join.
", "votes": 0, "replies": [ { "id": 2383355, "author_name": "Amir Mahboud", "author_url": "", "post_date": "2023-08-10T10:12:52.330000", "content": "G'day Sara , Would you like we team up? please email me at amir.mahboud@gmail.com
", "votes": 0, "replies": [] } ] }, { "id": 2294319, "author_name": "PIG", "author_url": "", "post_date": "2023-06-10T01:31:19.423000", "content": "404 - Not Found Cannot download the dataset. Thanks!
", "votes": 0, "replies": [ { "id": 2294320, "author_name": "PIG", "author_url": "", "post_date": "2023-06-10T01:33:20.087000", "content": "@ashleychow Thank you so much!
", "votes": 0, "replies": [ { "id": 2294493, "author_name": "tanukon", "author_url": "", "post_date": "2023-06-10T06:01:56.553000", "content": "I also have the same problem I cannot find this competition dataset.
", "votes": 1, "replies": [] } ] }, { "id": 2295908, "author_name": "PIG", "author_url": "", "post_date": "2023-06-11T11:35:41.540000", "content": "\n\n404 - Not Found Cannot download the dataset. Thanks!
\n
Now can be downloaded. Thanks!
", "votes": 1, "replies": [] } ] }, { "id": 2288601, "author_name": "Gabe The Man", "author_url": "", "post_date": "2023-06-05T14:14:59.900000", "content": "Hi,
\nI am pretty new to the idea's of deep learning and its usage in real world applications. I was wondering about 2 questions, how I can create a training and validation set and if there are any resources that I should look into considering I am trying to build a CNN and or a RNN for this competition but unfortunately, I just don't know where to start.
", "votes": 0, "replies": [ { "id": 2294318, "author_name": "Honghu Luo", "author_url": "", "post_date": "2023-06-10T01:03:00.547000", "content": "I suggest you to look at the EDA post under a competition, it will tell you what the data looks like, and as expected some will provide you the way to split data or Kaggle could already split them for you. Then you can dive deep into your desired algorithm in your mind by researching over the internet and combine with the data of a competition.
\nGood Luck :)
thanks！！I get a great Help
", "votes": 0, "replies": [] }, { "id": 2254323, "author_name": "Ислам Юсуфов", "author_url": "", "post_date": "2023-05-10T20:58:09.557000", "content": "thank you!
", "votes": 0, "replies": [] } ], "raw_markdown_by_id": { "2250737": "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 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).", "2324679": "Any team to join? ", "2271037": "Hi,\n\nfairly new to kaggle. Is there any way that I can access the competition data while using another Notebook provider? The kaggle notebook is running incredibly slow for me and I have access to a Paperspace Gradient subscription, I would like to use that one. \n\nSo far the only way I have found out is to download all the data locally, upload it to the paperspace gradient storage and then run the rest. However, this will a) take a lot of time and b) I do not have so much storage over there.", "2385097": "I m very new to DS, ML, and AI even.. looking for team to join", "2381658": "looking for teammates that have basically the same score.😺", "2379518": "I would like to join a team. I am not so new to Machine Learning but sharing ideas with others is what makes one stronger", "2375636": "Hello! \n\nI am new to Data Science and this is my first Kaggle competition (I've jumped into the ocean and have to learn how to swim). Could someone please tell me specifically which part of the code am I supposed to modify? I was able to install the dependencies and run the entire code on my local machine and it runs perfectly well. I am talking about the code by @gusthema .\n\nAll works well. But where and how should I make my contribution so that I can generate and submit my results?", "2355448": "new to kaggle want to join any team ,I love data ", "2351574": "Hi Everyone, I'm looking for a team to join. ", "2294319": "404 - Not Found Cannot download the dataset. Thanks!", "2288601": "Hi, \n\nI am pretty new to the idea's of deep learning and its usage in real world applications. I was wondering about 2 questions, how I can create a training and validation set and if there are any resources that I should look into considering I am trying to build a CNN and or a RNN for this competition but unfortunately, I just don't know where to start.", "2380449": "", "2380441": "", "2304445": "thanks！！I get a great Help", "2254323": "thank you!" } }