{
  "id": 499281,
  "title": "Need help understanding how to start",
  "url": "/competitions/home-credit-credit-risk-model-stability/discussion/499281",
  "author_name": "",
  "post_date": "2024-05-01T09:25:17.588461100Z",
  "votes": 2,
  "comment_count": 3,
  "views": 0,
  "content": "<p>I'm a beginner and this is my first Kaggle competition. I was wondering if anyone could guide me or offer advice on where to start? The various different training and test files are very confusing and I'm not sure which one I should work on? Do I have to individually perform EDA on each file or should I combine it? Please help</p>",
  "messages": [
    {
      "id": "2786357",
      "postDate": "05/01/2024 09:25:17",
      "content": "<p>I'm a beginner and this is my first Kaggle competition. I was wondering if anyone could guide me or offer advice on where to start? The various different training and test files are very confusing and I'm not sure which one I should work on? Do I have to individually perform EDA on each file or should I combine it? Please help</p>",
      "rawMarkdown": "I'm a beginner and this is my first Kaggle competition. I was wondering if anyone could guide me or offer advice on where to start? The various different training and test files are very confusing and I'm not sure which one I should work on? Do I have to individually perform EDA on each file or should I combine it? Please help",
      "votes": null
    },
    {
      "id": "2786553",
      "postDate": "05/01/2024 11:00:18",
      "content": "<p>Even though the competition ends in one month, you can review public high scoring notebooks, understand the code, and build on top of the meticulous work of others. For further ideas, you can check this competition's discussions, as well as the solutions of past competitions and Stack Overflow posts for coding setbacks. </p>\n<p>Even if the competitions are a great experience - for both learning and awards - I would suggest you to not participate in the competition if you are not familiar with the programming and data science workflow. If you are a novice in the field of machine learning, I would suggest you work on the Getting Started competitions instead, as well as the kaggle datasets, where you work with smaller (csv) files and the main concepts are easier to grasp (This was my 5-month journey, which was quite beneficial for me). </p>",
      "rawMarkdown": "Even though the competition ends in one month, you can review public high scoring notebooks, understand the code, and build on top of the meticulous work of others. For further ideas, you can check this competition's discussions, as well as the solutions of past competitions and Stack Overflow posts for coding setbacks. \n\nEven if the competitions are a great experience - for both learning and awards - I would suggest you to not participate in the competition if you are not familiar with the programming and data science workflow. If you are a novice in the field of machine learning, I would suggest you work on the Getting Started competitions instead, as well as the kaggle datasets, where you work with smaller (csv) files and the main concepts are easier to grasp (This was my 5-month journey, which was quite beneficial for me).",
      "votes": null
    },
    {
      "id": "2789554",
      "postDate": "05/02/2024 17:57:12",
      "content": "<p>There are Playground series that are good for training. This competition is with big data that alone poses additional problems with processing and takes time for training too. Come back to it in a few months as it's very interesting.</p>",
      "rawMarkdown": "There are Playground series that are good for training. This competition is with big data that alone poses additional problems with processing and takes time for training too. Come back to it in a few months as it's very interesting.",
      "votes": null
    },
    {
      "id": "2794893",
      "postDate": "05/05/2024 15:03:39",
      "content": "<p>check the below notebook &amp; reply me for any question in it..</p>\n<p><a href=\"url\" target=\"_blank\">https://www.kaggle.com/code/kunduruanil/credit-risk-data-understanding</a></p>",
      "rawMarkdown": "check the below notebook & reply me for any question in it..\n\n[https://www.kaggle.com/code/kunduruanil/credit-risk-data-understanding](url)",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 2786553,
      "author_name": "andreasbis",
      "author_url": "",
      "post_date": "05/01/2024 11:00:18",
      "content": "<p>Even though the competition ends in one month, you can review public high scoring notebooks, understand the code, and build on top of the meticulous work of others. For further ideas, you can check this competition's discussions, as well as the solutions of past competitions and Stack Overflow posts for coding setbacks. </p>\n<p>Even if the competitions are a great experience - for both learning and awards - I would suggest you to not participate in the competition if you are not familiar with the programming and data science workflow. If you are a novice in the field of machine learning, I would suggest you work on the Getting Started competitions instead, as well as the kaggle datasets, where you work with smaller (csv) files and the main concepts are easier to grasp (This was my 5-month journey, which was quite beneficial for me). </p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 2789554,
      "author_name": "eu1234",
      "author_url": "",
      "post_date": "05/02/2024 17:57:12",
      "content": "<p>There are Playground series that are good for training. This competition is with big data that alone poses additional problems with processing and takes time for training too. Come back to it in a few months as it's very interesting.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 2794893,
      "author_name": "kunduruanil",
      "author_url": "",
      "post_date": "05/05/2024 15:03:39",
      "content": "<p>check the below notebook &amp; reply me for any question in it..</p>\n<p><a href=\"url\" target=\"_blank\">https://www.kaggle.com/code/kunduruanil/credit-risk-data-understanding</a></p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "2786357": "I'm a beginner and this is my first Kaggle competition. I was wondering if anyone could guide me or offer advice on where to start? The various different training and test files are very confusing and I'm not sure which one I should work on? Do I have to individually perform EDA on each file or should I combine it? Please help",
    "2786553": "Even though the competition ends in one month, you can review public high scoring notebooks, understand the code, and build on top of the meticulous work of others. For further ideas, you can check this competition's discussions, as well as the solutions of past competitions and Stack Overflow posts for coding setbacks. \n\nEven if the competitions are a great experience - for both learning and awards - I would suggest you to not participate in the competition if you are not familiar with the programming and data science workflow. If you are a novice in the field of machine learning, I would suggest you work on the Getting Started competitions instead, as well as the kaggle datasets, where you work with smaller (csv) files and the main concepts are easier to grasp (This was my 5-month journey, which was quite beneficial for me).",
    "2789554": "There are Playground series that are good for training. This competition is with big data that alone poses additional problems with processing and takes time for training too. Come back to it in a few months as it's very interesting.",
    "2794893": "check the below notebook & reply me for any question in it..\n\n[https://www.kaggle.com/code/kunduruanil/credit-risk-data-understanding](url)"
  },
  "source": "meta"
}