{
  "fetched_at": "2026-10-19T23:59:12Z",
  "source_url": "https://www.kaggle.com/competitions/find-the-fund/data",
  "pages": [
    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
      "isPublished": false,
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      "postTitle": "",
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    {
      "name": "data-description",
      "content": "##Data Description\n<p>The data contains information about 40 thousands companies, spread across the globe, in the form of 17 columns, each having specific information about the respective company.</p>\n<h2>File descriptions</h2>\n<ul>\n<li><strong>train.csv</strong> - the training set</li>\n<li><strong>test.csv</strong> - the test set</li>\n<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format</li>\n</ul>\n<h2>Columns Description</h2>\n<ul>\n<li><strong>ID</strong> - Id of the Company</li>\n<li><strong>company_name</strong> - Name of the Company</li>\n<li><strong>website</strong> - Company Website URL</li>\n<li><strong>op_status</strong> - Operational status (0 = closed or operating, 1 = acquired)</li>\n<li><strong>domain</strong> - Industry Category of the company, including up to four subcategory divisions</li>\n<li><strong>founded_on</strong> - The date the company was founded (in string format ‘2003-09-04’)</li>\n<li><strong>hq_country_code</strong> - Country Code of the company’s HQ</li>\n<li><strong>hq_state_code</strong> - State Code of the company’s HQ</li>\n<li><strong>hq_region</strong> - Region of the company’s HQ</li>\n<li><strong>hq_city</strong> - City of the company’s HQ</li>\n<li><strong>total_funding_usd</strong> - Total Amount raised in all the funding rounds</li>\n<li><strong>funding_rounds</strong> - Total count of the funding rounds</li>\n<li><strong>first_funding_date</strong> -Date the company first raised funding (in string format ‘2008-07-11’)</li>\n<li><strong>last_funding_date</strong> - Date the company last raised funding (in string format ‘2010-10-28’)</li>\n<li><strong>num_investors</strong> - Number of investors</li>\n<li><strong>first_funding_utc</strong> - Time (in UTC) the company first raised funding (in string format ‘2010-10-28’)\n</li>\n<li><strong>last_funding_utc</strong> - Time (in UTC) the company last raised funding (in string format ‘2010-10-28’)\n</li>\n<li><strong>successful_investment</strong> - Output Label; whether investment will be successful or not (0 = unsuccessful, 1 = successful)\n</li",
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    },
    {
      "name": "Description",
      "content": "<p>Majumdar, Khanna and Rawat have been invited to the 20th Annual Global Entrepreneurship Summit in Los Angeles, because of their exceptional skills in the field of Machine Learning. The investors in the summit are curious to know about the various incubating startups that are participating in the summit, and are also interested in funding them, if they are impressed by their work.</p>\n<p>The organisation team of the event has the data of all the companies that participated in the previous editions and also some outsourced data about other startups. They are willing to share the data with our three friends, provided they help investors in deciding whether they should invest in a particular startup or not.</p>\n<p>Can you help our three friends in their task?\n</p>",
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    },
    {
      "name": "Evaluation",
      "content": "#Evaluation\n<p>We will be using categorisation accuracy to calculate the performance of your model. Categorisation accuracy is basically the same as sklearn.metrics.accuracy_score(). Which calculates (the number of correct predictions)/(total number of predictions).\n</p>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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}