{
  "fetched_at": "2026-10-19T19:13:33Z",
  "source_url": "https://www.kaggle.com/competitions/dataquest/data",
  "pages": [
    {
      "name": "rules",
      "content": " - The competition will go on for four days. \n - The competition will start from 28th Feb 8:00 PM.\n - The competition ends on 4th March 11:00 PM \n - The leaderboard will display your rankings \n - Top three rank holders will be awarded. Cash prizes include 500,300 & 200 rs \n - The final rankings may be different from initial rankings.\n   \n  ",
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      "content": "<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",
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      "name": "Description",
      "content": "<h3>Problem Description</h3>\n<p>Customer Happiness has become the top priority for service & product based companies. To an extent, some of the companies even appoint CHOs (Customer Happiness Officer) to ensure delivering a kickass and delightful customer experience. Well, machine learning is now playing a pivotal role in delivering that experience. The ability to predict happy and unhappy customers give companies a nice head-start to improve their experience. TripAdvisor is the world's largest travel site where you can compare and book hotels, flights, restaurants etc. The data set provided in this challenge consists of a sample of hotel reviews provided by the customers. Analysing customers reviews will help them understand about the hotels listed on their website i.e. if they are treating customers well or if they are providing hospitality services as expected. In this challenge, you have to predict if a customer is happy or not happy.</p>\n<h3>Data Description</h3>\n<p>You are given three files to download: train.csv, test.csv and sample_submission.csv\nThe training data has 30000 rows and test data has 9000 rows.</p>\n<h3>Variable &nbsp&nbsp&nbsp Description</h3>\n<ul>\n<li>User_ID &nbsp&nbsp&nbspunique ID of the customer</li>\n<li>Description &nbsp&nbsp&nbspdescription of the review posted</li>\n<li>Browser_Used&nbsp&nbsp&nbsp browser used to post the review</li>\n<li>Device_Used&nbsp&nbsp&nbsp device used to post the review</li>\n<li>Is_Response &nbsp&nbsp&nbsptarget Variable</li>\n</ul>\n<h3>Submissions</h3>\n<p>A participant has to submit a csv file containing User_ID and predicted labels in a csv format.\nCheck the sample submission file for format.</p><br>\nUser_ID, Is_Response<br>\nid80132,happy<br>\nid80133,not happy<br>\nid80134,not happy<br>\nid80135,not happy<br>\nid80136,happy<br>\n\n<h3>Evaluation Metric</h3>\n<p>Submission will be evaluated based on Accuracy score. Higher the better.</p>",
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