{
  "fetched_at": "2026-10-19T15:36:58Z",
  "source_url": "https://www.kaggle.com/competitions/airbnblala/data",
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    {
      "name": "rules",
      "content": "<!-- Begin Base Rules -->\n<h3>One account per participant </h3>\n<p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts. </p>\n<h3>No private sharing </h3>\n<p>Privately sharing code or data outside is not permitted. It's okay to share code if made available to all participants on the forums.</p>\n<h3>Teams</h3>\n<p>Teams are not allowed in this competition.</p>\n<h3>Submission Limits</h3>\n<p>You may submit a maximum of 3 entries per day.</p>\n<p>If doing this for course credit, you must have a total of at least five submissions </p>\n<p>You may select up to 2 final submissions for judging.</p>\n<h3>Competition Timeline</h3>\n<p>Start Date: April 11, 2018</p>\n<p>End Date: April 30, 2018 5:30 PM ET (11:30 PM UTC)</p>",
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      "content": "<h2>File descriptions</h2>\n<ul>\n<li><strong>analysisData.csv</strong>: Data for building a model</li>\n<li><strong>scoringData.csv</strong>: Use for applying predictings or scoring</li>\n<li><strong>sample_submission.csv</strong>: a sample submission file in the correct format</li>\n</ul>",
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      "name": "Description",
      "content": "**Description**\n\nThis is a listing of over 35000 Airbnb rentals in New York City. The goal of this competition is to predict the price for a rental using over 90 variables on the property, host, and past reviews.\n\n<br>\n\n**Goal**\n\nTo predict price of an Airbnb rental. Generate a prediction for each id in scoringData.csv.\n\n**Metric**\n\nSubmissions will be evaluated based on RMSE (root mean squared error) ([Wikipedia][1]). Lower the RMSE, better the model. \n\n**Submission File**\n\nThe submission file should be in text format (.csv) with only two columns, id and price. The price column must contain predicted price. Number of decimal places to use is up to you. The file should contain a header and have the following format:\n\n    \"id\",\"price\"\n    4989,129.486430474832\n    5054,132.653631075377\n    5136,135.133399411318\n    6090,134.517956672707\n    6990,135.133399411318\n\nAn example of the sample submission file (sample_submission.csv) is shared with the set of files. \n\n**Sample Code**\n\nHere is an illustration in R of how you can create a model, apply it to scoringData.csv and prepare a submission file (sample_submission.csv). \n\n    # ensure analysisData.csv and scoringData.csv are in your working directory\n\n    # following code will read data and construct a simple model\n    data = read.csv('analysisData.csv')\n    model = lm(price~minimum_nights+review_scores_rating,data)\n\n    # read in scoring data and apply model to generate predictions\n    scoringData = read.csv('scoringData.csv')\n    pred = predict(model,newdata=scoringData)\n\n    # construct submision from predictions\n    submissionFile = data.frame(id = scoringData$id, price = pred)\n    write.csv(submissionFile, 'sample_submission.csv',row.names = F)\n\n  [1]: https://en.wikipedia.org/wiki/Root-mean-square_deviation",
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