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  "fetched_at": "2026-10-20T03:54:33Z",
  "source_url": "https://www.kaggle.com/competitions/cs419-assgmt1-20-21/data",
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      "content": "\n<h2>File descriptions</h2>\n<ol>\n<li><strong>df_train.csv</strong> - the training set</li>\n<li><strong>df_val.csv</strong> - the development set</li>\n<li><strong>df_test.csv</strong> - the test set</li>\n</ol>\n<h2>Data fields</h2>\n<ol>\n<li><strong>name</strong> -  Brand and model name of automobile</li>\n<li><strong>year </strong> - Year it was manufactured</li>\n<li><strong>km_driven </strong> - value representing km driven </li>\n<li><strong> Fuel </strong> - Petrol/Diesel/CNG car</li>\n<li><strong> Seller type </strong> - Car being sold by an individual or a Dealer</li>\n<li><strong> Transmission </strong> - Manual/Automatic transmission car</li>\n<li><strong> Fuel </strong> - Petrol/Diesel/CNG car</li>\n<li> <strong> owner </strong> - number of previous owners</li>\n<li><strong> Mileage </strong> - Petrol/Diesel/CNG car</li>\n<li><strong> Engine </strong> - Petrol/Diesel/CNG car</li>\n<li><strong> Seats </strong> - Petrol/Diesel/CNG car</li>\n<li><strong> Max Power </strong> - Maximum power(HP) generated by engine </li>\n<li><strong> Torque </strong> - Maximum torque generated by engine </li>\n</ol>\n\n<h2>Target</h2>\n<ol>\n<li><strong> Selling Price </strong> - cost (in Rs.) of used car </li>\n</ol>",
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      "name": "Description",
      "content": "<p>Predicting prices of pre-owned automobiles can be useful for websites like Cars24 and Olx. Given just the model and manufacture year, one could come up with an reasonable price. Can one do better with additional features? In this assignment, you will be given a set of data points with different features characterizing pre-owned cars in India. You will need to find the best linear regression model that most accurately predicts car prices for a set of test cars.</p>\nPlease click on the \"Evaluation\" tab on the left for submission details.\n\n<ul>\n    <h3>One account per participant.</h3> \n\n    <li>You cannot sign up on Kaggle using multiple accounts and therefore you cannot submit from multiple accounts.</li>\n\n<li> You must have your <strong> ROLL Number </strong> (say 183050000) as your <strong> Public name </strong> till contest ends </li>\n</ul>\n<ul>\n    <h3>No private sharing among students</h3>\n\n    <li>\n        Privately sharing code or data is not permitted. <strong> Violation of this can lead to serious academic punishments. </strong>\n     \n</li></ul>\n\n<ul>\n    <h3>Submission Limits</h3>\n\n    <li>You may submit a maximum of 15 entries per day.</li>\n    <li>You may select up to 2 final submissions that will be used to evaluate your standing on the \"Private Leaderboard\".</li>\n</ul>\n<ul>\n    <h3>Competition Timeline</h3>\n    <p>Start Date: <strong>13/02/2020</strong></p>\n    <p>End Date: <strong>14/03/2020 11:55 PM IST  </strong></p>\n</ul>\n",
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    {
      "name": "Evaluation",
      "content": "<p>The evaluation pages describes how submissions will be scored and how students should format their submissions. </p>\n<hr>\n<p>The evaluation metric for this competition is <a href=\"https://en.wikipedia.org/wiki/Root-mean-square_deviation\">RMS Error</a>.  The RMS Error is given by:</p>\n\n<p>\\[ \\text{RMS Error} = \\sqrt{\\frac{\\sum_{i=1}^{n}(\\hat{y} - y)^2}{n}} \\]</p>\n\n<h3> Public Leaderboard is evaluated only on 50% of the test data, remaining 50% will be evaluated after the contest ends</h3>\n\n<h2>Submission Format</h2>\n<p> For every row in the test dataset, there should be two columns in the output.csv file: <strong> Id and target</strong>. Order of the results should be kept the same.</p>\n\n<pre>Id, target<br>0,10<br></pre>",
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    {
      "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!",
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