{
  "fetched_at": "2026-10-20T06:33:27Z",
  "source_url": "https://www.kaggle.com/competitions/cart-time-series/data",
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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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      "name": "data-description",
      "content": "<p> The data contains Godown Name, Date and Total Qty of Fridge Sold in the Godown</p>\n<li><strong>Data.xlsx</strong> - the training set</li>\n<li><strong>FestivalDates.xlsx</strong> - Important Festival Dates</li>\n<li><strong>sampleSubmission.txt</strong> - a sample submission file in the correct format</li>\n",
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      "name": "Description",
      "content": "<h2>Overview</h2>\n<p>Roma Electronics is a brick & mortar electronics store operating in the country for last 50 years. Roma electronics needs an accurate prediction of Quantity of  Fridge that will be sold for each Godown for the next 6 Months to</p>\n1. Block inventory with the brand\n2. Negotiate a good deal with the brand\n3. Avoid over/under stocking of inventory in their own godowns</p>\n\n<p>The procurement manager of Roma Electronics has contacted you to help him out with Fridge prediction for Each Godown for next 6 Months.</p>\n\n<p>The company has provided you day-wise data of the quantity of Fridge sold in each Godown for the last 3.5 years. They have also provided you with some special/festive dates when there are specific offers running which helps drive sales during these periods. \nHe expects you to predict the quantity sold in the next 6 Months for each Godown (Refer Sample submission in Kaggle).</p>\n\n\n",
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    {
      "name": "Evaluation",
      "content": "<p>The evaluation metric for this competition is <a href=\"https://www.statisticshowto.datasciencecentral.com/mean-absolute-percentage-error-mape/\"> MAPE</a>\n\n<p>The mean absolute percentage error (MAPE) is a statistical measure of how accurate a forecast system is. It measures this accuracy as a percentage, and can be calculated as the average absolute percent error for each time period minus actual values divided by actual values. Where At is the actual value and Ft is the forecast value, this is given by:<p>\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1255074%2Fe3d41829237cd8d75bd177c2bfedec3c%2FMAPE.PNG?generation=1577775597684407&alt=media)\n\n\n\n\n<p>The mean absolute percentage error (MAPE) is the most common measure used to forecast error, and works best if there are no extremes to the data (and no zeros).<p>",
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