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  "fetched_at": "2026-10-19T16:10:35Z",
  "source_url": "https://www.kaggle.com/competitions/CiVilium/data",
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      "name": "data-description",
      "content": "<p>You're given training.csv containing ~730000 data points, each representing a CiVilium exchange. You should use the time(Unix_Timestamp) and volume of an exchange (Volume_CiVilium) to predict the price of Civilium at that time. </p>\n\n<p>Use the model you developed to predict the prices</p>\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>Data fields</h2>\n<ul>\n<li><strong>Unix_Timestamp</strong> - Start time of time window (60s window), in Unix time</li>\n<li><strong>Volume_CiVilium</strong> - Volume of CiVilium transacted in this window, in Units (Uts)</li>\n<li><strong>Weighted_Price</strong> -  Volume Weighted Average Price, in Zibabwean Dollar (ZWD)</li>\n</ul>",
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      "name": "Description",
      "content": "<p>\nFor the longest time in the history of TechX, a mystical cryptocurrency, CiVilium (CiV), has been circulating amongst the Academic Leads. Although its price fluctuates wildly, we hope to discover an underlying pattern in its price change. </p>\n\nYour task is to develop a machine learning model that predicts Civilium's future price trends, thus giving you the power to manipulate CiVilium's market. Whoever gains a monopoly shall rule XAcademy.\n\n<h2>Dataset</h2>\nThe train.csv file given to you contains the weighted price and traded volume of CiVilium at ~730000 time stamps. You will be tested on test.csv which contains ~310000 data points \n\n<h2>Recommended libraries</h2>\nsklearn\nmatplotlib\n\n<h2>If you have completely no idea where to start, please see our starter notebook</h2>",
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      "name": "Evaluation",
      "content": "# RMSE loss:\n\nRoot-Mean-Square-Error or RMSE is one of the most popular measures to estimate the accuracy of our forecasting model’s predicted values versus the actual or observed values while training the regression models or time series models.\n\nIt measures the error in our predicted values when the target or response variable is a continuous number. For example, when using regression models to predict a quantity like income, sales value/volumes, demand volumes, scores, height or weight etc.\n\n![](https://miro.medium.com/max/548/0*IN6VnDGeafpUioDN)",
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    {
      "name": "rules",
      "content": " - Don't cheat!\n - Apply yourself!\n - Have fun!",
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      "name": "Prizes",
      "content": "## **Leaderboard Prizes:** \nAwarded on the basis of public leaderboard rank. \n    1st Place - 300 CiV\n    2nd Place - 200 CiV\n    3rd Place - 100 CiV\n\nAll successful submissions will be awarded 10 CiV. \n\nTo claim your price, \n1. Create your crypto wallet by adding the MetaMask extension to your chrome browser. \n2. After creating your account, switch your network from Ethereum Mainnet to Rinkeby Test Network. \n3. Click \"add your token\" and paste 0xc783bdc053696bcbd79dfbbc954f0100fb2dfed7.\n3. Private message chengyang your account ID for CiV. :)\n\n\n\n",
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