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  "fetched_at": "2026-10-25T05:15:58Z",
  "source_url": "https://www.kaggle.com/competitions/grid-optimization-challenge-iiit-bhagalpur-enyugma/data",
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      "name": "data-description",
      "content": "You are provided with a dataset that comprises multiple attributes:\n•\ttau[x]: Reaction time of participants. Tau1 pertains to the electricity producer.\n•\tp[x]: Nominal power consumed (negative) or produced (positive). For consumers, the range is [-0.5,-2]. p1 is derived as p1=abs(p2+p3+p4).\n•\tg[x]: Coefficient (gamma) proportional to price elasticity. g1 pertains to the electricity producer.\n•\tstab: A real number indicating the maximal real part of the characteristic equation root. A positive 'stab' indicates the system is linearly unstable, whereas a negative value indicates it is stable.\n•\tstabf: A categorical variable indicating system stability: 'stable' or 'unstable'.\n\n## Files\n\n*   **train.csv** - the training set\n*   **test.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format",
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      "content": "You are provided with a dataset containing different attributes of a grid. You have to accomplish two tasks. In the first one you have to regress the value of our desired attribute that is \"stab\". Apart from that you have to optimize the grid by finding the most optimal value of \"p1\".",
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      "name": "Evaluation",
      "content": "**Tasks:**\n**Prediction Task**:\n\nGoal: Predict the 'stab' value using the provided attributes.\nEvaluation: Your predictions will be evaluated against actual values using the Mean Absolute Error (MAE) metric for the 'stab' value.\n\n**Optimization Task:**\n\nGoal: Determine and optimize the individual values for parameters \"p2\", \"p3\", and \"p4\" to enhance grid stability. While it is known that \n\np1=abs(p2+p3+p4), participants should optimize values for p2, p3, and p4 individually to arrive at the best configuration of p1, that improves system stability. The range for \"pi\" is [-2, -0.5] for all i in {2,3,4}.\nEvaluation: Your optimized value of \"p1\" will be assessed in two 2 stages-:\n\n**Verification that they adhere to the constraint \np1=abs(p2+p3+p4).**\n\n**Using the Mean Absolute Error (MAE) metric, your optimized values will be compared against a reference optimal result to gauge their efficiency in improving the 'stab' value and, consequently, system stability.**\n\nThe weightage of task-1 will be 30 points where as of task-2 it will be 70 points.\n\n## Submission File\n\n**If the \"stab\" column will be absent in your submission file, you will get an infinite value as an error, so make SURE TO PROPERLY FOLLOW THE FORMAT GIVEN BELOW**\n\nFor each ID in the test set, you must predict the value for the \"stab\" variable and optimal value of \"p1\". The file should contain a header and have the following format:\n\n    ID, stab, OPTIMAL VALUE\n    2,0,0\n    5,0,0\n    6,0,0\n    etc.",
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      "name": "rules",
      "content": "1) The public leaderboard only has 40% of the testing data and hence the final evaluation and ranks might differ.\n2) Top 15 candidates will be asked to push the codes on Github and provide us with the repository's link.\n3) The final leaderboard of top 10 participants will be decided after evaluating there codes, optimization technique/values (i.e value of p1).\n\n**4) You must Submit your email for further contact details or you will be disqualified.**\n\n**5) Make sure to register for \"Smart Grid\" on the following link or you will be disqualified-:** https://enyugma.iiitbh.ac.in/register\n\n6)Apart from that:\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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