{
  "fetched_at": "2026-10-19T23:10:32Z",
  "source_url": "https://www.kaggle.com/competitions/gdsc-vashisht-ml-hackathon-open-for-all/data",
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      "content": "\n## Files\n\n*   **X_train.csv** - the training set\n*   **y_train.csv** - the true labels of the training dataset\n*   **X_test.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format\n\n## X Columns\n\ndate:  The date the football match was played (format YYYY-MM-DD).\n\nhome_team:  The name of the team playing on their home field.\n\naway_team: The name of the team playing on the opposing team's field.\n\ntournament: The name of the tournament the match belongs to\n\ncity: The city where the match was played.\n\ncountry:  The country where the match was played.\n\nneutral:  This column likely indicates whether the match was played at a neutral venue (i.e., not at either team's home stadium).  (True/False)\n\n## Y Column\n\nresult: 0 for a Draw, 1 for a loss to the home team, 2 for a win to the home team",
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    {
      "name": "Evaluation",
      "content": "The Evaluation Metric is the **accuracy score** between the Predicted And True Values",
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    {
      "name": "rules",
      "content": " - Don't cheat!\n - Apply yourself!\n - Have fun!",
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    {
      "name": "abstract",
      "content": "Put your machine learning skills to the test in this exciting hackathon during our technical fest! We're challenging you to build predictive models for one of the world's most beloved sports - football.\n\n**The Challenge**\n\nAnalyze our comprehensive football dataset and develop a model that can accurately predict the outcomes of upcoming matches (win, draw, loss). Our dataset includes:\n\nTeam Statistics: Historical results\nMatch Conditions: Home/away factors\nResults: Home(Win,Draw,Loss)\n\n**What You'll Gain**\n\nReal-world machine learning experience\nProblem-solving and data analysis practice\nChance to win prizes\nBragging rights among your peers!\nEvaluation\n\n**We're looking for models that excel in:**\n\nAccuracy: The percentage of correctly predicted outcomes.\nUncertainty Handling: Can your model adapt to the unpredictability of football?\nEfficiency: Resourceful solutions are always a plus.\n\n**How to Get Started**\n\nForm Teams: Collaboration is encouraged! (Optional)\nGet the Dataset: \nBuild Your Model: Time to get creative!\nSubmit Predictions: Let's see how you stack up.\n\n**Timeline**\n\nDataset Release: 16th March 11 PM\nSubmission Deadline: 16th March 7 PM\nWinners Announced: 17th March 10 AM\nDon't miss out on this fantastic opportunity to showcase your ML skills!\n\n**GDSC IIITDM RESERVES ALL RIGHTS FOR THIS COMPETITION**",
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