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  "source_url": "https://www.kaggle.com/competitions/ML-Olympiad-can-you-guess-the-beer-style/data",
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      "content": "##**This competition was cancelled due to a bug in the dataset.**\n\nPlease register for this competition\n[https://www.kaggle.com/competitions/ml-olympiad-can-you-guess-beer-style/\n](url)\n\n## Dataset Information\n\nThis dataset contains tasting profiles and consumer reviews for 403 unique beers from 281 different breweries. It was created by integrating information from two existing data sets on Kaggle: Beer Tasting Profiles Dataset and 1.5 Million Beer Reviews.\n\nThe training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each beer. Your model will be based on beer's features. You can also use feature engineering to create new features.\n\nThe test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each beer. It is your job to predict these outcomes. For each beer in the test set, use the model you trained to predict its style.\n\nWe also include a sample submission file named sample_submission.csv, a set of predictions as an example of what a submission file should look like.\n\n## Files\n\n*   **train.csv** - the dataset used to train model.\n*   **test.csv** - the dataset used to evaluate the model.\n*   **sample_submission.csv** - a sample submission file in the correct format.\n\n## Columns\n\n* ID: A unique ID that identifies the beer\n* Name:  Beer name\n* Brewery:  Brewery name\n* Beer Name (Full): Complete beer name (Brewery + Brew Name) \n* Description: Notes on the beer if available\n* ABV: Alcohol content of beer (% by volume)\n* Min IBU: The minimum IBU value each beer can possess\n* Max IBU: The maximum IBU value each beer can possess\n\n* The next eleven columns represent taste and aroma descriptors taken from up to 25 reviews of each beer.\n* Astringency\n* Body\n* Alcohol\n* Bitter\n* Sweet\n* Sour\n* Salty\n* Fruits\n* Hoppy\n* Spices\n* Malty\n\nThe last six columns refer to information regarding beer reviews:\n\n* review _ aroma: The average rating for aroma\n* review _ appearance: The average rating for appearance\n* review _ palate: The average rating for mouthfeel\n* review _ taste: The average rating for taste\n* review _ overall: The overall average rating of the beer\n* number _ of _ reviews: The number of reviews taken into consideration",
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      "content": "##**This competition was cancelled due to a bug in the dataset.**\n\nPlease register for this competition\n[https://www.kaggle.com/competitions/ml-olympiad-can-you-guess-beer-style/\n](url)\n\n## What is ML Olympiad?\n\nAn associated Kaggle Community Competitions hosted by Google ML communities, supported by Google Developers. (ML GDEs, TFUGs, or 3rd party ML communities host ML community competitions on Kaggleand ML Developer Programs team sends rewards to hosts of registered competitions.)\n\n## ML Competition\n\nThis is a machine learning competition focused on classifying beer into 17 distinct styles based on key descriptors. Participants will be provided with a dataset containing information such as IBU (International Bitterness Units), ABV (Alcohol By Volume), as well as professional ratings and descriptions. The goal is to develop a model that can accurately categorize beer into one of the 17 styles, based on the provided descriptors. The competition is open to all machine learning enthusiasts, and offers a unique opportunity to put your skills to the test and showcase your ability to tackle a complex classification problem.\n\n\n## Acknowledgements\n\nThe dataset used in this competition is based on the dataset from [Beer Profile And Rating Datsets](https://www.kaggle.com/datasets/ruthgn/beer-profile-and-ratings-data-set)\n\nWe would like to thank Google for the invite and the support provided while hosting and promoting this competition\n\n #MLOlympiad #AppliedML",
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      "name": "Evaluation",
      "content": "## Goal\nPredict beer style.\n\n## Metric\nF-Score (Macro)\n\n## Submission File Format\nYou should submit a csv file with exactly 58 entries plus a header row. Your submission will show an error if you have extra columns or rows.\n\nThe file should have exactly 2 columns:\n\n- ID (beer id)\n- Style_id (beer style)",
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      "name": "rules",
      "content": "# One account per team\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n\n# No private sharing outside teams\nPrivately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all participants on the forums.\n\n# Team Limits\nThe maximum team size is 3.\n\n# Submission Limits\nYou may submit a maximum of 5 entries per day.\nYou may select up to 2 final submissions for judging.\n\n# Competition Timeline\nStart Date: 02/07/2023 UTC 23:59\nEnd Date: 03/10/2023 UTC 23:59",
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