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  "source_url": "https://www.kaggle.com/competitions/bitter-or-not/data",
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      "content": "You are provided with data about the taste of different molecules divided into bitter, or not. The task is to design a robust Machine-Learning Model which can learn from this data and predict whether a given molecule is bitter or not.\n\n## Files\n\n*   **train.tsv** - the training set\n*   **test.tsv** - the test set \n*   **sample_submission.csv** - a sample submission file in the correct format\n\n\n## Columns\n\n*   `ID` - Unique identifier assigned to a compound\n*   `SMILES` - Structure of molecule in SMILES format. SMILES (Simplified Molecular Input Line Entry System) is a chemical notation that allows a user to represent a chemical structure in a way the computer can use. SMILES is a quickly learned and flexible notation.\n*   `Canonical SMILES` -  Canonical SMILES gives a single ‘canonical’ form for any particular molecule.\n*   `Bitter` - Contains binary values (True ,False) to tell if the molecule is bitter or not. \n\n## Data Source\nThis dataset is part of the Complex Systems Laboratory's (IIIT-Delhi) Computational Gastronomy repository. URL: https://cosylab.iiitd.edu.in/",
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      "name": "Description",
      "content": "The taste of an ingredient is experienced by virtue of molecules in it that interact with the taste receptors on the tongue. Bitterness is one of the major tastes to which humans have evolved to be averse. Finding ways of predicting whether a given molecule is 'bitter or not' is an important challenge in computational chemistry (chemoinformatics). \n\n##Challenge\nIn this challenge, you'll apply your machine learning skills to predict whether a compound will be bitter or not. \nYou will leverage a world-class data set to build a machine learning model that challenges the current model in production. Training, validation, and testing datasets include molecular structure in SMILES format and their original taste labels. You're free to explore any technique to create the most powerful model, from creating features to using the data more organically within a model.\n\n## Acknowledgements\nThis dataset is part of the Complex Systems Laboratory's (IIIT-Delhi) Computational Gastronomy repository. URL: https://cosylab.iiitd.edu.in/",
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      "name": "Evaluation",
      "content": "The evaluation metric for this competition is [F1-Score](https://en.wikipedia.org/wiki/F-score). The F1 score, commonly used in information retrieval, measures accuracy using the statistic of precision and recall.\n\nThe F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.\n\n## Submission Format\n\nFor every molecule/compound in the dataset, submission files should contain two columns: 'ID' and 'Bitter'. The file should contain a header and have the following format:\n```\nID,Bitter\n1,True \n```",
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      "name": "rules",
      "content": "## One account per participant\n\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n\n## No private sharing outside teams.\n\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\n\nThe maximum team size is 3.\n\n## Submission Limits\n\nYou may submit a maximum of **3 entries** per day.\nYou may select up to **2 final submissions** for judging.\n\n## Data Access and Use. \n\n**Competition Use only:** You may access and use the Competition Data only for participating in the Competition and on Kaggle.com forums. The Competition Sponsor reserves the right to disqualify any participant who uses the Competition Data other than as permitted by the Competition Website and these Rules. ",
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