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  "source_url": "https://www.kaggle.com/competitions/cuisine-classification-challenge/data",
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      "content": "The training dataset includes two files. \n\nThe train_label.csv file contains columns Recipe_ID and Cuisine. Recipe_ID is the ID of the recipe and Cuisine is the name of the regional cuisine to which the recipe belongs. Recipe_ID and Cuisine have one-to-one mapping. \n\nThe train_features.csv file contains columns Recipe_ID, Ingredient_ID, state, quantity, unit. Here Recipe_ID is the ID of the recipe and rest of the columns contain detailed information of the ingredients used in this recipe. Recipe_ID and other columns have one-to-many mappings. \n\nTest dataset includes test_features.csv file which contains the following columns: Recipe_ID, Ingredient_ID, state, quantity, unit. Here Recipe_ID is the ID of the recipe and rest of the columns contain detailed information of the ingredients used in this recipe. Recipe_ID and other columns have one-to-many mappings.\n \nYou are expected to generate a submission file with two columns: Recipe_ID and Cuisine. Here Recipe_ID should have all the unique values of recipe IDs in test dataset and Cuisine should have the predicted regional cuisine of the corresponding recipe.\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": "A cuisine is characterized by the distinct use of ingredient combinations and processing techniques in its recipes associated with a specific culture or geographic region. The ability to accurately classify a recipe into a cuisine would be valuable for a variety of culinary purposes including fusion recipe creation. \n\n##In this challenge\nYou will apply your machine learning skills to predict the cuisine category of a recipe. You will leverage a world-class dataset to build a machine learning model that gives the maximum accuracy with respect to the test set (visible and hidden). You are 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 statistics 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 recipe in the dataset, submission files should contain two columns: 'Recipe_ID' and 'Cuisine'. The file should contain a header and have the following format:\n```\nRecipe_ID,Cuisine\n2260,Indian Subcontinent\n```\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 4.\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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