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  "source_url": "https://www.kaggle.com/competitions/dsr-b29-minicomp/data",
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      "content": "This mini competition is adapted from the Kaggle Rossman challenge.  Please refrain from looking at the challenge on Kaggle until after you have finished - this will allow you to get a true measurement of where you are at as a data scientist.\n\n## Files\n\n*   **train.csv** - the training set\n*   **store.csv** - gives more information about the store \n*   **sample_submission.csv** - a sample submission file in the correct format\n*   **metaData.csv** - supplemental information about the data\n\n## Columns\n\n* Id - an Id that represents a (Store, Date) duple within the test set\n\n* Store - a unique Id for each store\n\n* Sales - the turnover for any given day (this is what you are predicting)\n\n* Customers - the number of customers on a given day\n\n* Open - an indicator for whether the store was open: 0 = closed, 1 = open\n\n* StateHoliday - indicates a state holiday. Normally all stores, with few exceptions, are closed on state holidays. Note that all schools are closed on public holidays and weekends. a = public holiday, b = Easter holiday, c = Christmas, 0 = None\n\n* SchoolHoliday - indicates if the (Store, Date) was affected by the closure of public schools\n\n* StoreType - differentiates between 4 different store models: a, b, c, d\n\n* Assortment - describes an assortment level: a = basic, b = extra, c = extended\n\n* CompetitionDistance - distance in meters to the nearest competitor store\n\n* CompetitionOpenSince[Month/Year] - gives the approximate year and month of the time the nearest competitor was opened\n\n* Promo - indicates whether a store is running a promo on that day\n\nPromo2 - Promo2 is a continuing and consecutive promotion for some stores: 0 = store is not participating, 1 = store is participating\n\nPromo2Since[Year/Week] - describes the year and calendar week when the store started participating in Promo2\nPromoInterval - describes the consecutive intervals Promo2 is started, naming the months the promotion is started anew. E.g. \"Feb,May,Aug,Nov\" means each round starts in February, May, August, November of any given year for that store.",
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      "name": "Description",
      "content": "This mini competition is adapted from the Kaggle Rossman challenge.  Please refrain from looking at the challenge on Kaggle until after you have finished - this will allow you to get a true measurement of where you are at as a data scientist.\n\nHere is a handy link to Kaggle's [competition documentation](https://www.kaggle.com/docs/competitions), which includes, among other things, instructions on [submitting predictions](https://www.kaggle.com/docs/competitions#making-a-submission).",
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      "name": "Evaluation",
      "content": "Submissions are evaluated on the Root Mean Square Percentage Error (RMSPE). The RMSPE is calculated as\n\n---\n\\textrm{RMSPE} = \\sqrt{\\frac{1}{n} \\sum_{i=1}^{n} \\left(\\frac{y_i - \\hat{y}_i}{y_i}\\right)^2},\n\nwhere y_i denotes the sales of a single store on a single day and yhat_i denotes the corresponding prediction. Any day and store with 0 sales is ignored in scoring.\n\n##  Submission File\nThe file should contain a header and have the following format:\n\n----\n`Id,Sales\n1,0\n2,0\n3,0\netc.`\n",
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      "name": "rules",
      "content": "### Submission Limits\nYou may submit a maximum of 5 entries per day.\n\nYou may select up to 2 final submissions for judging.\n\n### Competition Timeline\nStart Date: 08/02/2021\n\nEnd Date: 10/02/2021",
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