{
  "fetched_at": "2026-10-19T19:05:27Z",
  "source_url": "https://www.kaggle.com/competitions/acme-stores-forecasting/data",
  "pages": [
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      "name": "rules",
      "content": "\r\n\t<!-- Begin Base Rules -->\r\n    <h3>One account per participant</h3>\r\n    <p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\r\n    <h3>No private sharing outside teams</h3>\r\n    <p>\r\n        Privately sharing code or data outside of teams is not permitted.\r\n        It's okay to share code if made available to all participants on the forums.\r\n    </p>\r\n    <h3>Team Mergers</h3>\r\n    <p>Team mergers are not allowed in this competition.</p>\r\n\r\n    <h3>Team Limits</h3>\r\n    <p>There is no maximum team size.</p>\r\n    <h3>Submission Limits</h3>\r\n    <p>You may submit a maximum of 2 entries per day.</p>\r\n    <p>You may select up to 2 final submissions for judging.</p>\r\n    <h3>Competition Timeline</h3>\r\n    <p>Start Date: <strong>TBA</strong></p>\r\n    <p>Merger Deadline: <strong>None</strong></p>\r\n    <p>Entry Deadline: <strong>None</strong></p>\r\n    <p>End Date: <strong>2/1/2014 12:00 AM UTC</strong></p>\r\n\t<!-- End Base Rules -->\r\n<p>The rules page is a bit special: it will auto-update the text based on the parameters of the competition. The rules you need to add here are any additional rules relating to dataset usage. You may also put grading information here.</p>",
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      "name": "data-description",
      "content": "<p>A retail firm “ACME Stores Corporation” would like to profile behavioral customer purchase information to understand the purchase basket size in USD (spend_amount) of several items across the product taxonomy of the company. The managers have collected a sample of various customers for high rotation articles over the last month. The dataset has been enriched with additional information coming from the company CRM. This data pertains to sociodemographics (age, gender, marital status, city class and place of residence duration), product details (product SKU and category) and spend_amount from last month. At this point in the project, they would want you to calibrate a model to forecast the spend amount of each customer across several products. They expect this special forces project to help them launch a marketing campaign of hypersegmented offerings for the customers of the company.</p>\n \n\n<p>Your model performance will be evaluated based on the predictive ability over the spend_amount variable in an external data set, which has been withheld from you by your evil manager (see picture below). Your job is to create a prediction archive according to the rules that will be given by the instructor in class. Forecasts will be measured against the root mean squared error metric (RMSE). Bear in mind that RMSE inflicts larger penalties on large deviations, so it is in your best interests to not fail spectacularly.\n </p>\n\n \n \n \n<u>Business Glossary</u>\n \n<ul>\n<li>Variable\nDescription</li>\n<li>Customer_ID\nUnique Customer identifier (anonymized)</li>\n<li>Product_SKU\nProduct stock keeping unit (anonymized)</li>\n<li>Gender\nSex of Customer under consideration.</li>\n<li>Age\nAge of customer, discretized in ranges.</li>\n<li>Job\nCustomer job (masked)</li>\n<li>City_Type\nType of the City (according to size)</li>\n<li>Residence_Duration\nNumber of years since they moved to their home location</li>\n<li>Marital_Status\nWhether the customer is married or not</li>\n<li>Main_Category\nProduct class within the company taxonomy (anonymized)</li>\n<li>Secondary_Category\nAdditional category the product may belong (anonymized)</li>\n<li>Tertiary_Category\nAdditional category the product may belong (anonymized)</li>\n<li>Spend_Amount\nUSD spent for this product by this customer (Outcome attribute to be forecasted)</li>\n </ul>\n \n",
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      "name": "Description",
      "content": "<p>A retail firm “ACME Stores Corporation” would like to profile behavioral customer purchase information to understand the purchase basket size in USD (spend_amount) of several items across the product taxonomy of the company. The managers have collected a sample of various customers for high rotation articles over the last month. The dataset has been enriched with additional information coming from the company CRM. This data pertains to sociodemographics (age, gender, marital status, city class and place of residence duration), product details (product SKU and category) and spend_amount from last month. At this point in the project, they would want you to calibrate a model to forecast the spend amount of each customer across several products. They expect this special forces project to help them launch a marketing campaign of hypersegmented offerings for the customers of the company.\n</p>\n<p>Your model performance will be evaluated based on the predictive ability over the spend_amount variable in an external data set, which has been withheld from you by your evil manager (see picture below). Your job is to create a prediction archive according to the rules that will be given by the instructor in class. Forecasts will be measured against the root mean squared error metric (RMSE). Bear in mind that RMSE inflicts larger penalties on large deviations, so it is in your best interests to not fail spectacularly.</p>",
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    {
      "name": "Evaluation",
      "content": "<p>The evaluation pages describes how submissions will be scored and how students should format their submissions.</p>\n<hr />\n<p>The evaluation metric for this competition is <a href=\"https://en.wikipedia.org/wiki/Root-mean-square_deviation\">Root Mean Squared Error</a>. This is a metric traditionally used for evaluation of regression problems, such as the one posted.",
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