{
  "fetched_at": "2026-10-19T20:58:44Z",
  "source_url": "https://www.kaggle.com/competitions/cal-dma-spring-19/data",
  "pages": [
    {
      "name": "rules",
      "content": "\n\n - You are allowed to work as a team of up to 4 for this competition.\n - Do not reuse or copy existing code; write your own code\n - Have fun!",
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      "content": "<p>The data provided consists of users and the product IDs of the products they purchased. There are some other features as well. For some of the products, you will see that the target (Product_Category_1) is marked as -1. Those are your test data, i.e. the Product IDs for which you need to predict the category.  Note that you will need to transform the data to a one-row per product form for this. </p>\n\n",
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    {
      "name": "Description",
      "content": "<p>Welcome to Lab 5 in INFO 154/254! In this lab, you will get a chance to use and apply all the concepts you have learnt so far. This is also a very good opportunity to practise problem solving in a competitive setting. You will also have a chance to experience first hand the problems data scientists can face while dealing with a dataset. </p>\n\n<p>The dataset we have chosen contains information on black friday sales. The original dataset can be found <a href=\"https://www.kaggle.com/mehdidag/black-friday\">here</a>. </p>\n\n<p>Your task is to predict the primary category (<b> Product_Category_1</b> ) based on other features. </p>\n\n<p>Feel free to play around with the data as much as you want. Explore it. Create new features. Drop unnecessary features. Slice and dice, aggregate, drill up and drill down. </p>\n\n<p>For this lab, you need to submit your code via Kaggle as well as bCourses. There should be only one submission per team (on both Kaggle and bCourses). The deliverables include:</p>\n\n 1. **Write-up**: A brief description of the approach you used to solve the problems. Submit this on bCourses. Be brief but include all that is important. What was the data like? How did you explore it? How did you transform it? Which models did you use and why? What were your results? Make sure to include your team members' names as well as your Kaggle team name. You can find a template linked on bCourses.\n 2. **Code**: Include any notebooks or scripts you used in your solution. Submit as a zipped file on bCourses.\n 3. **Kaggle Submission**: Submit your results to Kaggle for automatic scoring. \n\nNote that this lab is worth twice as much as the regular labs. You have the opportunity to score 20 points instead of 10. \n",
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    {
      "name": "Evaluation",
      "content": "<p>Your task is to predict the primary product category given other features of the product. You may also create your own features. </p>\n<hr />\n<p>The evaluation metric for this competition is Accuracy.  </p>\n<p>While you are working on the problem, you should also check the precision and recall of your models. However, this is for your learning, and will not be considered in the evaluation.</p>\n<h2>Submission Format</h2>\n<p><strong>The solution file will be a CSV file consisting of Product_ID and your predicted class. It should contain two columns: Product_ID and Product_Category_1.</p>\n",
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