{
  "fetched_at": "2026-10-20T08:22:07Z",
  "source_url": "https://www.kaggle.com/competitions/execomdatathon/data",
  "pages": [
    {
      "name": "data-description",
      "content": "## Files\n\n*   **train.csv** - the training set\n*   **test.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format\n\n## Columns\n\n- date : Date in MM-DD-YYYY\n- day : Day of the Week\n- quarter : A portion of the month. A month was divided into four quarters\n- department : Associated department with the instance\n- team_no : Associated team number with the instance\n- no_of_workers : Number of workers in each team\n- no_of_style_change : Number of changes in the style of a particular product\n- targeted_productivity : Targeted productivity set by the Authority for each team for each day.\n- smv : Standard Minute Value, it is the allocated time for a task\n- wip : Work in progress. Includes the number of unfinished items for products\n- over_time : Represents the amount of overtime by each team in minutes\n- incentive : Represents the amount of financial incentive (in BDT) that enables or motivates a particular course of action.\n- idle_time : The amount of time when the production was interrupted due to several reasons\n- idle_men : The number of workers who were idle due to production interruption\n- **actual_productivity : The actual productivity that was delivered by the workers. It has to be categorized into three classes: 0 (low productivity), 1 (moderate productivity), 2 (high productivity).**\n\n**All the best!**\n### May the Accuracy be with You!\n![check] (https://drivendata-public-assets.s3.amazonaws.com/go-forth-and-model.jpg)\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "Welcome to the \"Mini-Datathon\"! Gear yourselves up for an exciting real-world application challenge that we have in store for you. This is where you will have to give your brain a workout and use your skills, to make a Classification Model for the given situation!\n\n## **About the data**\nThe apparel industry houses a huge amount and variety of data. At every step of the supply\nchain, data is collected and stored by each supply chain actor. This data, when used intelligently,\ncan help with solving a good deal of problems for the industry.\n\nData mining and machine learning have been at the forefront of research, helping to solve\nanalytical problems and overcoming business problems. The power of data mining in analyzing big data has been proven in various studies.\n\nThis dataset includes various attributes of the apparel industry which may include the logistics of the department, incentives, amount of workers, time given for a target etc. The dataset comprises of over 1000 instances which need to be analyzed and used to **predict the productivity performance of the teams.**\n\n## **Tasks**\n\n**(a) Convert the target column i.e. `actual_productivity` column into three classes (0, 1, 2) using the given thresholds:**\n- 0 (low productivity) for `actual_productivity` <= 0.5\n- 1 (moderate productivity) for 0.5 < `actual_productivity` <= 0.8\n- 2 (high productivity) for `actual_productivity` > 0.8\n\n**(b) Create a classification model to classify productivity performance into defined classes (0, 1, 2).**\n\n### Now that you have come this far, head to the data section and show us what you got!\n![check](https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSKRx5vM1A-WYg8cxu0FFYOU6HKOcHE7YRmww&usqp=CAU)\n ",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "![check](https://media.makeameme.org/created/evaluate-form-an.jpg)\n\nThe evaluation pages describes how submissions will be scored and how students should format their submissions. You don't need a sub-title at the top; the page title appears above. For an example of a high quality evaluation page, see the 2013 KDD Cup page, which we have included below:\n\n---\n\nThe evaluation metric for this competition is [Mean F1-Score](https://en.wikipedia.org/wiki/F-score). The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision \\( \\text{p} \\) and recall \\( \\text{r} \\).\n\nPrecision is the ratio of true positives \\(\\text{tp}\\) to all predicted positives \\(\\text{tp} + \\text{fp}\\). Recall is the ratio of true positives \\(\\text{tp}\\) to all actual positives \\(\\text{tp} + \\text{fn}\\). The F1 score is given by:\n\n\\[ \\text{F1} = 2\\frac{\\text{p} \\cdot \\text{r}}{\\text{p}+\\text{r}}\\ \\ \\mathrm{where}\\ \\ \\text{p} = \\frac{\\text{tp}}{\\text{tp}+\\text{fp}},\\ \\ \\text{r} = \\frac{\\text{tp}}{\\text{tp}+\\text{fn}} \\]\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\n**For every author in the dataset**, submission files should contain two columns: `AuthorId` and `DuplicateAuthorIds`. `DuplicateAuthorIds` should be a space-delimited list. Every `AuthorId` counts as its own duplicate, and every duplicate should be listed under each of its respective ids. For example, if you suspect author A, B, and C are the same, you should list `A,A B C`, `B,B A C`, `C,C A B`.\n\n\nThe file should contain a header and have the following format:\n```\nAuthorId,DuplicateAuthorIds\n1,1\n```",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "rules",
      "content": " - Don't cheat!\n - Apply yourself!\n - Have fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}