{
  "fetched_at": "2026-10-20T08:15:12Z",
  "source_url": "https://www.kaggle.com/competitions/datathon22/data",
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    {
      "name": "data-description",
      "content": "Files:\ntrain.csv- the training set\n\ntest.csv- the testing set\n\nsample_submission-  a sample submission file in the correct format\n\n\nColumns:\nDate: in dd/mm/yy\n\nday: day of the week\n\nquarter:  A portion of the month. The month is divided into 4 parts\n\ndepartment: associated dept. with instance\n\nteam: associated team number with instance\n\nno_of_workers: associated number of workers in each team no_of_style_change: no. of changes of style of a particular \nproduct\n\ntargeted_productivity: target productivity set by the authority\n\nsmv: standard minute value, alloted for a specific task\n\nwip: work in progress, includes number of unfinished products\n\n over_time: represents the amount of overtime in minutes\n\nincentive: represents the amount of financial incentive(in BDT) that motivates course of action\n\nidle_time: represents the amount of time when the production was interrupted\n\nidle_men: number of workers that were idle due to production intteruption\n\nactual_productivity : The actual % of productivity that was delivered by the workers. It ranges from 0-1\n\n\n\n",
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    {
      "name": "Description",
      "content": "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\nData Set Information:\nThe Garment Industry is one of the key examples of the industrial globalization of this modern era. It is a highly labour-intensive industry with lots of manual processes. Satisfying the huge global demand for garment products is mostly dependent on the production and delivery performance of the employees in the garment manufacturing companies. So, it is highly desirable among the decision makers in the garments industry to track, analyse and predict the productivity performance of the working teams in their factories. \n\nTasks:\n(a) Predict the target column i.e. actual_productivity between the range of 0-1\n",
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    {
      "name": "Evaluation",
      "content": "R2 SCORE:\nCoefficient of determination also called as R2 score is used to evaluate the performance of a linear regression model. It is the amount of the variation in the output dependent attribute which is predictable from the input independent variable(s). It is used to check how well-observed results are reproduced by the model, depending on the ratio of total deviation of results described by the model.",
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    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun and try your best!",
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