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  "fetched_at": "2026-10-20T06:59:47Z",
  "source_url": "https://www.kaggle.com/competitions/datahunt-svc/data",
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      "name": "data-description",
      "content": "## Files\n\n*   **train.xlsx** - the training set\n*   **test.xlsx** - the test set\n*   **sample_output.xlsx** - a sample submission file in the correct format",
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      "name": "rules",
      "content": "- It is mandatory to fill out this form: https://forms.gle/t1Pn3B8xPxCKeDYx8\n- This competition is open only to students from the University of Delhi Undergraduate students.\n- If any team member is not a student of University of Delhi, the team will be disqualified. \n- Participants are allowed to register in teams of 3-4 members\n- Cross-college teams are allowed\n- Participants can use Python/ MS Excel/ R to participate (using google colab is encouraged for students who do not wish to download the software)\n- Top 5 teams will be given 2 days prepare a presentation and discuss their solution with the panel of judges\n- The selected teams will have to provide their code/ excel workbook to verify the authenticity of the solution (negative points for copied solutions)",
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      "name": "Description",
      "content": "**Organized by the Department of Statistics, Sri Venkateswara College, University of Delhi**\n\nAll you need to know about Analytics Hackathon\n\nBy 2025, 200+ zettabytes of data will be in cloud storage around the globe. There is a shortage of skilled professionals who can analyze and interpret data to generate insights that drive businesses and bring about a pos society. To introduce students to real-life problems, which require a practical application of classroom learnings, we bring you the Analytics Hackathon.\n\n## Objective: Prediction of attrition at ABC Inc. to help the leadership plan department-wise recruitment strategies . \n\n### About the data\n\nGiven employee data is for ABC Inc. Information regarding each employee along with unique employee id has been provided. The Attrition column will help identity employee retained.  \n\n<hr/>\n\n### Evaluation\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 employee in the dataset**, submission files should contain two columns: `EmployeeNumber` and `Attrition`.\n\nThe file should contain a header and have the following format:\n```\nEmployeeNumber, Attrition\n    2322,           Yes\n```\n\nIn case of any dispute, the decision of the organizers shall be binding",
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