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  "fetched_at": "2026-10-19T23:33:14Z",
  "source_url": "https://www.kaggle.com/competitions/campus-recruitment-prediction/data",
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      "name": "data-description",
      "content": "# Overview\nThis data set consists of placement data of students in an MR campus. It includes secondary and higher secondary school percentage and specialization. It also includes degree specialization, type and work experience, and salary offers to the placed students.\n\n\n## Files\nThere are 4 files that will be included, which are:\n* Competition Notebook Template: `Campus Recruitment Prediction Competition.ipynb`. **Use this template to build your models.** Also, you can find it under **Code** tap.\n*  Train dataset file: `train.csv`\n*  Test dataset file: `test.csv`\n*  Submission sample file: `submission_sample.csv`, you have to submit along with your **Notebook** a **CSV** file that contains the `sl_no` and `status` of the best model you built with the highest accuracy.\n\n\n## Data Dictionary\n| Variable | Definition |\n| --- | --- |\n| sl_no | Serial Number |\n| gender | Gender |\n| ssc_p | Secondary Education percentage |\n| ssc_b | Board of Education |\n| hsc_p | Higher Secondary Education percentage |\n| hsc_b | Board of Education |\n| degree_p | Degree Percentage | \n| workex | Work Experience |\n| etest_p | Employability test percentage | \n| specialisation | Post Graduation (MBA) |\n| mba_p | MBA percentage | \n| hsc_s_freq | Specialization in Higher Secondary Education |\n| degree_t_freq | Under Graduation (Degree type) |\n| status | Status of placement |\n",
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    {
      "name": "Description",
      "content": "# Hello AI Bootcampers 🙋🤍 Welcome to the evaluation of the Machine Learning Track🤍\n\n## Campus Recruitment:\nA campus placement program is a program that provides jobs to students nearing the end of their studies at universities or other educational institutions. This type of program involves educational institutions partnering with corporations that want to recruit students.\n\n## Goal of Competition:\nThe main objective is to build a Machine Learning model that predicts whether students will have jobs after graduation or not.\n\n",
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      "name": "Evaluation",
      "content": "**This competition evaluates submissions based on the [Accuracy] evaluation metric. In addition, precision and recall should be considered. Precision is the ratio of true positives to all predicted positives. The recall is the ratio of true positives to all positives.**\n\n## Submission Format\nIt is recommended that each group submit one Notebook in `.ipynb` format along with a CSV file that contains the `sl_no` and `status` of the best model you built **that achieved the highest accuracy**. You can resubmit it if necessary, but **please delete the old version.**",
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      "name": "rules",
      "content": "##Team Limits\nThe maximum team size is 3 to 4.\n\n##Competition Timeline\n**Start Date: Dec 24, 2022 || 10:00**\n\n**End Date (Final Submission Deadline): Dec 24, 2022 || 14:00**\n\n##General Rules\n- **You must use the competition notebook template❗️**, you can find it under the **Data** and **Code** tap.\n- **You must use the same random state value in all the functions that require it, otherwise,  you will not be able to compete in the leaderboard❗️** \n- Apply yourself 👩‍💻👨‍💻!\n- Have fun 🤩 !",
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