{
  "fetched_at": "2026-10-20T05:16:56Z",
  "source_url": "https://www.kaggle.com/competitions/bosscoder-revised/data",
  "pages": [
    {
      "name": "data-description",
      "content": "The dataset comprises information from multiple databases within a higher education institution, detailing undergraduate students across various disciplines like agronomy, design, education, nursing, journalism, management, social service, and technologies. It includes data captured at enrollment, encompassing academic history, demographic details, and socio-economic factors, alongside academic performance indicators at the end of the first and second semesters. The objective is to develop classification models aimed at predicting student outcomes, specifically dropout rates and academic success, formulated as a three-category classification problem with significant class imbalance.\n\n**Files**\n**train.csv** - The training dataset; Target is the categorical target.\n**test.csv** - The test dataset; your objective is to predict the class of Target for each row.\n**sample_submission.csv** - A sample submission file in the correct format.\n\n**Dataset Description**\nBasic Information:\nNumber of entries: 3,096\nNumber of columns: 38\nColumn Descriptions:\nMarital status: Categorical (encoded as integers)\nApplication mode: Categorical (encoded as integers)\nApplication order: Numerical\nCourse: Categorical (encoded as integers)\nDaytime/evening attendance: Categorical (encoded as integers)\nPrevious qualification: Categorical (encoded as integers)\nPrevious qualification (grade): Numerical (float)\nNationality: Categorical (encoded as integers)\nMother's qualification: Categorical (encoded as integers)\nFather's qualification: Categorical (encoded as integers)\nMother's occupation: Categorical (encoded as integers)\nFather's occupation: Categorical (encoded as integers)\nAdmission grade: Numerical (float)\nDisplaced: Binary (encoded as integers)\nEducational special needs: Binary (encoded as integers)\nDebtor: Binary (encoded as integers)\nTuition fees up to date: Binary (encoded as integers)\nGender: Binary (encoded as integers)\nScholarship holder: Binary (encoded as integers)\nAge at enrollment: Numerical (integer)\nInternational: Binary (encoded as integers)\nCurricular units 1st sem (credited): Numerical (integer)\nCurricular units 1st sem (enrolled): Numerical (integer)\nCurricular units 1st sem (evaluations): Numerical (integer)\nCurricular units 1st sem (approved): Numerical (integer)\nCurricular units 1st sem (grade): Numerical (float)\nCurricular units 1st sem (without evaluations): Numerical (integer)\nCurricular units 2nd sem (credited): Numerical (integer)\nCurricular units 2nd sem (enrolled): Numerical (integer)\nCurricular units 2nd sem (evaluations): Numerical (integer)\nCurricular units 2nd sem (approved): Numerical (integer)\nCurricular units 2nd sem (grade): Numerical (float)\nCurricular units 2nd sem (without evaluations): Numerical (integer)\nUnemployment rate: Numerical (float)\nInflation rate: Numerical (float)\nGDP: Numerical (float)\nid: Numerical (integer, unique identifier)\n\n",
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    {
      "name": "Evaluation",
      "content": "Submissions are evaluated using the Accuracy score.",
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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",
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    },
    {
      "name": "abstract",
      "content": "Welcome to the Bosscoder Academy Data Science! Continuing in the tradition of our esteemed academy, we're excited to present engaging datasets that allow our community to hone their machine learning prowess. Anticipate a monthly competition designed to challenge and inspire, fostering skill development in a supportive environment. Join us as we embark on a journey of exploration and learning through data science excellence!\n**Your Goal:** The goal of this competition is to predict the academic risk of students in higher education.",
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}