# Bosscoder Revised — Data

来源：https://www.kaggle.com/competitions/bosscoder-revised/data

采集时间：2026-10-20T05:16:56Z

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.

**Files**
**train.csv** - The training dataset; Target is the categorical target.
**test.csv** - The test dataset; your objective is to predict the class of Target for each row.
**sample_submission.csv** - A sample submission file in the correct format.

**Dataset Description**
Basic Information:
Number of entries: 3,096
Number of columns: 38
Column Descriptions:
Marital status: Categorical (encoded as integers)
Application mode: Categorical (encoded as integers)
Application order: Numerical
Course: Categorical (encoded as integers)
Daytime/evening attendance: Categorical (encoded as integers)
Previous qualification: Categorical (encoded as integers)
Previous qualification (grade): Numerical (float)
Nationality: Categorical (encoded as integers)
Mother's qualification: Categorical (encoded as integers)
Father's qualification: Categorical (encoded as integers)
Mother's occupation: Categorical (encoded as integers)
Father's occupation: Categorical (encoded as integers)
Admission grade: Numerical (float)
Displaced: Binary (encoded as integers)
Educational special needs: Binary (encoded as integers)
Debtor: Binary (encoded as integers)
Tuition fees up to date: Binary (encoded as integers)
Gender: Binary (encoded as integers)
Scholarship holder: Binary (encoded as integers)
Age at enrollment: Numerical (integer)
International: Binary (encoded as integers)
Curricular units 1st sem (credited): Numerical (integer)
Curricular units 1st sem (enrolled): Numerical (integer)
Curricular units 1st sem (evaluations): Numerical (integer)
Curricular units 1st sem (approved): Numerical (integer)
Curricular units 1st sem (grade): Numerical (float)
Curricular units 1st sem (without evaluations): Numerical (integer)
Curricular units 2nd sem (credited): Numerical (integer)
Curricular units 2nd sem (enrolled): Numerical (integer)
Curricular units 2nd sem (evaluations): Numerical (integer)
Curricular units 2nd sem (approved): Numerical (integer)
Curricular units 2nd sem (grade): Numerical (float)
Curricular units 2nd sem (without evaluations): Numerical (integer)
Unemployment rate: Numerical (float)
Inflation rate: Numerical (float)
GDP: Numerical (float)
id: Numerical (integer, unique identifier)