地球资源数据云——数据资源详情
辍学与成功：学生数据分析
该数据集《Dropout and Success: Student Data Analysis》主要用于二分类任务，数据形态以表格为主，应用场景偏向医疗健康。 题目说明：Exploring the Impact of Dropout Rates on Student Success 任务类型：表格二分类。 建议流程：先做缺失值/异常值处理与特征编码，再比较逻辑回归、随机森林、XGBoost。 注意事项：疑似存在类别不均衡，建议使用分层抽样、类别权重与 F1/Recall 指标。 可用文件：student_data.csv。 Summary dataset created from a higher education institution (acquired from several disjoint databases) related to students enrolled in different undergraduate degrees, such as agronomy, design, education, nursing, journalism, management, social service, and technologies. The dataset includes information known at the time of student enrollment (academic path, demographics, and social - economic factors) and the students' academic performance at the end of the first and second semesters. The data is used to build classification models to predict students' dropout and academic sucess. The problem is formulated as a three category classification task, in which there is a strong imbalance towards one of the classes. Introduction This dataset delves into the correlation between dropout rates and student success in various educational settings. It includes comprehensive information on student demographics, academic performance, and factors contributing to dropout incidents.
摘要概览
该数据集《Dropout and Success: Student Data Analysis》主要用于二分类任务，数据形态以表格为主，应用场景偏向医疗健康。 题目说明：Exploring the Impact of Dropout Rates on Student Success
任务类型：表格二分类。
建议流程：先做缺失值/异常值处理与特征编码，再比较逻辑回归、随机森林、XGBoost。
注意事项：疑似存在类别不均衡，建议使用分层抽样、类别权重与 F1/Recall 指标。
可用文件：student_data.csv。
Summary
dataset created from a higher education institution (acquired from several disjoint databases) related to students enrolled in different undergraduate degrees, such as agronomy, design, education, nursing, journalism, management, social service, and technologies.
The dataset includes information known at the time of student enrollment (academic path, demographics, and social - economic factors) and the students' academic performance at the end of the first and second semesters. The data is used to build classification models to predict students' dropout and academic sucess.
The problem is formulated as a three category classification task, in which there is a strong imbalance towards one of the classes.
Introduction This dataset delves into the correlation between dropout rates and student success in various educational settings. It includes comprehensive information on student demographics, academic performance, and factors contributing to dropout incidents.