地球资源数据云——数据资源详情
CS 学生表现数据集
该数据集《CS Students Performance Dataset》主要用于监督学习任务，数据形态以文本为主，应用场景偏向交通/汽车。 题目说明：A comprehensive dataset containing academic records, skills, and performance det 任务类型：文本监督学习。 建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：cs_students.csv。 Content This dataset contains structured information about Computer Science students, including their personal details, academic performance, technical skills, project experience, and career interests. It includes measurable data such as GPA, age, number of projects, and programming skills (Python, SQL, Java), along with categorical information like gender, major, and interested domain. Each row represents an individual student, and each column represents a specific attribute related to that student. Context The dataset is designed for educational and analytical purposes. It can be used to analyze student performance, evaluate skill levels, identify trends in career interests, and build predictive models in the field of education and data science.
摘要概览
该数据集《CS Students Performance Dataset》主要用于监督学习任务，数据形态以文本为主，应用场景偏向交通/汽车。 题目说明：A comprehensive dataset containing academic records, skills, and performance det
任务类型：文本监督学习。
建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：cs_students.csv。
Content
This dataset contains structured information about Computer Science students, including their personal details, academic performance, technical skills, project experience, and career interests.
It includes measurable data such as GPA, age, number of projects, and programming skills (Python, SQL, Java), along with categorical information like gender, major, and interested domain. Each row represents an individual student, and each column represents a specific attribute related to that student.
Context
The dataset is designed for educational and analytical purposes. It can be used to analyze student performance, evaluate skill levels, identify trends in career interests, and build predictive models in the field of education and data science.