地球资源数据云——数据资源详情
音乐教育表现数据
该数据集《Music Education Performance Data》主要用于监督学习任务，数据形态以表格为主，应用场景偏向环保分类。 题目说明：student performance, physiological data, and engagement metrics for analysis 任务类型：表格监督学习。 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。 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：music_education_dataset.csv, music_education_dataset_new.csv。 This dataset was designed to evaluate the effectiveness of music education by collecting data on student performance, physiological information, engagement, and other metrics in a classroom setting, enhanced by Internet of Things (IoT) devices and Artificial Intelligence (AI) algorithms. The data simulates a learning environment where IoT devices track various music performance metrics, physiological responses, and behavioral patterns of students during music lessons. The dataset is structured to enable evaluation of the impact of music education, focusing on skill development, engagement, and performance outcomes. Features: Student Information: Student_ID: A unique identifier for each student (e.g., "S001", "S002"). Age: The age of the student (integer). Gender: The gender of the student, either "Male" or "Female". Class_Level: The level of music education (e.g., "Beginner", "Intermediate", "Advanced"). Music Performance Metrics:
摘要概览
该数据集《Music Education Performance Data》主要用于监督学习任务，数据形态以表格为主，应用场景偏向环保分类。 题目说明：student performance, physiological data, and engagement metrics for analysis
任务类型：表格监督学习。
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。
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：music_education_dataset.csv, music_education_dataset_new.csv。
This dataset was designed to evaluate the effectiveness of music education by collecting data on student performance, physiological information, engagement, and other metrics in a classroom setting, enhanced by Internet of Things (IoT) devices and Artificial Intelligence (AI) algorithms.
The data simulates a learning environment where IoT devices track various music performance metrics, physiological responses, and behavioral patterns of students during music lessons. The dataset is structured to enable evaluation of the impact of music education, focusing on skill development, engagement, and performance outcomes.
Features:
Student Information:
Student_ID: A unique identifier for each student (e.g., "S001", "S002"). Age: The age of the student (integer). Gender: The gender of the student, either "Male" or "Female". Class_Level: The level of music education (e.g., "Beginner", "Intermediate", "Advanced"). Music Performance Metrics: