地球资源数据云——数据资源详情
学生心理健康分析
该数据集《Student Mental Health 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：Student Mental Health Analysis during Online Learning 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。 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：Student Mental Health Analysis During Online Learning.csv。 This dataset contains responses from students regarding their mental health status during the era of online learning. The data was collected via surveys and focuses on various psychological and behavioral aspects influenced by remote education. The dataset can be used for exploratory data analysis (EDA), data visualization, and predictive modeling to better understand how online education impacts student mental well - being. The dataset consists of 1,000 entries and 10 columns, covering demographic details, lifestyle habits, and self - reported mental health indicators. Here's a summary of the columns and their purposes: | Name Student's first name (non - essential for analysis; can be anonymized) | Gender Gender of the respondent (Male/Female)
摘要概览
该数据集《Student Mental Health 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：Student Mental Health Analysis during Online Learning
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。
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：Student Mental Health Analysis During Online Learning.csv。
This dataset contains responses from students regarding their mental health status during the era of online learning. The data was collected via surveys and focuses on various psychological and behavioral aspects influenced by remote education.
The dataset can be used for exploratory data analysis (EDA), data visualization, and predictive modeling to better understand how online education impacts student mental well - being.
The dataset consists of 1,000 entries and 10 columns, covering demographic details, lifestyle habits, and self - reported mental health indicators. Here's a summary of the columns and their purposes:
| Name Student's first name (non - essential for analysis; can be anonymized)
| Gender Gender of the respondent (Male/Female)