地球资源数据云——数据资源详情
学生习惯与学业表现
该数据集《Student Habits vs Academic Performance》主要用于多分类任务，数据形态以表格为主，应用场景偏向医疗健康。 题目说明：Great for regression, classification, EDA, visualization, and even ML practice. 任务类型：表格多分类。 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_habits_performance.csv。 This is a simulated dataset exploring how lifestyle habits affect academic performance in students. With 1,000 synthetic student records and 15+ features including study hours, sleep patterns, social media usage, diet quality, mental health, and final exam scores, it’s perfect for ML projects, regression analysis, clustering, and data viz. Created using realistic patterns for educational practice. Ever wondered how much Netflix, sleep, or TikTok scrolling affects your grades? This dataset simulates 1,000 students' daily habits—from study time to mental health—and compares them to final exam scores. It's like spying on your GPA through the lens of lifestyle. Perfect for EDA, ML practice, or just vibing with data while pretending to be productive.
摘要概览
该数据集《Student Habits vs Academic Performance》主要用于多分类任务，数据形态以表格为主，应用场景偏向医疗健康。 题目说明：Great for regression, classification, EDA, visualization, and even ML practice.
任务类型：表格多分类。
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_habits_performance.csv。
This is a simulated dataset exploring how lifestyle habits affect academic performance in students. With 1,000 synthetic student records and 15+ features including study hours, sleep patterns, social media usage, diet quality, mental health, and final exam scores, it’s perfect for ML projects, regression analysis, clustering, and data viz.
Created using realistic patterns for educational practice.
Ever wondered how much Netflix, sleep, or TikTok scrolling affects your grades? This dataset simulates 1,000 students' daily habits—from study time to mental health—and compares them to final exam scores. It's like spying on your GPA through the lens of lifestyle. Perfect for EDA, ML practice, or just vibing with data while pretending to be productive.