地球资源数据云——数据资源详情
人工智能对 2030 年就业的影响
该数据集《AI Impact on Jobs 2030》主要用于监督学习任务，数据形态以表格为主，应用场景偏向文本内容分析。 题目说明：Predicting automation risk across global professions by 2030. 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：AI_Impact_on_Jobs_2030.csv。 This dataset simulates the future of work in the age of artificial intelligence. It models how various professions, skills, and education levels might be impacted by AI - driven automation by the year 2030. The goal is to enable research, machine learning modeling, and data visualization around the question: “Which types of jobs are most at risk of automation — and why?” It can be used for: Predictive modeling of automation probability
摘要概览
该数据集《AI Impact on Jobs 2030》主要用于监督学习任务，数据形态以表格为主，应用场景偏向文本内容分析。 题目说明：Predicting automation risk across global professions by 2030.
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：AI_Impact_on_Jobs_2030.csv。
This dataset simulates the future of work in the age of artificial intelligence. It models how various professions, skills, and education levels might be impacted by AI - driven automation by the year 2030.
The goal is to enable research, machine learning modeling, and data visualization around the question:
“Which types of jobs are most at risk of automation — and why?”
It can be used for:
Predictive modeling of automation probability