地球资源数据云——数据资源详情
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

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
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。
数据格式为 CSV。
该数据集覆盖范围为全球。
在本页登录后即可下载。建议引用格式:地球资源数据云. 人工智能对 2030 年就业的影响. https://www.gis5g.com/dataset/2031263649593987074