地球资源数据云——数据资源详情
人力资源分析
该数据集《HR Analytics》主要用于监督学习任务，数据形态以表格为主。 题目说明：Employee attrition prediction 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：HR - Employee - Attrition.csv。 HR Analytics helps us with interpreting organizational data. It finds the people - related trends in the data and allows the HR Department to take the appropriate steps to keep the organization running smoothly and profitably. Attrition in a corporate setup is one of the complex challenges that the people managers and the HRs personnel have to deal with. Interestingly, machine learning models can be deployed to predict potential attrition cases, helping the appropriate HR Personnel take the necessary steps to retain the employee. Tasks to perform: Data Cleaning: Deleting redundant columns. Renaming the columns. Dropping duplicates. Cleaning individual columns. Remove the NaN values from the dataset Check for some more Transformations
摘要概览
该数据集《HR Analytics》主要用于监督学习任务，数据形态以表格为主。 题目说明：Employee attrition prediction
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：HR - Employee - Attrition.csv。
HR Analytics helps us with interpreting organizational data. It finds the people - related trends in the data and allows the HR Department to take the appropriate steps to keep the organization running smoothly and profitably. Attrition in a corporate setup is one of the complex challenges that the people managers and the HRs personnel have to deal with.
Interestingly, machine learning models can be deployed to predict potential attrition cases, helping the appropriate HR Personnel take the necessary steps to retain the employee.
Tasks to perform:
Data Cleaning:
Deleting redundant columns. Renaming the columns. Dropping duplicates. Cleaning individual columns. Remove the NaN values from the dataset Check for some more Transformations