地球资源数据云——数据资源详情
员工流失分类数据集
该数据集《Employee Attrition Classification Dataset》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：An In - Depth Synthetic Simulation for Attrition Analysis and Prediction 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：test.csv, train.csv。 The Synthetic Employee Attrition Dataset is a simulated dataset designed for the analysis and prediction of employee attrition. It contains detailed information about various aspects of an employee's profile, including demographics, job - related features, and personal circumstances. The dataset comprises 74,498 samples, split into training and testing sets to facilitate model development and evaluation. Each record includes a unique Employee ID and features that influence employee attrition. The goal is to understand the factors contributing to attrition and develop predictive models to identify at - risk employees. This dataset is ideal for HR analytics, machine learning model development, and demonstrating advanced data analysis techniques. It provides a comprehensive and realistic view of the factors affecting employee retention, making it a valuable resource for researchers and practitioners in the field of human resources and organizational development. FEATURES: Employee ID: A unique identifier assigned to each employee. Age: The age of the employee, ranging from 18 to 60 years. Gender: The gender of the employee Years at Company: The number of years the employee has been working at the company. Monthly Income: The monthly salary of the employee, in dollars.
摘要概览
该数据集《Employee Attrition Classification Dataset》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：An In - Depth Synthetic Simulation for Attrition Analysis and Prediction
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：test.csv, train.csv。
The Synthetic Employee Attrition Dataset is a simulated dataset designed for the analysis and prediction of employee attrition. It contains detailed information about various aspects of an employee's profile, including demographics, job - related features, and personal circumstances.
The dataset comprises 74,498 samples, split into training and testing sets to facilitate model development and evaluation. Each record includes a unique Employee ID and features that influence employee attrition. The goal is to understand the factors contributing to attrition and develop predictive models to identify at - risk employees.
This dataset is ideal for HR analytics, machine learning model development, and demonstrating advanced data analysis techniques. It provides a comprehensive and realistic view of the factors affecting employee retention, making it a valuable resource for researchers and practitioners in the field of human resources and organizational development.
FEATURES:
Employee ID: A unique identifier assigned to each employee. Age: The age of the employee, ranging from 18 to 60 years. Gender: The gender of the employee Years at Company: The number of years the employee has been working at the company. Monthly Income: The monthly salary of the employee, in dollars.