找出导致员工流失的因素,并探讨一些重要问题,例如“按工作角色和流失率显示离家距离的细分”或“按教育程度和流失率比较平均月收入”。这是由IBM数据科学家创建的虚构数据集。 数据列表 数据名称上传日期大小下载 WA_Fn-UseC_-HR-Employee-Attrition.csv2021-02-05222.63KB ...
df=pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')df.head() 在这里插入图片描述 代码语言:javascript 复制 df.dtypes Age int64 Attrition object BusinessTravel object DailyRate int64 Department object DistanceFromHome int64 Education int64 Education...
找出导致员工流失的因素,并探讨一些重要问题,例如“按工作角色和流失率显示离家距离的细分”或“按教育程度和流失率比较平均月收入”。这是由IBM数据科学家创建的虚构数据集。 WA_Fn-UseC_-HR-Employee-Attrition.csv
Attrition 1470 non-null object BusinessTravel 1470 non-null object DailyRate 1470 non-null int64 Department 1470 non-null object DistanceFromHome 1470 non-null int64 Education 1470 non-null int64 EducationField 1470 non-null object EmployeeCount 1470 non-null int64 EmployeeNumber 1470 non-null int64...
df = pd.read_csv('WA_Fn-UseC_-HR-Employee-Attrition.csv') df.head()# Output shown below 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 数据集的Pandas Dataframe的输出 # checking whether the dataset contains any missing values... df.shape == df.dropna().shape # Output shown below ...
attrition = pd.read_csv('E:/user/desktop/tangjiajun/Desktop/ibm-employee-attrition.git/data/WA_Fn-UseC_-HR-Employee-Attrition.csv') attrition.head() 3.1.3理解各数据变量含义 'Attrition'将是模型训练的目标列。 3.2数据探索 3.2.1查询空值 ...
This is a fictional data set created by IBM data scientists. WA_Fn-UseC_-HR-Employee-Attrition.csv
A command line utility for predicting whether employees are at risk of voluntarily leaving their employment. To make predictions, AtPred uses a Support Vector Machine trained against publicly available IBM HR employee attrition data. - MLBC-lab/AtPred
ibm-hr-analytics-attrition-dataset-数据集爱说**e〝 上传 数据集 WA_Fn-UseC_-HR-Employee-Attrition.csv 点赞(0) 踩踩(0) 反馈 所需:1 积分 电信网络下载 easy-iot-物联网开发资源 2024-10-22 22:59:29 积分:1 M0G3507软件模拟IIC 2024-10-22 20:17:55 积分:1 ...
这是由 IBM 数据科学家创建的虚构数据集。...in filenames: print(os.path.join(dirname, filename)) 输出前五行 df = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset...所有工作的人都在18岁以上删除不必要的列 df= df.drop(['EmployeeCount','EmployeeNumber','Over18','Standard...