一、比赛说明

比赛地址：/c/bi-attrition-predict

问题描述

数据包括员工的各种统计信息，以及该员工是否已经离职，统计的信息包括工资、出差、工作环境满意度、工作投入度、是否加班、是否升职、工资提升比例等。

需要通过训练数据得出员工离职预测的结果。

评分标准

AUC

二、代码

import pandas as pdtrain = pd.read_csv('train.csv',index_col=0)test = test1 = pd.read_csv('test.csv',index_col=0)#print(train['Attrition'].value_counts())# 处理Attrition字段train['Attrition'] = train['Attrition'].map(lambda x: 1 if x == 'Yes' else 0)from sklearn.preprocessing import LabelEncoder# 查看数据是否有空值#print(train.isna().sum())# 去掉没用的列 员工号码，标准工时（=80）train = train.drop(['EmployeeNumber', 'StandardHours'], axis=1)test = test.drop(['EmployeeNumber', 'StandardHours'], axis=1)# 对于分类特征进行特征值编码attr=['Age','BusinessTravel','Department','Education','EducationField','Gender','JobRole','MaritalStatus','Over18','OverTime']lbe_list = []for feature in attr:lbe=LabelEncoder()train[feature] = lbe.fit_transform(train[feature])test[feature] = lbe.transform(test[feature])lbe_list.append(lbe)#print(train)from sklearn.svm import SVC, LinearSVCfrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import MinMaxScalerX_train, X_valid, y_train, y_valid = train_test_split(train.drop('Attrition',axis=1), train['Attrition'], test_size=0.2, random_state=42)mms = MinMaxScaler(feature_range = (0, 1))X_train = mms.fit_transform(X_train)X_valid = mms.fit_transform(X_valid)test = mms.fit_transform(test)model = SVC(kernel='rbf', gamma="auto",max_iter=1000,random_state=33,verbose=True,tol=1e-5,cache_size=50000)#print(X_train)#print(y_train)#print(sum(y_train))model = LinearSVC(max_iter=1000,random_state=33,verbose=True,)model.fit(X_train, y_train)predict = model.predict(test)print(predict)#print(test)#predict = model.predict_proba(test)[:, 1]test1['Attrition'] = predict# 转化为二分类输出#test['Attrition']=test['Attrition'].map(lambda x:1 if x>=0.5 else 0)test1[['Attrition']].to_csv('submit_svc.csv')

三、分数

0.72327