使用python进行数据处理的实例(数据为某公司HR部门关于员工信息的部分摘录,kaggle上面的一次赛题)
https://www.kaggle.com/c/kfru-dbm-hr-analytics
该实例是根据其他所给属性预测员工是否会离职,代码实现如下所示
import pandas as pd
from sklearn.preprocessing import MinMaxScaler,StandardScaler
from sklearn.preprocessing import LabelEncoder,OneHotEncoder
from sklearn.decomposition import PCA
def hr_preprocessing(sl=False,le=False,npr=False,amh=False,tsc=False,wa=False,pl5=False,dp=False,slr=False,lower_id=False,ld_n=1):
df=pd.read_csv(‘C:\\Users\\Administrator\Desktop\\network\\HR.csv‘)
#1 清洗数据,根据探索性数据分析出的结果来去掉空值
df=df.dropna(subset=[‘satisfaction_level‘,‘last_evaluation‘])
df=df[df[‘satisfaction_level‘]<=1][df[‘salary‘]!=‘nme‘]
#2 得到标注
label=df[‘left‘]
df=df.drop(‘left‘,axis=1)
#3 特征选择(因为本文的特征本来就不多,暂时不做)
# 4 特征处理(归一化,标准化,降维)
scaler_lst=[sl,le,npr,amh,tsc,wa,pl5]
column_lst = ["satisfaction_level", "last_evaluation", "number_project",
"average_monthly_hours", "time_spend_company", "Work_accident",
"promotion_last_5years"]
for i in range(len(scaler_lst)):
if not scaler_lst[i]:
df[column_lst[i]]=MinMaxScaler().fit_transform(df[column_lst[i]].values.reshape(-1,1)).reshape(1,-1)[0]
else:
df[column_lst[i]]=StandardScaler().fit_transform(df[column_lst[i]].values.reshape(-1,1)).reshape(1,-1)[0]
#针对离散值的处理
scaler_lst=[slr,dp]
column_lst=[‘salary‘,‘department‘]
for i in range(len(scaler_lst)):
if not scaler_lst[i]:
if column_lst[i]==‘salary‘:
df[column_lst[i]]=[map_salary(s)for s in df[‘salary‘].values]
else:
df[column_lst[i]]=LabelEncoder().fit_transform(df[column_lst[i]])
df[column_lst[i]]=MinMaxScaler().fit_transform(df[column_lst[i]].values.reshape(-1,1)).reshape(1,-1)[0]
else:
df=pd.get_dummies(df,columns=[column_lst[i]])
if lower_id:
return PCA(n_components=ld_n).fit_transform(df.values),label
return df,label
d=dict([(‘low‘,0),(‘medium‘,1),(‘high‘,2)])
def map_salary(s):
return d.get(s,0)
#建模
def hr_modeling_nn(features,label):
from sklearn.model_selection import train_test_split
#sklearn中没有可以一次性将数据划分成验证集、训练集、测试集的包,所以需要进行两步划分
f_v=features.values
l_v=label.values
X_tt,X_validation,Y_tt,Y_validation=train_test_split(f_v,l_v,test_size=0.2)
X_train,X_test,Y_train,Y_test=train_test_split(X_tt,Y_tt,test_size=0.25)
#print(len(X_train),len(X_test),len(X_validation))
#分类
from sklearn.metrics import accuracy_score,recall_score,f1_score
from sklearn.neighbors import NearestNeighbors,KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB,BernoulliNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import GradientBoostingClassifier
models=[]
models.append((‘KNN‘,KNeighborsClassifier(n_neighbors=3)))
models.append((‘GaussianNB‘,GaussianNB()))
models.append((‘BernoulliNB‘,BernoulliNB()))
models.append((‘DecisionTreeGini‘,DecisionTreeClassifier()))
models.append((‘DecisionTreeEntropy‘,DecisionTreeClassifier(criterion=‘entropy‘)))
models.append((‘SVM:‘,SVC(C=1000)))
models.append((‘OriginalRandomForest‘,RandomForestClassifier()))
models.append((‘RandomForest‘,RandomForestClassifier(n_estimators=11,max_features=None)))
models.append((‘Adaboost‘,AdaBoostClassifier(n_estimators=100)))
models.append((‘LogisticRegression‘,LogisticRegression(C=1000,tol=1e-10,solver=‘sag‘,max_iter=10000)))
models.append((‘GBDT‘,GradientBoostingClassifier(max_depth=6,n_estimators=100)))
for clf_name ,clf in models:
clf.fit(X_train,Y_train)
xy_list=[(X_train,Y_train),(X_validation,Y_validation),(X_test,Y_test)]
for i in range(len(xy_list)):
X_part=xy_list[i][0]
Y_part=xy_list[i][1]
Y_pred=clf.predict(X_part)
print(i)
print(clf_name,‘-ACC‘,accuracy_score(Y_part,Y_pred))
print(clf_name,‘-REC:‘,recall_score(Y_part,Y_pred))
print(clf_name,‘-F1:‘,f1_score(Y_pred,Y_part))
#回归
def regr_t(features,label):
print(‘X‘,features)
print(‘Y‘,label)
from sklearn.linear_model import LinearRegression,Ridge,Lasso
regr=Ridge(alpha=1)
regr.fit(features.values,label.values)
Y_pred=regr.predict(features.values)
print(‘Coef:‘,regr.coef_)
from sklearn.metrics import mean_squared_error,mean_absolute_error,r2_score
print(‘MSE:‘,mean_squared_error(label.values,Y_pred))
print(‘MAE:‘,mean_absolute_error(label.values,Y_pred))
print(‘R2:‘,r2_score(label.values,Y_pred))
def main():
features,label=hr_preprocessing()
hr_modeling_nn(features,label)
regr_t(features[[‘number_project‘,‘average_monthly_hours‘]],features[‘last_evaluation‘])
if __name__==‘__main__‘:
main()
来源:https://blog.csdn.net/weixin_39667003/article/details/85632885
原文地址:https://www.cnblogs.com/qixidi/p/10229246.html
时间: 2024-10-14 04:15:23