Python 建模步骤，python建模步骤,#%%#载入数据 、

Python 建模步骤，python建模步骤,#%%#载入数据 、

#%%#载入数据 、查看相关信息import pandas as pdimport numpy as npfrom  sklearn.preprocessing import LabelEncoderprint(‘第一步：加载、查看数据‘)file_path = r‘D:\train\201905data\liwang.csv‘band_data = pd.read_csv(file_path,encoding=‘UTF-8‘)band_data.info()band_data.shape#%%#print(‘第二步：清洗、处理数据，某些数据可以使用数据库处理数据代替‘)#数据清洗:缺失值处理：丢去、#查看缺失值band_data.isnull().sumband_data = band_data.dropna()#band_data = band_data.drop([‘state‘],axis=1)# 去除空格band_data[‘voice_mail_plan‘] = band_data[‘voice_mail_plan‘].map(lambda x: x.strip())band_data[‘intl_plan‘] = band_data[‘intl_plan‘].map(lambda x: x.strip())band_data[‘churned‘] = band_data[‘churned‘].map(lambda x: x.strip())band_data[‘voice_mail_plan‘] = band_data[‘voice_mail_plan‘].map({‘no‘:0, ‘yes‘:1})band_data.intl_plan = band_data.intl_plan.map({‘no‘:0, ‘yes‘:1})for column in band_data.columns:    if band_data[column].dtype == type(object):        le = LabelEncoder()        band_data[column] = le.fit_transform(band_data[column])#band_data = band_data.drop([‘phone_number‘],axis=1)#band_data[‘churned‘] = band_data[‘churned‘].replace([‘ True.‘,‘ False.‘],[1,0])#band_data[‘intl_plan‘] = band_data[‘intl_plan‘].replace([‘ yes‘,‘ no‘],[1,0])#band_data[‘voice_mail_plan‘] = band_data[‘voice_mail_plan‘].replace([‘ yes‘,‘ no‘],[1,0])#%%# 模型  [重复、调优]print(‘第三步：选择、训练模型‘)x = band_data.drop([‘churned‘],axis=1)y = band_data[‘churned‘]from sklearn import model_selectiontrain,test,t_train,t_test = model_selection.train_test_split(x,y,test_size=0.3,random_state=1)from sklearn import treemodel = tree.DecisionTreeClassifier(max_depth=2)model.fit(train,t_train)fea_res = pd.DataFrame(x.columns,columns=[‘features‘])fea_res[‘importance‘] = model.feature_importances_t_name= band_data[‘churned‘].value_counts()t_name.indeximport graphvizimport osos.environ["PATH"] += os.pathsep + r‘D:\software\developmentEnvironment\graphviz-2.38\release\bin‘dot_data= tree.export_graphviz(model,out_file=None,feature_names=x.columns,max_depth=2,                         class_names=t_name.index.astype(str),                         filled=True, rounded=True,                         special_characters=False)graph = graphviz.Source(dot_data)#graphgraph.render("dtr")#%%print(‘第四步：查看、分析模型‘)#结果预测res = model.predict(test)#混淆矩阵from sklearn.metrics import confusion_matrixconfmat = confusion_matrix(t_test,res)print(confmat)#分类指标 https://blog.csdn.net/akadiao/article/details/78788864from sklearn.metrics import classification_reportprint(classification_report(t_test,res))#%%print(‘第五步：保存模型‘)from sklearn.externals import joblibjoblib.dump(model,r‘D:\train\201905data\mymodel.model‘)#%%print(‘第六步：加载新数据、使用模型‘)file_path_do = r‘D:\train\201905data\do_liwang.csv‘deal_data = pd.read_csv(file_path_do,encoding=‘UTF-8‘)#数据清洗:缺失值处理deal_data = deal_data.dropna()deal_data[‘voice_mail_plan‘] = deal_data[‘voice_mail_plan‘].map(lambda x: x.strip())deal_data[‘intl_plan‘] = deal_data[‘intl_plan‘].map(lambda x: x.strip())deal_data[‘churned‘] = deal_data[‘churned‘].map(lambda x: x.strip())deal_data[‘voice_mail_plan‘] = deal_data[‘voice_mail_plan‘].map({‘no‘:0, ‘yes‘:1})deal_data.intl_plan = deal_data.intl_plan.map({‘no‘:0, ‘yes‘:1})for column in deal_data.columns:    if deal_data[column].dtype == type(object):        le = LabelEncoder()        deal_data[column] = le.fit_transform(deal_data[column])#数据清洗#加载模型model_file_path = r‘D:\train\201905data\mymodel.model‘deal_model = joblib.load(model_file_path)#预测res = deal_model.predict(deal_data.drop([‘churned‘],axis=1))#%%print(‘第七步：执行模型，提供数据‘)result_file_path = r‘D:\train\201905data\result_liwang.csv‘deal_data.insert(1,‘pre_result‘,res)deal_data[[‘state‘,‘pre_result‘]].to_csv(result_file_path,sep=‘,‘,index=True,encoding=‘UTF-8‘)

Python 建模步骤