Python 建模步骤

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#%%
#载入数据 、查看相关信息
import pandas as pd
import numpy as np
from  sklearn.preprocessing import LabelEncoder

print(第一步:加载、查看数据)

file_path = rD:\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().sum

band_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_selection
train,test,t_train,t_test = model_selection.train_test_split(x,y,test_size=0.3,random_state=1)

from sklearn import tree
model = 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.index

import graphviz

import os
os.environ["PATH"] += os.pathsep + rD:\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)
#graph
graph.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_matrix
confmat = confusion_matrix(t_test,res)
print(confmat)

#分类指标 https://blog.csdn.net/akadiao/article/details/78788864
from sklearn.metrics import classification_report
print(classification_report(t_test,res))

#%%
print(第五步:保存模型)

from sklearn.externals import joblib
joblib.dump(model,rD:\train\201905data\mymodel.model)

#%%
print(第六步:加载新数据、使用模型)
file_path_do = rD:\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 = rD:\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 = rD:\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)

 

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