ML之DT：机器学习根据大量数据,已知年龄、收入、是否上海人、私家车价格的一个人，预测是否有真实购买上海黄浦区楼房的能力

ML之DT：机器学习根据大量数据,已知年龄、收入、是否上海人、私家车价格的一个人，预测是否有真实购买上海黄浦区楼房的能力

目录

输出结果​​

实现代码

输出结果

实现代码

  

   

    

     

    
    

     

      from sklearn.feature_extraction import DictVectorizer  
     
    
   

    

     

    
    

     

      import csv 
     
    
   

    

     

    
    

     

      from sklearn import tree  
     
    
   

    

     

    
    

     

      from sklearn import preprocessing  
     
    
   

    

     

    
    

     

      from sklearn.externals.six import StringIO
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      allElectronicsData = open(r'F:/AI/DL_month1201/01DTree/niu.csv', 'rt')
     
    
   

    

     

    
    

     

      reader = csv.reader(allElectronicsData)  
     
    
   

    

     

    
    

     

      headers = next(reader) 
     
    
   

    

     

    
    

     

      print(headers)
     
    
   

    

     

    
    

     

      featureList = [] 
     
    
   

    

     

    
    

     

      labelList = []   
     
    
   

    

     

    
    

     

      for row in reader: 
     
    
   

    

     

    
    

     

       labelList.append(row[len(row)-1])  
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       rowDict = {}
     
    
   

    

     

    
    

     
 for i in range(1, len(row)-1):  
     
    
   

    

     

    
    

     

       rowDict[headers[i]] = row[i] 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       featureList.append(rowDict) 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      print(featureList)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      vec = DictVectorizer()   
     
    
   

    

     

    
    

     

      dummyX = vec.fit_transform(featureList) .toarray()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      print("dummyX: " + str(dummyX))
     
    
   

    

     

    
    

     

      print(vec.get_feature_names())
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      print("labelList: " + str(labelList))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      lb = preprocessing.LabelBinarizer()   
     
    
   

    

     

    
    

     

      dummyY = lb.fit_transform(labelList)
     
    
   

    

     

    
    

     

      print("dummyY: " + str(dummyY))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      clf = tree.DecisionTreeClassifier(criterion='entropy')  
     
    
   

    

     

    
    

     

      clf = clf.fit(dummyX, dummyY)  
     
    
   

    

     

    
    

     

      print("clf: " + str(clf))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      with open("niu.dot", 'w') as f: 
     
    
   

    

     

    
    

     

       f = tree.export_graphviz(clf, feature_names=vec.get_feature_names(), out_file=f)  
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      oneRowX = dummyX[0, :]
     
    
   

    

     

    
    

     

      print("oneRowX: " + str(oneRowX))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      newRowX = oneRowX
     
    
   

    

     

    
    

     

      newRowX[0] = 1
     
    
   

    

     

    
    

     

      newRowX[2] = 0
     
    
   

    

     

    
    

     

      print("newRowX: " + str(newRowX))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      predictedY = clf.predict([newRowX])
     
    
   

    

     

    
    

     

      print("predictedY: " + str(predictedY))
     
    
  
 

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ML之DT：机器学习根据大量数据,已知年龄、收入、是否上海人、私家车价格的一个人，预测是否有真实购买上海黄浦区楼房的能力

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原文链接：yunyaniu.blog.csdn.net/article/details/83550197