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