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

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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