基于python的手写数字识别knn_用sklearn中的KNN实现Kaggle手写数字识别

import csv

from sklearn import neighbors

#导入训练数据和测试数据

def loadData(filename1,filename2,trainDataSet,trainTargetSet,testDataSet):

with open(filename1,'r') as csvfile1:

lines1 = csv.reader(csvfile1)

dataSet = list(lines1)

for x in range(1,len(dataSet)):

temp = []

dataSet[x][0] = int(dataSet[x][0])

trainTargetSet.append(dataSet[x][0])

for y in range(1,785):

#if dataSet[x][y] != 0:

#dataSet[x][y] = 1

dataSet[x][y] = int(dataSet[x][y])

temp.append(dataSet[x][y])

trainDataSet.append(temp)

with open(filename2,'r') as csvfile2:

lines2 = csv.reader(csvfile2)

dataSet2 = list(lines2)

for x in range(1,len(dataSet2)):

temp = []

for y in range(784):

#if dataSet2[x][y] != 0:

#dataSet2[x][y] = 1

dataSet2[x][y] = int(dataSet2[x][y])

temp.append(dataSet2[x][y])

testDataSet.append(temp)

return trainDataSet,trainTargetSet,testDataSet

#将结果保存为csv文件用于在kaggle网站提交

def saveResult(result):

#结果保存的路径

with open(r'result_new.csv','w',newline='') as myFile:

myWriter=csv.writer(myFile)

x=0

for i in result:

x += 1

tmp=[x]

tmp.append(i)

myWriter.writerow(tmp)

def main():

trainDataSet = []

trainTargetSet = []

testDataSet = []

print("开始加载数据")

#训练数据和测试数据的路径

loadData(r'train.csv', r'test.csv', trainDataSet, trainTargetSet, testDataSet)

knn = neighbors.KNeighborsClassifier()

print("数据加载完毕，开始训练模型")

knn.fit(trainDataSet,trainTargetSet)

print("模型训练完毕，开始预测")

prediction = knn.predict(testDataSet)

print("预测结果:", prediction)

print("打印完毕，开始保存")

saveResult(prediction)

print("保存完毕")

if __name__ == '__main__':

main()

开始加载数据

数据加载完毕，开始训练模型

模型训练完毕，开始预测

预测结果: [2 0 9 ..., 3 9 2]

打印完毕，开始保存

保存完毕

上传到Kaggle网站上的结果：

结果排名