Python 我尝试使用SVM模型进行机器学习，但它没有'；不完整

我试图在数据集上使用SVM模型。列车记录125973条，试验记录22544条。它永远保持计算，永远不会完成。有人能帮我吗。下面是到目前为止我的python代码

import pandas as pd
import sklearn.svm as s
import sklearn.metrics as m
import sklearn.preprocessing as pp

traindata = pd.read_csv("E:\\a-train.csv")

testdata = pd.read_csv("E:\\a-test.csv")

for col in traindata.columns:
    if traindata[col].dtype == type(object):
        le = pp.LabelEncoder()
        traindata[col] = le.fit_transform(traindata[col])
        
for col in testdata.columns:
    if testdata[col].dtype == type(object):
        le = pp.LabelEncoder()
        testdata[col] = le.fit_transform(testdata[col])

countS = 0

featsTrain = traindata.values[:,0:13]

featsTest = testdata.values[:,0:13]

lblsTrain = traindata.values[:,13]

lblsTest = testdata.values[:,13]

modelS = s.SVC(cache_size = 7000)

modelS.fit(featsTrain, lblsTrain)

lblsPredS = modelS.predict(featsTest)

for a,b in zip(lblsTest, lblsPredS):
   if a == b:
      countS += 1

accS = (round(countS/(len(featsTest)), 3)) * 100

print( m.confusion_matrix(lblsTest, lblsPredS) )

print(m.classification_report(lblsTest, lblsPredS))

print("\nAccuracy = ", accS, "%")

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