Python 想根据这些数据训练一个模型，预测发动机是否会发生故障

在训练模型旁边要做什么？

首先决定哪个模型适合数据。它应该是

逻辑回归

还是

KNN

或

随机森林

等

finalfaultdata = pd.read_csv('FinalFaultData.csv')

passedmaster = pd.read_csv('PASSEDMASTER.csv')

A = pd.concat([finalfaultdata,passedmaster])

print(len(A))

train, test = train_test_split(A, test_size = 0.2,random_state = 40)

print(len(train))

print(len(test))

y = A['Verdict']

print(y)

select = ['Engine Temperature', 'Stepper Motor Position', 'MAP', 'Injection Time', 'Ignition Angle 
          Output','Water Pressure', 'Oil Pressure', 'Oil Temperature', 'Exhaust Temperature', 'Fuel 
          Pressure','Mech Errors', 'Throttle Position', 'Engine Load', 'Lambda AVG Correction', 'Intake 
          Air Temp', 'Idle Speed Controller', 'Battery Voltage', 'Lambda Correction', 'Throttle Status']
           
X = A[select]

print(X)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 5)

print (X_train.shape, X_test.shape, y_train.shape, y_test.shape)