Please i need an explanation for this code as it's a part of my graduation project, thank you Here is the code:

**Please i need an explanation for this code as it's a part of my graduation project, thank you

Here is the code: This svm code for classify**

clf = svm.SVR()
clf.fit(X=featureTrain, y=targetTrain)
test_svm = clf.predict(featureTest)
acc1 = []
x=targetTest.values
for i in range(test_svm.size):
  diff = np.abs(test_svm[i] - x[i])
  if diff >0:
    perDiff = (diff/x[i])*100
    acc1.append(100-np.abs(perDiff))
  else:
      acc1.append(np.abs(100))
print('SVM accuracy: '+str(np.mean(acc1)))
---------------------------------------------------------------------

This neural network code which had ann model

X = featureTrain.values
Y = targetTrain.values
model = Sequential()
model.add(Dense(16, input_dim=6, kernel_initializer='uniform', 
activation='relu'))
model.add(Dense(32, kernel_initializer='uniform', activation='relu'))
model.add(Dense(200, kernel_initializer='uniform', activation='relu'))
model.add(Dense(100, kernel_initializer='uniform', activation='relu'))
model.add(Dense(32, kernel_initializer='uniform', activation='relu'))
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='mse', optimizer='adam', metrics=['accuracy'])
# checkpoint
filepath="../drive/My Drive/weights.best.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, 
save_best_only=True, mode='max')
callbacks_list = [checkpoint]
# Fit the model
model.fit(X, Y, epochs=150, batch_size=10, callbacks=callbacks_list, 
verbose=1)

predictions = model.predict(featureTest)
acc2 = []
x=targetTest.values
for i in range(predictions.size):
  diff = np.abs(predictions[i] - x[i])
  if diff >0:
    perDiff = (diff/x[i])*100
    acc2.append(100-np.abs(perDiff))
  else:
      acc2.append(np.abs(100))