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Given the following text problem statement, write Python code to implement the functionality described below in problem statement
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Problem:
making new class prediction for a classification problem

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Python Code::
from keras.models import Sequential
from keras.layers import Dense
from sklearn.datasets import make_blobs
from sklearn.preprocessing import MinMaxScaler
from numpy import array
X, y = make_blobs(n_samples=100, centers=2, n_features=2, random_state=1)
scalar = MinMaxScaler()
scalar.fit(X)
X = scalar.transform(X)
model = Sequential()
model.add(Dense(4, input_shape=(2,), activation='relu'))
model.add(Dense(4, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam')
model.fit(X, y, epochs=500, verbose=0)
Xnew = array([[0.89337759, 0.65864154]])
ynew = model.predict_classes(Xnew)

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