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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 predictions for a regression problem

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Python Code::
from keras.models import Sequential
from keras.layers import Dense
from sklearn.datasets import make_regression
from sklearn.preprocessing import MinMaxScaler
from numpy import array
X, y = make_regression(n_samples=100, n_features=2, noise=0.1, random_state=1)
scalarX, scalarY = MinMaxScaler(), MinMaxScaler()
scalarX.fit(X)
scalarY.fit(y.reshape(100,1))
X = scalarX.transform(X)
y = scalarY.transform(y.reshape(100,1))
model = Sequential()
model.add(Dense(4, input_shape=(2,), activation='relu'))
model.add(Dense(4, activation='relu'))
model.add(Dense(1, activation='linear'))
model.compile(loss='mse', optimizer='adam')
model.fit(X, y, epochs=1000, verbose=0)
Xnew = array([[0.29466096, 0.30317302]])
ynew = model.predict(Xnew)

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