Neural network is not giving the expected output after training in Python

My neural network is not giving the expected output after training in Python. Is there any error in the code? Is there any way to reduce the mean squared error (MSE)?

I tried to train (Run the program) the network repeatedly but it is not learning, instead it is giving the same MSE and output.

Here is the Data I used:

https://drive.google.com/open?id=1GLm87-5E_6YhUIPZ_CtQLV9F9wcGaTj2

Here is my code:

#load and evaluate a saved model
from numpy import loadtxt
from tensorflow.keras.models import load_model

# load model
model = load_model('ANNnew.h5')
# summarize model.
model.summary()
#Model starts
import numpy as np
import pandas as pd 
from tensorflow.keras.layers import Dense, Activation
from tensorflow.keras.models import Sequential
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
# Importing the dataset
X = pd.read_excel(r"C:\filelocation\Data.xlsx","Sheet1").values
y = pd.read_excel(r"C:\filelocation\Data.xlsx","Sheet2").values

# Splitting the dataset into the Training set and Test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.08, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# Initialising the ANN
model = Sequential()

# Adding the input layer and the first hidden layer
model.add(Dense(32, activation = 'tanh', input_dim = 4))

# Adding the second hidden layer
model.add(Dense(units = 18, activation = 'tanh'))

# Adding the third hidden layer
model.add(Dense(units = 32, activation = 'tanh'))

#model.add(Dense(1))
model.add(Dense(units = 1))

# Compiling the ANN
model.compile(optimizer = 'adam', loss = 'mean_squared_error')

# Fitting the ANN to the Training set
model.fit(X_train, y_train, batch_size = 100, epochs = 1000)

y_pred = model.predict(X_test)
for i in range(5):
    print('%s = %d (expected %s)' % (X[i].tolist(), y_pred[i], y[i].tolist()))


plt.plot(y_test, color = 'red', label = 'Test data')
plt.plot(y_pred, color = 'blue', label = 'Predicted data')
plt.title('Prediction')
plt.legend()
plt.show()

# save model and architecture to single file
model.save("ANNnew.h5")
print("Saved model to disk")