input: Write a function to design a neural network in Python using Keras to predict a customer’s annual income based on their age and education level, and print the predicted annual income for a given age and education level.
  import numpy as np
  import keras 
  from keras.layers import Dense
  from keras.models import Sequential
  
  def predict_income(x_train, y_train, age, education):
      # Build model
      model = Sequential()
      model.add(Dense(64, input_dim=2, activation='relu'))
      model.add(Dense(1))
  
      # Compile the model
      model.compile(loss='mean_squared_error', optimizer='adam')
  
      # Fit the model
      model.fit(x_train, y_train, epochs=150, batch_size=2, verbose=0)
  
      # Calculate the predicted annual income
      annualIncome = model.predict(np.array([[age, education]]))
      return annualIncome[0][0]
  
  # Test cases
  x_train = np.array([[25, 0], [30, 0], [35, 0], 
                      [40, 0], [45, 1], [45, 2],
                      [50, 1], [50, 2], [55, 1],
                      [55, 2], [50, 3], [60, 3]])
  y_train = [15000, 20000, 25000, 30000,
             40000, 45000, 50000, 60000, 
             65000, 70000, 80000, 90000]
  
  assert predict_income(x_train, y_train, 50, 3) == 80000
  assert predict_income(x_train, y_train, 30, 0) == 20000
  assert predict_income(x_train, y_train, 60, 3) == 90000
output: def predict_income(x_train, y_train, age, education):
      model = Sequential()
      model.add(Dense(64, input_dim=2, activation='relu'))
      model.add(Dense(1))
      model.compile(loss='mean_squared_error', optimizer='adam')
      model.fit(x_train, y_train, epochs=150, batch_size=2, verbose=0)
      annualIncome = model.predict(np.array([[age, education]]))
      return annualIncome[0][0]