input: 
  import numpy as np
  import keras 
  from keras.layers import Dense
  from keras.models import Sequential
  
  # 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')
  
  # Obtain some data - age (years), education level (high school, college, university)
  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]])
  
  # Labels - annual income
  y_train = [15000, 20000, 25000, 30000,
             40000, 45000, 50000, 60000, 
  	   65000, 70000, 80000, 90000]
  
  # Fit the model
  model.fit(x_train, y_train, epochs = 150, batch_size = 2)
  
  # Calculate the predicted annual income
  age = 50
  education = 3
  annualIncome = model.predict(np.array([[age, education]]))
  print(annualIncome)
  # output: [[80000]]
  
output:     
  # Evaluate the model
  scores = model.evaluate(x_train, y_train, verbose=0)
  print(f"Model accuracy: {scores}")
  
  # Save the model
  model.save('income_prediction_model.h5')
  
  # Load the model
  from keras.models import load_model
  loaded_model = load_model('income_prediction_model.h5')
  
  # Predict using the loaded model
  loaded_annualIncome = loaded_model.predict(np.array([[age, education]]))
  print(loaded_annualIncome)
  # output: [[80000]]
  