input: def simple_linear_regression(data: pd.DataFrame) -> None:
      """ A function that performs simple linear regression on the given dataset. It trains a linear regression model, makes predictions, and visualizes the results.
      >>> data = pd.DataFrame({'X': [1, 2, 3, 4], 'Y': [2, 3, 4, 5]})
      >>> simple_linear_regression(data)
      No output
      """
      import matplotlib.pyplot as plt
      from sklearn import linear_model
  
      # Set X and Y
      X = data['X'].values.reshape(-1, 1)
      Y = data['Y'].values
  
      # Create linear regression model
      model = linear_model.LinearRegression()
  
      # Train the model
      model.fit(X, Y)
  
      # Make predictions
      Y_pred = model.predict(X)
  
      # Visualize the results
      plt.scatter(X, Y)
      plt.plot(X, Y_pred, color='red')
      plt.show()
output:     # Print the coefficients
      print(f"Intercept: {model.intercept_}")
      print(f"Coefficient: {model.coef_[0]}")