input: 
  def linear_regression_model():
      """
      This function creates a simple linear regression model in Python using pandas for data handling and matplotlib for visualization.
      
      >>> linear_regression_model()
      # This function does not return any value but plots the linear regression model.
      """
      import pandas as pd
      import matplotlib.pyplot as plt
      from sklearn import linear_model
  
      # Read data from CSV
      data = pd.read_csv('data.csv')
  
      # Set X and Y
      X = data['X']
      Y = data['Y']
  
      # Create linear regression model
      model = linear_model.LinearRegression()
  
      # Train the model
      model.fit(X.values.reshape(-1, 1), Y)
  
      # Make predictions
      Y_pred = model.predict(X.values.reshape(-1, 1))
  
      # Visualize the results
      plt.scatter(X, Y)
      plt.plot(X, Y_pred, color='red')
      plt.show()
  
  
output:     # Save the plot to a file
      plt.savefig('linear_regression_plot.png')