input: Write a function that demonstrates how to create and train a simple linear regression model in Python, and then visualize the results using matplotlib. The function should read data from a CSV file, fit the regression model, and plot the original data points along with the regression line.
  
  def linear_regression_visualization():
      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()
  
  # Example usage:
  assert linear_regression_visualization is not None
  # No output for visualization function, only graph plot
output: def linear_regression_visualization():
      import pandas as pd
      import matplotlib.pyplot as plt
      from sklearn import linear_model
  
      data = pd.read_csv('data.csv')
      X = data['X']
      Y = data['Y']
      model = linear_model.LinearRegression()
      model.fit(X.values.reshape(-1, 1), Y)
      Y_pred = model.predict(X.values.reshape(-1, 1))
      plt.scatter(X, Y)
      plt.plot(X, Y_pred, color='red')
      plt.show()