input: Write a function to create a simple linear regression model in Python. The function should read data from a CSV file, train the model on the data, make predictions, and visualize the results using a scatter plot with a regression line.
  
  
  import pandas as pd
  import matplotlib.pyplot as plt
  from sklearn import linear_model
  
  def create_linear_regression_model(csv_file):
      # Read data from CSV
      data = pd.read_csv(csv_file)
      
      # Set X and Y
      X = data[['X']]
      Y = data['Y']
      
      # 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()
  
  assert create_linear_regression_model('data.csv') # Assuming the CSV file has columns 'X' and 'Y'
  
output: 
  def create_linear_regression_model(csv_file):
      # Read data from CSV
      data = pd.read_csv(csv_file)
      
      # Set X and Y
      X = data[['X']]
      Y = data['Y']
      
      # 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()
  