input: 
  def simple_linear_regression():
      """ Create a simple linear regression model in Python.
      The code reads data from a CSV file, trains a linear regression model, and
      visualizes the results by plotting the original data points and the fitted
      regression line.
      Example:
      for simple_linear_regression()
      (code executes and shows a plot of the linear regression results)
      """
      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, 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:     # Reshape X to be a 2D array
      X = X.values.reshape(-1, 1)
  
      # 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.xlabel('X')
      plt.ylabel('Y')
      plt.title('Simple Linear Regression')
      plt.show()