input: def simple_linear_regression():
      """
      This function implements a simple linear regression model in Python. It reads data from a CSV file,
      fits a linear model to the data, makes predictions, and visualizes the results.
      
      Example:
      (Assuming 'data.csv' contains columns 'X' and 'Y')
      >>> simple_linear_regression()
      (Displays a scatter plot of X vs Y with a red line representing the linear regression model)
      """
output:     
      import pandas as pd
      import matplotlib.pyplot as plt
      from sklearn.linear_model import LinearRegression
  
      # Load the data from the CSV file
      data = pd.read_csv('data.csv')
  
      # Extract the features (X) and the target (Y)
      X = data[['X']]
      Y = data['Y']
  
      # Create and fit the linear regression model
      model = LinearRegression()
      model.fit(X, Y)
  
      # Make predictions using the model
      predictions = model.predict(X)
  
      # Visualize the results
      plt.scatter(X, Y, color='blue')
      plt.plot(X, predictions, color='red')
      plt.xlabel('X')
      plt.ylabel('Y')
      plt.title('Simple Linear Regression')
      plt.show()
  