input: def linear_regression_example():
      """
      Demonstrate a simple linear regression model in Python. The code reads data
      from a CSV file, trains a linear regression model, makes predictions, and then
      visualizes the results.
      
      Example:
      linear_regression_example()
      """
output:     
      import pandas as pd
      import numpy as np
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LinearRegression
      from sklearn.metrics import mean_squared_error
      import matplotlib.pyplot as plt
  
      # Load data from CSV
      data = pd.read_csv('data.csv')
  
      # Split data into features and target
      X = data[['feature1', 'feature2']]
      y = data['target']
  
      # Split data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train the linear regression model
      model = LinearRegression()
      model.fit(X_train, y_train)
  
      # Make predictions
      y_pred = model.predict(X_test)
  
      # Evaluate the model
      mse = mean_squared_error(y_test, y_pred)
      print(f'Mean Squared Error: {mse}')
  
      # Visualize the results
      plt.scatter(y_test, y_pred)
      plt.xlabel('Actual Values')
      plt.ylabel('Predicted Values')
      plt.title('Actual vs Predicted Values')
      plt.show()
  