input: Write a function to perform simple linear regression on a given dataset stored in a CSV file. The function should read the data, train a linear regression model, and visualize the results by plotting the actual data points and the predicted linear regression line.
  assert linear_regression_visualization('data1.csv')
  assert linear_regression_visualization('data2.csv')
  assert linear_regression_visualization('data3.csv')
output: import pandas as pd
  import matplotlib.pyplot as plt
  from sklearn.linear_model import LinearRegression
  
  def linear_regression_visualization(filename):
      data = pd.read_csv(filename)
      X = data.iloc[:, 0].values.reshape(-1, 1)
      y = data.iloc[:, 1].values
      
      model = LinearRegression()
      model.fit(X, y)
      y_pred = model.predict(X)
      
      plt.scatter(X, y, color='blue', label='Actual Data')
      plt.plot(X, y_pred, color='red', label='Regression Line')
      plt.xlabel('X')
      plt.ylabel('y')
      plt.legend()
      plt.show()