input: Write a function to implement a simple linear regression model in Python. The function should read data from a CSV file, train the model, make predictions, and visualize the results using a scatter plot and a line plot.
  assert implement_linear_regression('data.csv')==None
  assert implement_linear_regression('another_data.csv')==None
  assert implement_linear_regression('empty_data.csv')==None
  assert implement_linear_regression('invalid_data.csv')==None
output: def implement_linear_regression(filename):
      import pandas as pd
      import numpy as np
      import matplotlib.pyplot as plt
      from sklearn.linear_model import LinearRegression
  
      try:
          data = pd.read_csv(filename)
          X = data.iloc[:, 0].values.reshape(-1, 1)
          y = data.iloc[:, 1].values.reshape(-1, 1)
  
          model = LinearRegression()
          model.fit(X, y)
  
          y_pred = model.predict(X)
  
          plt.scatter(X, y, color='blue')
          plt.plot(X, y_pred, color='red')
          plt.show()
      except:
          pass