input: Write a python function to perform linear regression on the Boston dataset and make predictions.
  assert predict_boston([[1, 2, 1, 50, 6.5], [8, 4, 3, 60, 4.0], [3, 4, 4, 65, 11.3]]) == lr.predict([[1, 2, 1, 50, 6.5], [8, 4, 3, 60, 4.0], [3, 4, 4, 65, 11.3]])
  assert predict_boston([[2, 5, 2, 55, 7.0], [7, 3, 2, 58, 3.5]]) == lr.predict([[2, 5, 2, 55, 7.0], [7, 3, 2, 58, 3.5]])
output: def predict_boston(data):
      from sklearn.datasets import load_boston
      from sklearn.linear_model import LinearRegression
      from sklearn.model_selection import train_test_split
  
      # Load the dataset
      boston = load_boston()
      X, y = boston.data, boston.target
  
      # Split the dataset 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)
  
      # Initialize the Linear Regression model
      lr = LinearRegression()
  
      # Fit the model on the training data
      lr.fit(X_train, y_train)
  
      # Make predictions on the given data
      predictions = lr.predict(data)
  
      return predictions