input: Write a function to split a given dataset into training and testing sets and evaluate the accuracy of a Linear Regression model.
  assert evaluate_model(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])) > 0
  assert evaluate_model(np.array([[1, 0.1], [2, 0.2], [3, 0.3], [4, 0.4], [5, 0.5]])) > 0
  assert evaluate_model(np.array([[1, 100], [2, 200], [3, 300], [4, 400], [5, 500]])) > 0
output: from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LinearRegression
  from sklearn.metrics import accuracy_score
  import numpy as np
  
  def evaluate_model(data):
      X = data[:, :-1]
      y = data[:, -1]
      
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      model = LinearRegression()
      model.fit(X_train, y_train)
      
      y_pred = model.predict(X_test)
      y_pred_rounded = np.round(y_pred)
      
      return accuracy_score(y_test, y_pred_rounded)