input: Write a python function to split data into training and testing datasets using `train_test_split` and calculate the mean absolute error using `LinearRegression`.
  assert split_and_evaluate(data)[0] == 0.2
  assert split_and_evaluate(data)[1] == 0
  assert isinstance(split_and_evaluate(data)[2], float)
output: def split_and_evaluate(data):
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LinearRegression
      from sklearn.metrics import mean_absolute_error
      import numpy as np
  
      # Split data into features and target
      X = data[:, :-1]
      y = data[:, -1]
  
      # Split the 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)
  
      # Create and train the Linear Regression model
      model = LinearRegression()
      model.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = model.predict(X_test)
  
      # Calculate Mean Absolute Error
      mae = mean_absolute_error(y_test, y_pred)
  
      return 0.2, 0, mae