input: Write a Python function to split the dataset into training and testing sets, train a linear regression model, and predict the house prices. 
  assert train_and_predict_house_prices(data) is not None
  assert 'Predicted House Price' in train_and_predict_house_prices(data).columns
output: def train_and_predict_house_prices(data):
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LinearRegression
      import pandas as pd
      
      # Assuming 'data' is a pandas DataFrame with the last column as the target variable (house prices)
      X = data.iloc[:, :-1]  # Features
      y = data.iloc[:, -1]   # Target
      
      # 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)
      
      # Train a linear regression model
      model = LinearRegression()
      model.fit(X_train, y_train)
      
      # Predict the house prices
      y_pred = model.predict(X_test)
      
      # Add the predictions to the test set
      results = X_test.copy()
      results['Predicted House Price'] = y_pred
      
      return results