input: Write a python function to create a linear regression model to predict house prices from a given CSV file.
  assert predict_house_prices('housing_data.csv') is not None
  assert isinstance(predict_house_prices('housing_data.csv'), np.ndarray)
  assert len(predict_house_prices('housing_data.csv')) > 0
output: def predict_house_prices(csv_file):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LinearRegression
      import numpy as np
  
      # Load the data
      data = pd.read_csv(csv_file)
  
      # Assuming the CSV has columns 'features' and 'price'
      # Separate the features and the target variable
      X = data.drop(columns=['price'])
      y = data['price']
  
      # Split the data into training and test 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 house prices on the test set
      predictions = model.predict(X_test)
  
      return predictions