input: Write a function to predict house prices given a dataset and features using linear regression in Python.
  assert np.isclose(predict_house_prices('houses_data.csv', ['size', 'age', 'num_bedrooms'], 'price'), mean_squared_error)
  assert np.isclose(predict_house_prices('houses_data_example.csv', ['sqft', 'years_old', 'bedrooms'], 'listing_price'), mean_squared_error)
  assert np.isclose(predict_house_prices('real_estate_data.csv', ['area', 'construction_year', 'rooms'], 'market_value'), mean_squared_error)
output: import numpy as np
  import pandas as pd
  from sklearn.linear_model import LinearRegression
  from sklearn.metrics import mean_squared_error
  
  def predict_house_prices(dataset_path, feature_columns, target_column):
      # Load the dataset
      data = pd.read_csv(dataset_path)
  
      # Extract features and target
      X = data[feature_columns]
      y = data[target_column]
  
      # Initialize and fit the linear regression model
      model = LinearRegression()
      model.fit(X, y)
  
      # Make predictions
      predictions = model.predict(X)
  
      # Calculate mean squared error
      mse = mean_squared_error(y, predictions)
      
      return mse