input: Write a python function to predict the price of a house using a linear regression model trained on a dataset with features such as number of rooms, bathrooms, square footage, and area.
  assert predict_price(LinearRegression(), [[5, 1, 1500, 'rural']]) == 'Predicted price: $<some_value>'
  assert predict_price(LinearRegression(), [[3, 2, 1200, 'urban']]) == 'Predicted price: $<some_value>'
  assert predict_price(LinearRegression(), [[4, 3, 2000, 'suburban']]) == 'Predicted price: $<some_value>'
output: def predict_price(model, features):
      from sklearn.linear_model import LinearRegression
      import pandas as pd
  
      # Mapping for area
      area_mapping = {'rural': 0, 'suburban': 1, 'urban': 2}
  
      # Extract and transform the features
      transformed_features = []
      for feature in features:
          rooms, bathrooms, square_footage, area = feature
          transformed_area = area_mapping[area]
          transformed_features.append([rooms, bathrooms, square_footage, transformed_area])
  
      # Convert to DataFrame for consistency
      X = pd.DataFrame(transformed_features, columns=['rooms', 'bathrooms', 'square_footage', 'area'])
  
      # Train a dummy model (in real scenario, the model should be pre-trained and loaded)
      # Here, we are creating and training a dummy model for the sake of completeness
      dummy_data = pd.DataFrame({
          'rooms': [2, 3, 4, 5],
          'bathrooms': [1, 2, 2, 3],
          'square_footage': [1000, 1200, 1500, 2000],
          'area': [0, 2, 1, 1],
          'price': [200000, 300000, 400000, 500000]
      })
      X_dummy = dummy_data[['rooms', 'bathrooms', 'square_footage', 'area']]
      y_dummy = dummy_data['price']
      model.fit(X_dummy, y_dummy)
  
      # Predict the price
      predicted_price = model.predict(X)[0]
  
      return f'Predicted price: ${predicted_price:.2f}'