# This algorithm demonstrates how to apply list comprehension in Python

# Criteria that must be met for an entry to be returned
target_values = [{'body_style': 'SUV','drive_train': '4WD','price_range': '$30k-$40k'},
                 {'body_style': 'Sedan','drive_train':'Front','price_range': '$20k-$30k'}]

# Sample dataset of cars
sample_dataset = [{'make': 'Acura','model': 'MDX','body_style': 'SUV','drive_train': '4WD','price_range':'$40k-$50k'},
                  {'make': 'Acura','model': 'RSX','body_style': 'Sedan','drive_train': 'Front','price_range':'$20k-$30k'},
                  {'make': 'Acura','model': 'TSX','body_style': 'Sedan','drive_train': 'Front','price_range':'$20k-$30k'},
                  {'make': 'Acura','model': 'TL','body_style': 'Sedan','drive_train': 'Front','price_range':'$30k-$40k'},
                  {'make': 'Acura','model': 'RL','body_style': 'Sedan','drive_train': 'Front','price_range':'$40k-$50k'},
                  {'make': 'Honda','model': 'CR-V','body_style': 'SUV','drive_train': '4WD','price_range':'$30k-$40k'},
                  {'make': 'Honda','model': 'Pilot','body_style': 'SUV','drive_train': '4WD','price_range':'$40k-$50k'},
                  {'make': 'Honda','model': 'Civic','body_style': 'Sedan','drive_train': 'Front','price_range':'$10k-$20k'},
                  {'make': 'Honda','model': 'Accord','body_style': 'Sedan','drive_train': 'Front','price_range':'$20k-$30k'}]

# Array to store results
matching_values = []

# Search function
def search(body_style, drive_train, price_range, data):
    search_result = [element for element in data if element['body_style'] == body_style and element['drive_train'] == drive_train and element['price_range'] == price_range]
    return search_result

# Iterate over dataset to find cars that meet criteria
for i in sample_dataset:
    search_result = search(i['body_style'], i['drive_train'], i['price_range'], target_values)
    if search_result:
      matching_values.append(i)