As a first step, create a list of all possible matches, comparing 'italian' with the restaurant types listed in the cuis

Here's the code to create a list of all possible matches, comparing 'italian' with the restaurant types listed in the `cuisine_type` column: ```python # Create a list of matches, comparing 'italian' with the cuisine_type column matches = restaurants['cuisine_type'].apply(lambda x: fuzz.token_sort_ratio('italian', x)) # Inspect the first 5 matches print(matches[0:5]) ``` In this code, we use the `.apply()` method to apply the `fuzz.token_sort_ratio()` function to each value in the `cuisine_type` column of the `restaurants` DataFrame. The `fuzz.token_sort_ratio()` function calculates the similarity between two strings by tokenizing them and sorting the tokens alphabetically before calculating the ratio of matching tokens. We pass 'italian' as the first argument to `fuzz.token_sort_ratio()`, and `x` (each value in the `cuisine_type` column) as the second argument. We store the resulting match scores in a new variable called `matches`. We then print the first 5 matches using the `print()` function and slicing the `matches` list from index 0 to 5.