input: Write a function that reads in a tweet and removes all punctuation, special characters and stopwords. The function should return a list of filtered words.
  
  def clean_tweet(tweet):
      import re
      import string
      from nltk.corpus import stopwords
      
      tweet_clean = re.sub(r'[^\w\s]','',tweet) #remove punctuation
      tokens = tweet_clean.split() #split string into individual words 
      stop_words = set(stopwords.words("english")) #get list of stopwords
      filtered_tokens = [w for w in tokens if not w in stop_words] #remove stopwords
      return filtered_tokens
  
  assert clean_tweet("I'm so excited to be going to the #MuseumTour! #BigDay #travel") == ['excited', 'going', 'MuseumTour', 'BigDay', 'travel']
  assert clean_tweet("") == []
  assert clean_tweet("Hello World!") == ['Hello', 'World']
  assert clean_tweet("The quick brown fox jumps over the lazy dog.") == ['quick', 'brown', 'fox', 'jumps', 'lazy', 'dog']
output: def clean_tweet(tweet):
      import re
      import string
      from nltk.corpus import stopwords
      
      tweet_clean = re.sub(r'[^\w\s]','',tweet)
      tokens = tweet_clean.split()
      stop_words = set(stopwords.words("english"))
      filtered_tokens = [w for w in tokens if not w in stop_words]
      return filtered_tokens