4OH4

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel

search_terms = 'fruit and vegetables'
documents = ['cars drive on the road', 'tomatoes are actually fruit']

doc_vectors = TfidfVectorizer().fit_transform([search_terms] + documents)

cosine_similarities = linear_kernel(doc_vectors[0:1], doc_vectors).flatten()
document_scores = [item.item() for item in cosine_similarities[1:]]

4OH4

import os
import sqlite3

class DAO(object):
    """
        SQLite3 Data Access Object

        Usage:
        >>> dao = DAO('example.db')
        Database connection initialised

4OH4

import nltk
nltk.download('wordnet')

from nltk.corpus import wordnet
print(wordnet.get_version())