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#!pip install jovian --upgrade --quiet
#!pip install pandas numpy sklearn matplotlib
import matplotlib.pyplot
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
data=pd.read_csv('netflix_titles.csv')
interested=data['description']
interested.isna().sum()
0
import re
def clean(text):
    text = text.lower()
    text=text.split(' ')
    text=str(text).split(',')

data.set_index('title', inplace = True)
interested.apply(lambda x: clean(x))
interested
0       Before planning an awesome wedding for his gra...
1       Jandino Asporaat riffs on the challenges of ra...
2       With the help of three human allies, the Autob...
3       When a prison ship crash unleashes hundreds of...
4       When nerdy high schooler Dani finally attracts...
                              ...                        
6229    This parody of first-person shooter games, mil...
6230    Marc Maron stars as Marc Maron, who interviews...
6231    Nursery rhymes and original music for children...
6232    Set during the Russian Revolution, this comic ...
6233    This hit sitcom follows the merry misadventure...
Name: description, Length: 6234, dtype: object