Text Classification - Naive Bayes - Product Summary

# Disclaimer: some code copied form this https://towardsdatascience.com/multi-class-text-classification-model-comparison-and-selection-5eb066197568

import logging
import pandas as pd
import numpy as np
from numpy import random
import gensim
import nltk
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer

from sklearn.metrics import accuracy_score, confusion_matrix
import matplotlib.pyplot as plt
from nltk.corpus import stopwords
import re
from bs4 import BeautifulSoup
from io import BytesIO
import requests

filename = 'https://docs.google.com/spreadsheet/ccc?key=1sN_OwRH8evSuMjjpjUJTonuJ3vKWC5iZj8yiEAF728k&output=csv'

r = requests.get(filename)
data = r.content

df = pd.read_csv(BytesIO(data))

df.head()

# remove not null

df = df[pd.notnull(df['category'])]

df.describe()

df.shape

# Count words

df.head(10)

df = df[pd.notnull(df['summary'])]


df['summary']

# count words
df['summary'].apply ( lambda x: len(x.split(' ')) ).sum()

REPLACE_BY_SPACE_RE = re.compile('[/(){}\[\]\|@,;]')
BAD_SYMBOLS_RE = re.compile('[^0-9a-z #+_]')
STOPWORDS = set(stopwords.words('english'))

def clean_text(text):
    """
        text: a string
        
        return: modified initial string
    """
    text = BeautifulSoup(text, "lxml").text # HTML decoding
    text = text.lower() # lowercase text
    text = REPLACE_BY_SPACE_RE.sub(' ', text) # replace REPLACE_BY_SPACE_RE symbols by space in text
    text = BAD_SYMBOLS_RE.sub('', text) # delete symbols which are in BAD_SYMBOLS_RE from text
    text = ' '.join(word for word in text.split() if word not in STOPWORDS) # delete stopwors from text
    return text

df['summary'] = df['summary'].apply(clean_text)

# Check the words count again

df['summary'].apply ( lambda x: len(x.split(' ')) ).sum()

X = df.summary
y = df.category

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Using NB

from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfTransformer

nb = Pipeline([('vect', CountVectorizer()),
               ('tfidf', TfidfTransformer()),
               ('clf', MultinomialNB()),
              ])
nb.fit(X_train, y_train)

nb.fit(X_train, y_train)

from sklearn.metrics import classification_report
y_pred = nb.predict(X_test)

print('accuracy %s' % accuracy_score(y_pred, y_test))