Text Classification.py

import sklearn.datasets as skd

categories = ['alt.atheism', 'soc.religion.christian','comp.graphics', 'sci.med']
news_train = skd.load_files('/home/ayush/Desktop/Fetch20newsgroup/train', categories= categories, encoding= 'ISO-8859-1')
news_test = skd.load_files('/home/ayush/Desktop/Fetch20newsgroup/test/',categories= categories, encoding= 'ISO-8859-1')

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

text_clf = Pipeline([('vect', TfidfVectorizer()), 
                      ('clf', MultinomialNB()) ])

# train the model
text_clf.fit(news_train.data, news_train.target)
# Predict the test cases
predicted = text_clf.predict(news_test.data)

from sklearn import metrics
from sklearn.metrics import accuracy_score
import numpy as np

print('Accuracy achieved is ' + str(np.mean(predicted == news_test.target)))
print(metrics.classification_report(news_test.target, predicted, target_names=news_test.target_names)),
metrics.confusion_matrix(news_test.target, predicted)