FakeNewsCount-vectorizer.ipynb

Fake News Classifier
Dataset: https://www.kaggle.com/c/fake-news/data#

import pandas as pd
df=pd.read_csv('fake-news/train.csv')
df.head()
id	title	author	text	label
0	0	House Dem Aide: We Didn’t Even See Comey’s Let...	Darrell Lucus	House Dem Aide: We Didn’t Even See Comey’s Let...	1
1	1	FLYNN: Hillary Clinton, Big Woman on Campus - ...	Daniel J. Flynn	Ever get the feeling your life circles the rou...	0
2	2	Why the Truth Might Get You Fired	Consortiumnews.com	Why the Truth Might Get You Fired October 29, ...	1
3	3	15 Civilians Killed In Single US Airstrike Hav...	Jessica Purkiss	Videos 15 Civilians Killed In Single US Airstr...	1
4	4	Iranian woman jailed for fictional unpublished...	Howard Portnoy	Print \nAn Iranian woman has been sentenced to...	1
## Get the Independent Features

X=df.drop('label',axis=1)
X.head()
id	title	author	text
0	0	House Dem Aide: We Didn’t Even See Comey’s Let...	Darrell Lucus	House Dem Aide: We Didn’t Even See Comey’s Let...
1	1	FLYNN: Hillary Clinton, Big Woman on Campus - ...	Daniel J. Flynn	Ever get the feeling your life circles the rou...
2	2	Why the Truth Might Get You Fired	Consortiumnews.com	Why the Truth Might Get You Fired October 29, ...
3	3	15 Civilians Killed In Single US Airstrike Hav...	Jessica Purkiss	Videos 15 Civilians Killed In Single US Airstr...
4	4	Iranian woman jailed for fictional unpublished...	Howard Portnoy	Print \nAn Iranian woman has been sentenced to...
## Get the Dependent features
y=df['label']
y.head()
0    1
1    0
2    1
3    1
4    1
Name: label, dtype: int64
df.shape
(18285, 5)
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer
df=df.dropna()
df.head(10)
id	title	author	text	label
0	0	House Dem Aide: We Didn’t Even See Comey’s Let...	Darrell Lucus	House Dem Aide: We Didn’t Even See Comey’s Let...	1
1	1	FLYNN: Hillary Clinton, Big Woman on Campus - ...	Daniel J. Flynn	Ever get the feeling your life circles the rou...	0
2	2	Why the Truth Might Get You Fired	Consortiumnews.com	Why the Truth Might Get You Fired October 29, ...	1
3	3	15 Civilians Killed In Single US Airstrike Hav...	Jessica Purkiss	Videos 15 Civilians Killed In Single US Airstr...	1
4	4	Iranian woman jailed for fictional unpublished...	Howard Portnoy	Print \nAn Iranian woman has been sentenced to...	1
5	5	Jackie Mason: Hollywood Would Love Trump if He...	Daniel Nussbaum	In these trying times, Jackie Mason is the Voi...	0
7	7	Benoît Hamon Wins French Socialist Party’s Pre...	Alissa J. Rubin	PARIS — France chose an idealistic, traditi...	0
9	9	A Back-Channel Plan for Ukraine and Russia, Co...	Megan Twohey and Scott Shane	A week before Michael T. Flynn resigned as nat...	0
10	10	Obama’s Organizing for Action Partners with So...	Aaron Klein	Organizing for Action, the activist group that...	0
11	11	BBC Comedy Sketch "Real Housewives of ISIS" Ca...	Chris Tomlinson	The BBC produced spoof on the “Real Housewives...	0
messages=df.copy()
messages.reset_index(inplace=True)
messages.head(10)
index	id	title	author	text	label
0	0	0	House Dem Aide: We Didn’t Even See Comey’s Let...	Darrell Lucus	House Dem Aide: We Didn’t Even See Comey’s Let...	1
1	1	1	FLYNN: Hillary Clinton, Big Woman on Campus - ...	Daniel J. Flynn	Ever get the feeling your life circles the rou...	0
2	2	2	Why the Truth Might Get You Fired	Consortiumnews.com	Why the Truth Might Get You Fired October 29, ...	1
3	3	3	15 Civilians Killed In Single US Airstrike Hav...	Jessica Purkiss	Videos 15 Civilians Killed In Single US Airstr...	1
4	4	4	Iranian woman jailed for fictional unpublished...	Howard Portnoy	Print \nAn Iranian woman has been sentenced to...	1
5	5	5	Jackie Mason: Hollywood Would Love Trump if He...	Daniel Nussbaum	In these trying times, Jackie Mason is the Voi...	0
6	7	7	Benoît Hamon Wins French Socialist Party’s Pre...	Alissa J. Rubin	PARIS — France chose an idealistic, traditi...	0
7	9	9	A Back-Channel Plan for Ukraine and Russia, Co...	Megan Twohey and Scott Shane	A week before Michael T. Flynn resigned as nat...	0
8	10	10	Obama’s Organizing for Action Partners with So...	Aaron Klein	Organizing for Action, the activist group that...	0
9	11	11	BBC Comedy Sketch "Real Housewives of ISIS" Ca...	Chris Tomlinson	The BBC produced spoof on the “Real Housewives...	0
messages['title'][6]
'Benoît Hamon Wins French Socialist Party’s Presidential Nomination - The New York Times'
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
ps = PorterStemmer()
corpus = []
for i in range(0, len(messages)):
    review = re.sub('[^a-zA-Z]', ' ', messages['title'][i])
    review = review.lower()
    review = review.split()
    
    review = [ps.stem(word) for word in review if not word in stopwords.words('english')]
    review = ' '.join(review)
    corpus.append(review)
corpus[3]
'civilian kill singl us airstrik identifi'
## Applying Countvectorizer
# Creating the Bag of Words model
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(max_features=5000,ngram_range=(1,3))
X = cv.fit_transform(corpus).toarray()
X.shape
(18285, 5000)
y=messages['label']
## Divide the dataset into Train and Test
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=0)
cv.get_feature_names()[:20]
['abandon',
 'abc',
 'abc news',
 'abduct',
 'abe',
 'abedin',
 'abl',
 'abort',
 'abroad',
 'absolut',
 'abstain',
 'absurd',
 'abus',
 'abus new',
 'abus new york',
 'academi',
 'accept',
 'access',
 'access pipelin',
 'access pipelin protest']
cv.get_params()
{'analyzer': 'word',
 'binary': False,
 'decode_error': 'strict',
 'dtype': numpy.int64,
 'encoding': 'utf-8',
 'input': 'content',
 'lowercase': True,
 'max_df': 1.0,
 'max_features': 5000,
 'min_df': 1,
 'ngram_range': (1, 3),
 'preprocessor': None,
 'stop_words': None,
 'strip_accents': None,
 'token_pattern': '(?u)\\b\\w\\w+\\b',
 'tokenizer': None,
 'vocabulary': None}
count_df = pd.DataFrame(X_train, columns=cv.get_feature_names())
count_df.head()
abandon	abc	abc news	abduct	abe	abedin	abl	abort	abroad	absolut	...	zero	zika	zika viru	zionist	zone	zone new	zone new york	zoo	zu	zuckerberg
0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
4	0	0	0	0	0	0	0	0	0	1	...	0	0	0	0	0	0	0	0	0	0
5 rows × 5000 columns

import matplotlib.pyplot as plt
def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    See full source and example: 
    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
    
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
MultinomialNB Algorithm
from sklearn.naive_bayes import MultinomialNB
classifier=MultinomialNB()
from sklearn import metrics
import numpy as np
import itertools
classifier.fit(X_train, y_train)
pred = classifier.predict(X_test)
score = metrics.accuracy_score(y_test, pred)
print("accuracy:   %0.3f" % score)
cm = metrics.confusion_matrix(y_test, pred)
plot_confusion_matrix(cm, classes=['FAKE', 'REAL'])
accuracy:   0.902
Confusion matrix, without normalization

classifier.fit(X_train, y_train)
pred = classifier.predict(X_test)
score = metrics.accuracy_score(y_test, pred)
score
0.9019055509527755
y_train.shape
(12250,)
Passive Aggressive Classifier Algorithm
from sklearn.linear_model import PassiveAggressiveClassifier
linear_clf = PassiveAggressiveClassifier(n_iter=50)
linear_clf.fit(X_train, y_train)
pred = linear_clf.predict(X_test)
score = metrics.accuracy_score(y_test, pred)
print("accuracy:   %0.3f" % score)
cm = metrics.confusion_matrix(y_test, pred)
plot_confusion_matrix(cm, classes=['FAKE Data', 'REAL Data'])
c:\users\krish.naik\appdata\local\continuum\anaconda3\envs\nlp\lib\site-packages\sklearn\linear_model\stochastic_gradient.py:117: DeprecationWarning: n_iter parameter is deprecated in 0.19 and will be removed in 0.21. Use max_iter and tol instead.
  DeprecationWarning)
accuracy:   0.918
Confusion matrix, without normalization

Multinomial Classifier with Hyperparameter
classifier=MultinomialNB(alpha=0.1)
previous_score=0
for alpha in np.arange(0,1,0.1):
    sub_classifier=MultinomialNB(alpha=alpha)
    sub_classifier.fit(X_train,y_train)
    y_pred=sub_classifier.predict(X_test)
    score = metrics.accuracy_score(y_test, y_pred)
    if score>previous_score:
        classifier=sub_classifier
    print("Alpha: {}, Score : {}".format(alpha,score))
Alpha: 0.0, Score : 0.8903065451532726
Alpha: 0.1, Score : 0.9020712510356255
Alpha: 0.2, Score : 0.9025683512841757
Alpha: 0.30000000000000004, Score : 0.9024026512013256
Alpha: 0.4, Score : 0.9017398508699255
Alpha: 0.5, Score : 0.9015741507870754
Alpha: 0.6000000000000001, Score : 0.9022369511184756
Alpha: 0.7000000000000001, Score : 0.9025683512841757
Alpha: 0.8, Score : 0.9015741507870754
Alpha: 0.9, Score : 0.9017398508699255
## Get Features names
feature_names = cv.get_feature_names()
classifier.coef_[0]
array([ -9.10038883,  -8.62276128,  -9.10038883, ..., -10.79498456,
        -8.91467169,  -9.32864749])
### Most real
sorted(zip(classifier.coef_[0], feature_names), reverse=True)[:20]
[(-4.000149156604985, 'trump'),
 (-4.287872694443541, 'hillari'),
 (-4.396389621061519, 'clinton'),
 (-4.899969726208735, 'elect'),
 (-5.176598600897756, 'new'),
 (-5.234730366348767, 'comment'),
 (-5.273968180973631, 'video'),
 (-5.3868167681180115, 'war'),
 (-5.396821854078974, 'us'),
 (-5.412019714988405, 'hillari clinton'),
 (-5.417137433425386, 'fbi'),
 (-5.48068448454208, 'vote'),
 (-5.566255475855405, 'email'),
 (-5.578238842742501, 'world'),
 (-5.634015380199913, 'obama'),
 (-5.734501455772904, 'donald'),
 (-5.763095255139644, 'donald trump'),
 (-5.785090276725191, 'russia'),
 (-5.846224665218559, 'day'),
 (-5.862110622807369, 'america')]
### Most fake
sorted(zip(classifier.coef_[0], feature_names))[:5000]
[(-10.794984555596727, 'abe'),
 (-10.794984555596727, 'abroad'),
 (-10.794984555596727, 'abus new'),
 (-10.794984555596727, 'abus new york'),
 (-10.794984555596727, 'act new'),
 (-10.794984555596727, 'act new york'),
 (-10.794984555596727, 'advic'),
 (-10.794984555596727, 'advis new'),
 (-10.794984555596727, 'advis new york'),
 (-10.794984555596727, 'age new'),
 (-10.794984555596727, 'age new york'),
 (-10.794984555596727, 'agenda breitbart'),
 (-10.794984555596727, 'ail'),
 (-10.794984555596727, 'aleppo new'),
 (-10.794984555596727, 'aleppo new york'),
 (-10.794984555596727, 'ali'),
 (-10.794984555596727, 'america breitbart'),
 (-10.794984555596727, 'america new york'),
 (-10.794984555596727, 'american breitbart'),
 (-10.794984555596727, 'american new'),
 (-10.794984555596727, 'american new york'),
 (-10.794984555596727, 'ami'),
 (-10.794984555596727, 'ami schumer'),
 (-10.794984555596727, 'amp'),
 (-10.794984555596727, 'ann'),
 (-10.794984555596727, 'ann coulter'),
 (-10.794984555596727, 'applaud'),
 (-10.794984555596727, 'arson'),
 (-10.794984555596727, 'asian'),
 (-10.794984555596727, 'assail'),
 (-10.794984555596727, 'athlet'),
 (-10.794984555596727, 'attack breitbart'),
 (-10.794984555596727, 'attack new'),
 (-10.794984555596727, 'attack new york'),
 (-10.794984555596727, 'attack syria'),
 (-10.794984555596727, 'attend'),
 (-10.794984555596727, 'avert'),
 (-10.794984555596727, 'away new'),
 (-10.794984555596727, 'away new york'),
 (-10.794984555596727, 'back new'),
 (-10.794984555596727, 'back new york'),
 (-10.794984555596727, 'baltimor'),
 (-10.794984555596727, 'ban breitbart'),
 (-10.794984555596727, 'ban new'),
 (-10.794984555596727, 'ban new york'),
 (-10.794984555596727, 'bangladesh'),
 (-10.794984555596727, 'barron trump'),
 (-10.794984555596727, 'basic'),
 (-10.794984555596727, 'basketbal'),
 (-10.794984555596727, 'basketbal player'),
 (-10.794984555596727, 'baton'),
 (-10.794984555596727, 'baton roug'),
 (-10.794984555596727, 'battl new'),
 (-10.794984555596727, 'battl new york'),
 (-10.794984555596727, 'bay'),
 (-10.794984555596727, 'beach new'),
 (-10.794984555596727, 'beach new york'),
 (-10.794984555596727, 'begin new'),
 (-10.794984555596727, 'behead photo'),
 (-10.794984555596727, 'behind trump'),
 (-10.794984555596727, 'berkeley breitbart'),
 (-10.794984555596727, 'betsi'),
 (-10.794984555596727, 'betsi devo'),
 (-10.794984555596727, 'bill breitbart'),
 (-10.794984555596727, 'bill new'),
 (-10.794984555596727, 'bill new york'),
 (-10.794984555596727, 'bill reilli'),
 (-10.794984555596727, 'billi'),
 (-10.794984555596727, 'blue state'),
 (-10.794984555596727, 'blue state blue'),
 (-10.794984555596727, 'bolster'),
 (-10.794984555596727, 'border patrol'),
 (-10.794984555596727, 'border patrol agent'),
 (-10.794984555596727, 'border state'),
 (-10.794984555596727, 'border wall'),
 (-10.794984555596727, 'border wall breitbart'),
 (-10.794984555596727, 'bowl'),
 (-10.794984555596727, 'box offic'),
 (-10.794984555596727, 'bradi'),
 (-10.794984555596727, 'breath'),
 (-10.794984555596727, 'breitbart news daili'),
 (-10.794984555596727, 'brief new'),
 (-10.794984555596727, 'brief new york'),
 (-10.794984555596727, 'broadway'),
 (-10.794984555596727, 'bronx'),
 (-10.794984555596727, 'brooklyn'),
 (-10.794984555596727, 'brooklyn new'),
 (-10.794984555596727, 'brooklyn new york'),
 (-10.794984555596727, 'bureaucrat'),
 (-10.794984555596727, 'caitlyn'),
 (-10.794984555596727, 'caitlyn jenner'),
 (-10.794984555596727, 'cal'),
 (-10.794984555596727, 'california today'),
 (-10.794984555596727, 'calm'),
 (-10.794984555596727, 'campaign breitbart'),
 (-10.794984555596727, 'campaign new'),
 (-10.794984555596727, 'campaign new york'),
 (-10.794984555596727, 'campu breitbart'),
 (-10.794984555596727, 'cann'),
 (-10.794984555596727, 'car new'),
 (-10.794984555596727, 'car new york'),
 (-10.794984555596727, 'care act'),
 (-10.794984555596727, 'care act new'),
 (-10.794984555596727, 'care bill'),
 (-10.794984555596727, 'care breitbart'),
 (-10.794984555596727, 'carri fisher'),
 (-10.794984555596727, 'carson'),
 (-10.794984555596727, 'case new'),
 (-10.794984555596727, 'case new york'),
 (-10.794984555596727, 'castro'),
 (-10.794984555596727, 'caucu'),
 (-10.794984555596727, 'ceas'),
 (-10.794984555596727, 'ceas fire'),
 (-10.794984555596727, 'chamber'),
 (-10.794984555596727, 'championship'),
 (-10.794984555596727, 'championship new'),
 (-10.794984555596727, 'championship new york'),
 (-10.794984555596727, 'chang new'),
 (-10.794984555596727, 'chang new york'),
 (-10.794984555596727, 'charg new york'),
 (-10.794984555596727, 'charleston'),
 (-10.794984555596727, 'charlott'),
 (-10.794984555596727, 'cher'),
 (-10.794984555596727, 'chief new'),
 (-10.794984555596727, 'chief new york'),
 (-10.794984555596727, 'china new york'),
 (-10.794984555596727, 'chri christi'),
 (-10.794984555596727, 'christi'),
 (-10.794984555596727, 'christoph'),
 (-10.794984555596727, 'chuck'),
 (-10.794984555596727, 'chuck todd'),
 (-10.794984555596727, 'circuit'),
 (-10.794984555596727, 'citi new'),
 (-10.794984555596727, 'citi new york'),
 (-10.794984555596727, 'citizenship'),
 (-10.794984555596727, 'clinton new york'),
 (-10.794984555596727, 'clue'),
 (-10.794984555596727, 'coach'),
 (-10.794984555596727, 'colleg new'),
 (-10.794984555596727, 'colleg new york'),
 (-10.794984555596727, 'collid'),
 (-10.794984555596727, 'collin'),
 (-10.794984555596727, 'comedi'),
 (-10.794984555596727, 'comedi central'),
 (-10.794984555596727, 'comey fire'),
 (-10.794984555596727, 'commission'),
 (-10.794984555596727, 'common core'),
 (-10.794984555596727, 'common ground'),
 (-10.794984555596727, 'compet'),
 (-10.794984555596727, 'complex'),
 (-10.794984555596727, 'conceal'),
 (-10.794984555596727, 'confirm hear'),
 (-10.794984555596727, 'congress breitbart'),
 (-10.794984555596727, 'congress new york'),
 (-10.794984555596727, 'control new'),
 (-10.794984555596727, 'control new york'),
 (-10.794984555596727, 'convent new'),
 (-10.794984555596727, 'convent new york'),
 (-10.794984555596727, 'corner'),
 (-10.794984555596727, 'coulter'),
 (-10.794984555596727, 'counsel'),
 (-10.794984555596727, 'countri breitbart'),
 (-10.794984555596727, 'cours'),
 (-10.794984555596727, 'court block'),
 (-10.794984555596727, 'court new'),
 (-10.794984555596727, 'court new york'),
 (-10.794984555596727, 'court rule new'),
 (-10.794984555596727, 'cpac'),
 (-10.794984555596727, 'crash new'),
 (-10.794984555596727, 'crash new york'),
 (-10.794984555596727, 'crime breitbart'),
 (-10.794984555596727, 'crisi new'),
 (-10.794984555596727, 'crisi new york'),
 (-10.794984555596727, 'critic new'),
 (-10.794984555596727, 'crown'),
 (-10.794984555596727, 'cunningham'),
 (-10.794984555596727, 'curb'),
 (-10.794984555596727, 'davo'),
 (-10.794984555596727, 'day breitbart'),
 (-10.794984555596727, 'day new york'),
 (-10.794984555596727, 'day parad'),
 (-10.794984555596727, 'dead new'),
 (-10.794984555596727, 'dead new york'),
 (-10.794984555596727, 'deal breitbart'),
 (-10.794984555596727, 'deal new'),
 (-10.794984555596727, 'deal new york'),
 (-10.794984555596727, 'death new'),
 (-10.794984555596727, 'death new york'),
 (-10.794984555596727, 'debat new'),
 (-10.794984555596727, 'debat new york'),
 (-10.794984555596727, 'debbi reynold'),
 (-10.794984555596727, 'defi'),
 (-10.794984555596727, 'defund'),
 (-10.794984555596727, 'delingpol'),
 (-10.794984555596727, 'dem rep'),
 (-10.794984555596727, 'dem sen'),
 (-10.794984555596727, 'democrat breitbart'),
 (-10.794984555596727, 'democrat new'),
 (-10.794984555596727, 'democrat new york'),
 (-10.794984555596727, 'deport breitbart'),
 (-10.794984555596727, 'despair'),
 (-10.794984555596727, 'devo'),
 (-10.794984555596727, 'dh'),
 (-10.794984555596727, 'die new'),
 (-10.794984555596727, 'die new york'),
 (-10.794984555596727, 'diego'),
 (-10.794984555596727, 'disappoint'),
 (-10.794984555596727, 'divorc'),
 (-10.794984555596727, 'donald trump breitbart'),
 (-10.794984555596727, 'donald trump campaign'),
 (-10.794984555596727, 'donald trump inaugur'),
 (-10.794984555596727, 'dossier'),
 (-10.794984555596727, 'drama'),
 (-10.794984555596727, 'drive car'),
 (-10.794984555596727, 'drug new'),
 (-10.794984555596727, 'drug new york'),
 (-10.794984555596727, 'dunham'),
 (-10.794984555596727, 'dylann'),
 (-10.794984555596727, 'dylann roof'),
 (-10.794984555596727, 'eas'),
 (-10.794984555596727, 'easter'),
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