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Fake News Classifier.py

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+import numpy as np
+import pandas as pd
+import seaborn as sb
+import matplotlib.pyplot as plt
+from sklearn import metrics
+from sklearn.metrics import classification_report,confusion_matrix,accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import PassiveAggressiveClassifier
+
+#reading data into pandas dataframe
+df=pd.read_csv('news.csv')
+
+#printing first 5 entries in the table/dataframe
+print(df.head())
+
+#deciding predicate and predicator variables
+x=df['text']
+y=df['label']
+
+#splitting data into train dataset and test dataset
+x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=0)
+
+#vectorization and fitting data 
+tf_vect=TfidfVectorizer(stop_words='english',max_df=0.6)
+tf_train=tf_vect.fit_transform(x_train) 
+tf_test=tf_vect.transform(x_test)
+
+#predicting lables of test data using PassiveAgrressiveClassifier
+passiveac=PassiveAggressiveClassifier(max_iter=60) #more the value of max_iter, more accuracy can be obtained
+passiveac.fit(tf_train,y_train)
+pred=passiveac.predict(tf_test)
+
+#calculating accuracy
+
+print(classification_report(y_test, pred))
+
+print(confusion_matrix(y_test,pred))
+
+print(accuracy_score(y_test,pred))
+
+#Fake News classifier model