fake_news_detection.py

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.metrics import accuracy_score, confusion_matrix

df = pd.read_csv('/home/lp-n-12/Downloads/news.csv')

labels = df['label']
x_train, x_test, y_train, y_test = train_test_split(df['text'], labels, test_size=0.2, random_state=7)

tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_df=0.7)

tfidf_train = tfidf_vectorizer.fit_transform(x_train)
tfidf_test = tfidf_vectorizer.transform(x_test)

pac = PassiveAggressiveClassifier(max_iter=50)
pac.fit(tfidf_train, y_train)

y_pred = pac.predict(tfidf_test)
score = accuracy_score(y_test, y_pred)
print(f'Accuracy: {round(score*100, 2)}%')

con = confusion_matrix(y_test, y_pred, labels=['FAKE', 'REAL'])
print("confusion matrix", con)