Fake News.py

import numpy as np
import pandas as pd
import itertools
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

# Load the dataset
df = pd.read_csv('D:\\DataFlair\\news.csv')

# Get shape and head
print(df.shape)
print(df.head())

labels = df.label

# DataFlair - Split the dataset
x_train, x_test, y_train, y_test = train_test_split(df['text'], labels, test_size=0.2, random_state=7)

# DataFlair - Initialize a TfidfVectorizer
tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_df=0.7)

# DataFlair - Fit and transform train set, transform test set
tfidf_train = tfidf_vectorizer.fit_transform(x_train)
tfidf_test = tfidf_vectorizer.transform(x_test)

# DataFlair - Initialize a PassiveAggressiveClassifier
pac = PassiveAggressiveClassifier(max_iter=50)
pac.fit(tfidf_train, y_train)

# DataFlair - Predict on the test set and calculate accuracy
y_pred = pac.predict(tfidf_test)
score = accuracy_score(y_test, y_pred)
print(f'Accuracy: {round(score * 100, 2)}%')

# DataFlair - Build confusion matrix
conf_matrix = confusion_matrix(y_test, y_pred, labels=['FAKE', 'REAL'])
print(conf_matrix)