Python script for Fake News Classification using TF-IDF vectorization, Logistic Regression, and Passive Aggressive Classifier. Dataset preprocessing includes tokenization, stemming, and stopword removal. Achieves high accuracy in distinguishing between genuine and fake news.
This repository contains Python code for classifying news articles as fake or genuine. It uses a combination of NLTK for text processing and scikit-learn for machine learning.
- Clone the repository.
- Install the necessary libraries using
pip install -r requirements.txt. - Run
fake_news_classification.pyto train and test the models.
The dataset consists of fake and genuine news articles.
The preprocessing steps include tokenization, stemming, and removing stop words.
Two models are implemented: Logistic Regression and Passive Aggressive Classifier.
- Logistic Regression Accuracy: XX%
- Passive Aggressive Classifier Accuracy: XX%
GANESH ADIMALUPU