Fake News Prediction

A machine learning project that identifies fake news articles using Natural Language Processing (NLP) and Logistic Regression. This system includes a Streamlit web app for real-time predictions and a Jupyter notebook for model training and evaluation.

Key Highlights:

* Fake News Classification: Detects whether a news article is Real or Fake using a trained Logistic Regression model.

* Advanced NLP Pipeline: Performs text cleaning, stemming, stopword removal, and TF-IDF vectorization for effective feature extraction.

* Interactive Web App: Built with Streamlit, featuring a dark-themed, user-friendly interface for instant verification.

* Real-time Predictions: Instantly classifies news articles entered by users.

* Retrainable Model: Includes a Jupyter Notebook for retraining the model on new datasets (e.g., WELFake).

* Transparent Performance: Evaluated using accuracy, precision, recall, F1-score, and confusion matrix.

Technologies Used: Python, Streamlit, scikit-learn, NLTK, Pandas, NumPy, Matplotlib, Pickle.

Usage: Enter a news title and content, click “ Predict”, and instantly see whether the article is real or fake — with the option to start a new prediction anytime.