📰 Fake News Detection using Machine Learning

This project focuses on detecting fake news articles using Natural Language Processing (NLP) and Machine Learning.
It leverages the Bag-of-Words model, TF-IDF vectorization, and a Naive Bayes classifier to distinguish between True and Fake news.

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🚀 Features

• Preprocessed over 45,000 news articles (True + Fake dataset).
• Implemented text cleaning (tokenization, stopword removal, lowercasing).
• Converted text to numerical features using CountVectorizer + TF-IDF.
• Trained a Multinomial Naive Bayes classifier with ~97% accuracy.
• Built a pipeline for easy training and evaluation.
• Allows custom news input to predict whether it's Fake or True.

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🛠️ Tech Stack

• Python 3
• Pandas, NumPy – Data processing
• NLTK – NLP preprocessing
• Scikit-learn – ML models (Naive Bayes, train_test_split, TF-IDF)
• Jupyter Notebook / VS Code

📊 Model Performance

• Accuracy: ~97%
• Precision/Recall/F1: High balance across both classes (Fake & True)