Fake News Classification Case Study

This repository contains a Jupyter Notebook demonstrating a case study on fake news detection using Natural Language Processing (NLP) and machine learning.

Overview

The notebook covers:

• Data cleaning and preprocessing
• Tokenization and lemmatization
• Feature extraction using Bag-of-Words
• Model training and evaluation using:
  • Logistic Regression
  • Support Vector Machine (SVM)
• Exploratory data analysis with visualizations (Seaborn)

Purpose

This project is part of a learning exercise to understand:

• Text preprocessing for NLP
• Transforming text into machine learning-ready features
• Comparing performance of different ML classifiers

Dataset

• Publicly available dataset
• Contains news articles labeled as Fake or Real

Usage

1. Clone the repository
2. Open the notebook in Jupyter
3. Run each cell sequentially to reproduce the analysis

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This repository is for educational purposes and showcases my learning in NLP and text classification.