Fake News Detection
Project by Gireeshee Pendela
📌 Project Overview
This project aims to detect whether a given news article is real or fake using Natural Language Processing (NLP) and supervised machine learning models.
We use a labeled dataset of real and fake news articles to: - Preprocess and clean the text - Convert it into numerical features using TF-IDF Vectorization - Train models such as Logistic Regression and Multinomial Naive Bayes - Evaluate performance using standard classification metrics - Visualize insights like confusion matrices, word clouds, and top predictive words
🔍 Problem Statement
Fake news can spread rapidly and influence public opinion. This project focuses on building a model that can automatically distinguish between fake and real news articles based on textual content.
📦 Dataset
The dataset consists of: - Fake.csv: News articles labeled as fake - True.csv: News articles labeled as real
The two datasets are combined and labeled (0 = fake, 1 = real), then cleaned and preprocessed for modeling.
🛠️ Technologies Used
- Python (Pandas, NumPy, Scikit-learn)
- Natural Language Toolkit (NLTK)
- Matplotlib, Seaborn
- WordCloud
- Quarto (for documentation and static site)