📰 Fake News Detector using Machine Learning

A Fake News Detection Web App built using Python, Scikit-learn, and Streamlit.
It classifies news articles as Real or Fake based on their textual content.

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🚀 Live Demo

👉 Click here to try the app

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🧠 Overview

This project uses Natural Language Processing (NLP) and a Machine Learning model trained on real-world news datasets to detect whether a piece of news is genuine or fake.

Users can paste any headline or article text into the app, and it will instantly predict the authenticity with a confidence score.

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⚙️ How It Works

1. The dataset contains two files:

   • Fake.csv
   • True.csv

2. Data was preprocessed and combined into one dataframe.

3. Text features were extracted using TF-IDF Vectorization.

4. A PassiveAggressiveClassifier was trained to distinguish fake vs. real news.

5. The final trained model and vectorizer were saved as:

   • model.pkl
   • vectorizer.pkl

6. These are used in the Streamlit web app for real-time prediction.

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🧰 Tech Stack

Technology	Purpose
Python	Programming language
Scikit-learn	Machine learning algorithms
Pandas / NumPy	Data processing
TF-IDF Vectorizer	Feature extraction
Streamlit	Web app interface

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🧾 Installation & Usage

1️⃣ Clone this repository

git clone https://github.com/<your-username>/fake-news-detector.git
cd fake-news-detector

###🌐 Deployment

The project is deployed using Streamlit Cloud.
Once deployed, users can access it directly through a shareable URL.

🧑‍💻 Author

Aayush Dhote
💼 Aspiring AI Engineer | Data Science & Machine Learning Enthusiast]
❤️ Acknowledgements

Streamlit

Scikit-learn Documentation

Dataset: Kaggle – Fake and True News