Fake-news-Detection

This project implements a machine learning model using Logistic Regression to detect whether a given news article is real or fake. It processes a dataset of news articles, vectorizes the text using TF-IDF, and applies text preprocessing techniques such as stemming and stopword removal.

Features:

• Preprocessing of news data using stemming and stopword removal.
• Vectorization of text data with TF-IDF.
• Logistic Regression model for classification of fake and real news.
• Command-line interface for user input to check the authenticity of news articles.

Dataset

The dataset used is train.csv, which contains the following columns:

• id: Unique identifier for each article.
• title: Title of the news article.
• author: Author of the article.
• text: Full text of the article.
• label: Target label (1: Fake, 0: Real)

Dependencies

• Python 3.x
• NumPy
• pandas
• scikit-learn
• nltk
• Pytorch

Setup Instructions

1. Clone the repository:

   git clone https://github.com/jaiwantD/Fake-news-Detection

2. Create a virutal enivronment
3. Add the dependencies (Enssure GPU is enabled)

   pip install numpy pandas scikit-learn nltk pytorch