PR FAKE NEWS

Fake News Detection Project

This project aims to detect fake vs. real news articles using machine learning and natural language processing (NLP) techniques.

Dataset: Fake and Real News Dataset from Kaggle (~45,000 articles).

Process:

Load and merge real and fake news datasets.

Assign binary labels (0 = Fake, 1 = Real).

Perform data cleaning and preprocessing (handling text, stopwords, etc.).

Apply vectorization techniques (e.g., TF-IDF, CountVectorizer).

Train machine learning models (Logistic Regression, Naïve Bayes, Random Forest, or deep learning models).

Evaluate performance with metrics like accuracy, precision, recall, and F1-score.

Technologies Used:

Python (Pandas, NumPy, Scikit-learn, NLTK)

Machine Learning models for classification

Kaggle Dataset

Goal: To build an automated system that can classify news articles as fake or real, helping combat misinformation and promoting trustworthy information sources.