Fake News Detection Using LSTM | Project-Based Learning (UE23CS3505) | Deep Learning Project

This video showcases our Project-Based Learning (PBL) work for the course UE23CS3505, where we developed a Fake News Detection System using LSTM (Long Short-Term Memory) — a powerful Deep Learning model capable of understanding text context and sequence. Our system identifies whether a given news article is Real or Fake using Word2Vec embeddings for semantic meaning and LSTM for contextual learning. ✨ Project Highlights: ✅ Data preprocessing and text cleaning (using Python & NLP) ✅ Word2Vec for word embeddings ✅ LSTM model for sequential pattern learning ✅ Visualization of results (✅ Real / ❌ Fake) ✅ Practical demo with test news samples 🔍 Technologies Used: Python | TensorFlow | Keras | Gensim | Word2Vec | NLP | LSTM | Pandas | Matplotlib 🎯 Objective: To design an intelligent system that automatically detects fake or misleading news articles using natural language understanding. 🎓 Guide / Instructor: Mrs. Nayana K Assistant Professor Department of CSE GMU 📘 Course: Project-Based Learning (PBL) — UE23CS3505 #FakeNewsDetection #DeepLearning #LSTM #NLP #PythonProject #Word2Vec #AIProject #MachineLearning #PBL #UE23CS3505