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

Описание: 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
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