Fake-and-True-News-Detection-using-NLP

Goal

The objective is to build a deep learning model that classifies news article into fake and true news using NLP principles and Gloves embedding.

I managed to build a model made up of LSTM units and achieved 99.7 accuracy on the validation set when provided the article title and body.

data_cleaning.ipynb: the notebook where data cleaning was carried out
completed-fake-true-news: the final notebook where the model was developed
True.csv, Fake.csv & fake_news_dataset : the data sources
glove.6B.100d: https://www.kaggle.com/datasets/danielwillgeorge/glove6b100dtxt, the glove emdedding file (coudn't add this file because of gits size limit, hence the link to download it)

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
Fake.csv		Fake.csv
README.md		README.md
True.csv		True.csv
clean_fake_news_dataset.csv		clean_fake_news_dataset.csv
completed-fake-true-news.ipynb		completed-fake-true-news.ipynb
data_cleaning.ipynb		data_cleaning.ipynb
fake_news_dataset.csv		fake_news_dataset.csv