Fake News Detection using Machine Learning & Neural Networks

Combating Fake news has become one of greatest challenges on the Social Media today. Everyday millions of people are affected from Fake News causing real impacts within minutes as Fake news spreads like a wildfire. Generally, Fake news refers to false, inaccurate, or misleading information. The goal of this project is to build Machine Learning & Deep Learning Models that classifies the news as fake or Real.

Dataset

Source: https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset?select=True.csv

The models are built using Real and Fake news datasets. Each dataset contains the following columns:

• title: Title of the Article
• text: Text of the Article
• subject: Subject of the Article
• date: When the article was posted

Notebook

Please access the Fake News Detection using Machine Learning & Neural Networks.ipynb file to view the work done

Technologies

1. Python
2. Jupyter Notebook

Libraries

1. Pandas
2. Sckit-Learn
3. Numpy
4. Matplotlib
5. Seaborn
6. Tensorflow/Keras
7. gensim (NLP)
8. nltk (NLP)

Machine Learning & Neural Network Models

1. Logistic Regression
2. Support Vector Classifier
3. Random Forest Classifier
4. Fully Connected Artificial Neural Network
5. Recurrent Neural Network - GRU