This project aims at detecting fake and real news using machine learning algorithms and techniques.
(The weights of our best model can be found here)
In recent years, due to the booming development of online social networks and online media in general, fake news for various commercial and political purposes has been appearing in large numbers and has been spread all over in the online world. So, we want to build machine learning algorithms that can detect fake news.
The dataset was acquired by Kaggle
On the first phase we explore and clean our data. We have balanced data, which is very important for our models.
We use many algorithms in order to meet our business goals.
As we have evaluate all the models, we conclude that the best model is the Feed Forward NN with accuracy above 98% for both training and validation and very small values for the loss function. (The weights of our best model can be found here)
Most of our models have very high accuracy not only because of the pre-processing but also because of the nature of the data. So, we use Dummy Classifier which result in 51% accuracy and that means that our results are far better than those chosen with a not clever way.
Konstantina Georgiopoulou
Anastasios Theodorou
Christos Kallaras
Stavros Kasiaris