Using Machine Learning and NLP to segregate the unreliable and reliable news articles

This project analyzes about 18k news articles and their titles, lemmatizes and cleans them and then uses them as features, which are then used in Logistic Regression, to classify them as reliable (0) or not reliable (1). It is able to achieve a test set accuracy of 95.8%.

The dataset used is available on Kaggle. It contains over 20000 news articles along with their authors, titles and labels classifying them as reliable or not reliable.

It is present as train.csv in the repository. To access the data online, follow this link.

1. Use this link to download the dataset and set the folder containing the downloaded data as the working directory.

2. Make sure you have all the libraries used in the fake_news.py file. In case you need to download any of the libraries, use this command:

pip install 'your library name'

3. Once you have all the libraries imported, copy the code from fake_news.py and run it.