Deploying a Text Classification Model in Python

This article is the last of a series in which I cover the whole process of developing a machine learning project. If you have not read the previous two articles, I strongly encourage you to do it here and here. The project involves the creation of a real-time web application that gathers data from several newspapers and shows a summary of the different topics that are being discussed in the news articles. This is achieved with a supervised machine learning classification model that is able to predict the category of a given news article, a web scraping method that gets the latest news from the newspapers, and an interactive web application that shows the obtained results to the user. As I explained in the first post of this series, the reason I'm writing these articles is because I've noticed that most of the times, the content published on the internet, books or literature regarding data science focus on the following: we have a labeled dataset and we train models to obtain a performance metric.