You are a Data Scientist working at upday, a news aggregator and recommender.

The engineering team at upday is gathering on a regular basis articles from all the Web. In order to provide a proper filtering functionality in the app, the articles need to be categorized.

You have at your disposal a pre-labelled dataset that maps different articles and their metadata to a specific category.

It's up to you now to help the company providing a solution for automatically categorizing articles.

The repository contains a dataset with some english articles and some information about them:

• category
• title
• text
• url

The purpose of the task is to provide a classification model for the articles.

You should make a pull request to this repository containing the solution. If you need to clarify any point of your solution, please include an explanation in the PR description.

What we expect:

• Explanation about the solution you adopted and the results from your data exploration
• Documentation of the results of your model, including the metrics adopted and the final evaluation
• The training and evaluation code

The solution should just perform better than random, also we expect you to use model that is not just rules-based.

How to present the documentation and the code is up to you, whether to provide one or more jupyter notebooks or via a different mean.

Scripts to be run from the command line:

• A script for training the dataset
• A script for evaluating the dataset
• A script to infer the category given an article