##### Run the scraper with `python scraper_news.py` and fetch 3 documents. The scraper is not expected to fetch 3 documents and stop by itself, you can stop it manually.
###### Do the learning curves prove the topics classifier is trained correctly - without overfitting? Ask the student to explain what the term "overfitting" means and how he avoided this phenomenon.
> Additionally, you can look for external resources. For example, Wikipedia has a good page on "overfitting".
##### Ask the student to train and store the topic classifier model in a file named `topic_classifier.pkl`.
##### Analyse the DataFrame with 300 articles: relevance of the topics matched, relevance of the sentiment, relevance of the scandal detected and relevance of the companies matched. The algorithms are not 100% accurate, so you should expect a few issues in the results.
##### Analyse the output: relevance of the topic(s) matched, relevance of the sentiment, relevance of the scandal detected (if detected on the three articles) and relevance of the company(ies) matched.
