I solved this case study as the practical exam for my "Professional Data Scientist" certification by DataCamp.
"Tasty Bytes" is a company offering an online search engine for cooking recipes. Further, for users with a monthly subscription they offer entire meal plans and for a premium subscriptions they even deliver the engredients. The product manager observed that the overall traffic on their web page increases significantly (as much as 40%) when popular recipes are displayed on their home page. Increasing traffic translates to increasing the number of subscriptions. Maximizing the traffic is hence important to increase the revenue of the company. While currently recipes to be displayed are chosen by the product manager manually, they seek to find a more data-driven approach to select popular recipes that increase traffic on their web page in order to, as a consequence, increase the number of subscriptions to their services and products.
The product manager requested us to provide a means to
- predict which recipes will lead to high traffic,
- correctly predict high traffic recipes 80% of the time.
For more details see the instructions file.
There is no Git history for this project as the development was fully conducted on the DataCamp platform and only loaded onto Github after completion.