Data Management Project
Evaluating the relationship between games and reviews.
This Project was written in collaboration with Paolo Caggiano for the Data Management course in the Master Degree in Data Science.
The project is interested in addressing different questions related to the evergrowing videogame market:
Are professional critic's review the main factor of a game's success? Or, as the game community is strictly bound to the internet, other users' reviews are what count the most?
How much are the reviews important in terms of success or failure of a game?
Are there genres more attractive than others in terms of reviews?
To answer this questions, we scraped data by both Steam and Metacritic, and then stored them in a MongoDb database.
From this last we took all the steps necessary to clean and integrate the data of the two different sources.
The project therefore showcases our ability to carry out a data science project from the acquisition phase, through the storing, cleaning and integration of the data, to the final exploratory analysis and conclusion drawing phases.
It also demonstrates our abilities to make decision throughout all of the stages based on the type of data and the type of analysis we wanted to carry out.
All this was achieved through the use of languages like Python and platforms like MongoDb and Jupyter Notebook.
Further information about the decisions we made for the acquisition, storing and integration of the data can be read on the official report.