To make the Internet of Things and Big Data more tangible, we took a popular old analog product, a Pinball (Flipper) machine from 1987, equipped it with sensors and a raspberry pi and connected it to the internet. By processing the sensor data, it records individual player profiles, compares them with other players and, using machine learning algorithms, it separates different player types. To ease the recording of the highscore, a camera reads the score at the end of the match, which is then saved to a data base after confirmation.
Connected to a display it can also show a live visualization of the sensor data, and of course a highscore list. Since it is configured as a webserver, the database is accessible from any other computer, so the data can be analyzed independently from the current location of the Flipper and of the compute power limits of the pi. And as you have to keep the players up-to- date, the Flipper can automatically send e-mails with information about current highscores or your own profile stats.
Of course this project wasn’t just for fun. The procedure pretty much covers all parts of a Data Science project and was used to train the skills of our trainees.