Dear Readers – here we are again at the beginning of a new month. Last months we have been focusing on the Hows, with helpful articles on visualizations and guides. This month we decided to look into some of the amazing applications of Machine Learning.
From Weather, to Medicine and Real Estate, going through Gaming suggestions as well, here are some of the new articles on ML applications!
Improving User Engagement with Smart Game Recommendations
Andre Tan goes through the processes involved in the construction of a recommendation engine on a mobile game distribution company such as Playphone. He tackles the problem of non proactive users, and how an approach that suggests relevant games for the users to try is pivotal to the success of the game store.
How can AI be your dietitian and prevent diabetes
Diabetes is a major health burden affecting 8.5% of the global adult population, with complications including stroke, heart attack, kidney failure and vision loss, among others. Postprandial blood glucose response – PPGR – is a major risk factor for Diabetes Type 2 and it varies greatly among individuals. Still there is no existing methods that can accurately predict PPGR, with the current models still relying on carbohydrate counting, which doesn’t provide a good prediction. In this article Keith Kwan tackles this problem using machine learning.
Moneyball For Real Estate
Making use of the easiness to access data on location, how we move, how long we stay, how much we pay, and how we feel, Easy Atlas goes through three case scenarios: 1) open up a cafe in Paris, 2) invest in residential real estate in Shanghai, and 3) situate a nightclub in New York City, to showcase how data can help us in making these decisions.
Mining Social Media to inform Haze Response
In order to respond to a forest and peatland fire or haze event, a disaster management authority needs fire hotspot information along with baseline information, such as an estimate of the affected population and data on the available facilities. Pulse Lab Jakarta teamed up with researchers from the University of Kassel to test the potencial of social media to assist haze response.
Stock2Vec – From ML to P/E
In this article Jon Perl uses Machine Learning algorithm Word2Vec to attempt to group similar companies in the stock market.
We also thank all the great new writers who joined us recently Evan Baker, Nahua Kang, Manish Chablani, Michael Green, Pranavathiyani G, Ophir Samson, Andrew Pierno, Jerome Samson, Lee Schlenker, Markus Ojala, Dat Tran, Kiran Sudhir, Juarez Bochi, Jason Kowal, Rob Thomas, Arthur Gretton, Kenny Jones, Lee Robb, ericcolson, Viet Vu, Johannes Rieke, Ravi Shekhar, Anish Singh Walia and many others.
Inês Teixeira.
