On April 16th, the fifth edition of Datathon 2019 – the International Data Science Hackathon finished. More than 140 participants participated in the two-day event, held in four locations in the world – Sofia, Bangalore (India), Jaipur (India), Beirut (Lebanon) and online. The task was to work with real data to solve cases of business […]
On the 14th of March, the Data Conference 2019 was held in Athens with the participation of local and international lectors who introduced the topics of Machine & Deep Learning and Data Science in Business Environment. Sergi Sergiev, the founder of Data Science Society was also invited to present the topic about open-innovation and open-source […]
From 1st to 4th of March Data Science Society was a co-host of a cool event in Sofia Lab. The event was Hack & Design Challenge vol 1: Make Games and AI (organized by Hardcore Game Jam) and it was all about meeting the AI community with the game designing community. It was not exactly […]
What is Data Science? In this Data science online training you will understand all basics to advanced statistics and learn how to program in R & Python and how to use R & Python for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming […]
Sofia Air Pollution Case Team BG-USA: Kristiyan Vachev – Bulgaria () Sergey Vichev – Bulgaria () Stefan Panev – Bulgaria Georgi Kirilov – Bulgaria Mike Lane – USA () Data Preparation Geocoding the construction data: The original source file can be found here. Basically, this is very very similar to geocoding as proposed in the original documentation […]
Sofia is a city with significant concentrations of particulate matter less than 10 micrometers in diameter (PM10.) A high concentration of PM10 is disruptive to life and the climate. The purpose of this project is to predict the concentration of PM10 at a particular day given the climatic conditions. This is important in allowing the making of policies to reduce the pollution in the city. Our contribution consists of a random forest regressor that acheives the purpose with 70 to 80% accuracy.