Fault Prediction using Random Forest
This talk presents an overview on predicting device faults in HFC networks. Although this might look like a clear time series problem, slightly different approach using Random Forest algorithm which returns promising results will be proposed. Besides this, talk will also feature some data preparation using data.table and usage of mlR package for Machine Learning tasks.
Senior R Developer, Ibis Instruments
Branko is a software engineer and self-thought R developer with several years of working experience in telecommunications and fintech, currently applying Machine Learning for Ibis Instruments and mentoring students on Springboard. He has BSc degree in Software Engineering and MSc degree in Signals and Systems. In his free time, except reading books and enjoying life, Branko is dedicated to developing Data Science community in Serbia and spreading love for R.