European Corn Borer (ECB) is an insect that has been known as a hop pest, its infestations are one of the major problems for hop cultivation. In this thesis, several supervised machine learning techniques are applied on the data collected by the Slovenian Institute of Hop Research and Brewing for 18 years to predict the number of ECB, and the effectiveness of these techniques is compared and analyzed. The collected data consists of two parts: The first part is weather condition data which contains temperature, relative humidity, and precipitation from 8 pm to 6 am each day from April 30 to September 28, each year from 1999 to 2017; the second part is the number of ECBs captured at night on the corresponding date.