Purchase prediction in eshop

Shopping in an eshop is in virtual space, not in the physical stores of companies with ability to influence customer behavior. Therefore, there is a requirement to monitor customer behavior while visiting the store’s website to positively affect the outcome of visit. Supervised machine learning is one of the most used methods to predict customer behaviour nowadays. The goal of this thesis is to analyze input data and use it for training machine learning model. Two independent algorithms will be used for model training: gradient increasing decision tree and logistic regression. After training, results will be compared and better model will be picked. The picked model will be experimentally tested on data from the discount portal.