Purchase prediction in eshop

The topic of our bachelor thesis is to predict user’s behaviour during his visit in eshop, specifically, whether he is going to buy something at the end of his visit or not. Our task is to create a method for this prediction.
We can predict user’s behaviour based on his behaviour in the past or behaviour during his present session. This type of information can be very useful for merchants and start era of personalized advertisements in their eshop. For example they will be able to create personalized newsletters for groups of customers, which showed interest in particular category of products in the past.
To solve this type of problem we need to use machine learning. Machine learning gives computers ability to learn without being explicitly programmed. For training our machine learning model we need to extract features from data, on which it will learn. Features can describe customers,products or sessions.
We use multiple algorithms for training and then compare reached results. After we modify features and try to boost models to get better results.
For training and validation our model we use data provided by discount portal zlavadna.sk