Nowadays with the help of modern technology we can get so much information from a simple shopping. It can be very useful for the store to optimize their stock holding, thereby they can reduce the prices, and it helps them to sell at a reduced price. It can be useful indirectly for the customers as well, because they can get personalised offers, and discounts, so they can hear of products that they are intrested in.
The goal of my thesis is to predict the date, and spending of the next shopping. For these predictions I used anonim shopping data, which contains the dates and spendings about the purchases.
The first section of my thesis contains the theoretical background of the project. It contains a methodology that I used during the project, the rules for timeline analysis, and the knowledge about programming and modelling methodology.
The second section concentrates on the concrete implementation. TIn this part, there is the intruducion of my dataset and the models, evaluation, and afterwards it gives tips for further improvements.