Remote work or hybrid work after the pandemic is the new default in people's workplaces and many have been making good home offices themselves. This is why increasingly many offices are empty most of the time. This kind of prediction can be a good tool to help seek almost the right size offices and with presence sensors and reserved spaces; there can be enough space for different type of work. Meeting rooms, quiet rooms or areas, and ad hoc spaces just to sit down and print out papers and move to a meeting are the kinds of places employees need when they come to the office. For example, lounge and café type of spaces are also good for small talk with persons outside of your team. The purpose of an office space has changed after the pandemic and the company needs to be ready to act based on different kinds of variables and predictions. In this thesis, the focus was on investigating the possibility of making some kind of prediction that can be used to anticipate office sizes in the future. My employer asked me to see if there is some kind of library or model to predict our office places in the future. This project-based thesis aimed to make the first iteration of a prediction that can be used and modified according to company needs. The commissioning company provided all the data needed for this thesis work. The results show that the prediction met the requirements of the commission. With more data, the prediction will be more accurate. Prophet may not be the best for doing predictions and therefore the suggestion is to further investigate differ ent libraries for different kinds of needs. Longer periods of data will get better prediction results and also adjusting variables that are used to make predictions would be ones that need more investigating.