The data set comprises people's activity on Office 365 and my goal is to predict whether the person is experiencing pressure or not using machine learning algorithms
Very often in data science the problem or the data (or both) is not well defined from the start. Thus the first task is often to design a properly defined ML problem out of the vague initial problem. This is a really important step if one wants to achieve anything useful: ML is not magical, it's a tool which needs to be used in the right way in order to provide interesting results.
According to your description, the initial problem seems ill-defined: for a goal such as predicting whether somebody experiences pressure, there's no clear way to represent "pressure experienced" other than asking the user how much pressure they feel. If your data doesn't contain any such information from the user and you can't obtain it in any other way, then it's likely that the problem cannot be solved at all. As a rule of thumb, if a human cannot find indications in the data which helps answering the question, it's likely that a ML system cannot either.
So your first task is to determine if there's anything in the data that can represent "pressure experienced by the user". For example, maybe a stressed user tends to click everywhere randomly? Then maybe you could try to find in your data if there is a particular pattern or "clicking frequency" and calculate a new column which represents the "stress level" based on this. But there is a high risk that such an assumption is not very reliable, and this would influence your results.