Predicting or patron find of a binary variable over time
Since you say you are a beginner, I suggest using a simple binary classifier: logistic regression. It takes the input vector, and transforms it by taking its dot product with a weight parameter (estimating which is the goal) before passing it through the logistic function:

$\hat y(x) = \dfrac{1}{1+\exp(-\left&lt; w, x \right&gt;)}$ and $y=\begin{cases}1, \text{user present} \\ 0, \text{user absent} \end{cases}$. 

For details read the wikipedia article. For features, I would use the time of day encoded as $\left( \sin (2\pi t/T), \cos (2\pi t/T) \right)$, a hot-encoded categorical variable for the day of the week, a binary variable to indicate weekends, and so on. It's a crude model that leaves a lot of data on table, such as correlations between users, but as a beginner's exercise it is appropriate.