2
$\begingroup$

I'm new to ML and trying to find some practical use to it I encountered with the chance of saving the connections and disconnections (the binary variable) of a bunch of users like this:

"User A connected at 10:02:33

User A discconected at 10:05:02"

I was wondering how could I determine whenever the user is going to connect again, analyzing his pasts connections, disconnections and time online.

My concers are the following:

  • Would this be posible?
  • What's the best method for this?
  • How much samples per user would I need?
  • What would be the best way to structure the data, and besides the user identifier, action(connect/disconnect) and time the action take place what other info would be useful?

An just as an extra question: Would it be possible to add more data in real-time to improve the algorithm prediction? How?

$\endgroup$
1
$\begingroup$

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:

$\hat y(x) = \dfrac{1}{1+\exp(-\left< w, x \right>)}$ 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.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.