I have ~7 million rows of customer data (~500 sparse attributes)
A million out of them have opted in to a new service.
How do I use this signal to predict which of the remaining customers are likely to adopt the service? And how do I measure the effectiveness?
Problems face so far -
- Unable to treat this as a supervised problem due to lack of definitely negative variable
- Unable to apply label propagation because there is only one class
Apart from treating this as an anomaly detection problem (oneclasssvm etc.), I also tried using nearest neighbors based approach.
Looking for other ways to solve the problem if there are some go-to techniques that I am missing.
I know there is an answer here but it only talks about oneclasssvm that I have already tried. Also trying to find ways to measure model effectiveness along with any novel ways to solve.