Data Science Asked by Vivek Kalyanarangan on June 29, 2021
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 –
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.
The topic you are interest in is called "PU learning" or "positive and unlabeled learning".
You can start by having a look into survey literature.
Answered by Graph4Me Consultant on June 29, 2021
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