I am a c# senior developer and I got a task to try and predict the potential in each new client, or maybe the worth of each customer. I don't have experience with machine learning, but I played with accord-framework.net and got some nice results on simple task.
My data model for training is:
GeoLocation, // the country of ip when registed. iso code string
Age, // number
DateRegistered, //date time
Email, //string can be broken to vendors as catergorial (gmail, yahoo, microsoft and such)
EmailValidated, //is the email really exists. bool
PhoneNumber, //string
PhoneNumberValidated, // is the phone number really exists
CampaignName, //string (may be categirial)
UserAgent, //string should I make it categorial? (has info about browser, device, verndor, operation system and such, long string)
LandedOnPage, //string first url the customer entered from
RegisteredFromPage, //string url of the page that the user registered from
RefererUrl, //string url the client came to our site from,
NumberOfPurchases, //the amount of times the customer puschase something on our site
CustomerValueUsd, //the total amount of USD the customer spent in our site
The output shoud be CustomerValueUsd
I have a lot of data in the history, so I can back test it.
My questions:
- Does it make sense to do this task even though I don't have an experience with machine learning? How complicated is this task considering I'm using a well known framework?
- Assuming that I'm taking the task, which algorithm should I choose to perform this kind of task?
- How should I build the training data? see my comments, do you think my comments are ok to start with? or maybe I can break the data directly?