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
I have a lot of data in the history, so I can back test it.
- 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?