I have a query to solve. I have data regarding customers and number of visits done to them. These are in two tables. So I want to join two table and create different features so that I can find better/lagging customers from the data based on scoring.
- id(unique id for customer)
- assigned_user_id(user assigned)
- company_id(company_which user works for)
- expected value(revenue expected)
- status(whether a lead/customer([0,1]categorical))
- date(visit date done)
- schedule_date(actually scheduled)
- next_action_date(if any next action)
- status(visit done or not)
So I need to score lead/customer on the basis of these data and find a list of top user. Also from the number of visits done, I need to figure out how many average/sum of visits done per customer so that I can find next forecast of visit(In case he doesnt go or forget about lead).
I'm new in manipulating data with pandas. How can I;
- find total visit done per customer and other features from dates?
- any resource that I can follow for how to work with dates data in pandas?
- I'm trying to use featuretools to get the features.(any other advise)?
- also I'm trying to automate this feature. So I'll feed in last 30 days visits as data and then I have to figure out the next visit date(prediction)Important (I so don't know how will i do that but trying ;P)
Sorry If i missed any info, please give me feedback if any issue.
Thanks a lot.