0
$\begingroup$

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.

  • customer_dataset:
    1. id(unique id for customer)
    2. name
    3. assigned_user_id(user assigned)
    4. company_id(company_which user works for)
    5. expected value(revenue expected)
    6. status(whether a lead/customer([0,1]categorical))

visit_dataset

  • customer_id(unique_id)
    1. user_id
    2. company_id
    3. date(visit date done)
    4. schedule_date(actually scheduled)
    5. next_action_date(if any next action)
    6. 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;

  1. find total visit done per customer and other features from dates?
  2. any resource that I can follow for how to work with dates data in pandas?
  3. I'm trying to use featuretools to get the features.(any other advise)?
  4. 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.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.