BookmarkSubscribeRSS Feed
vrushankshah
Fluorite | Level 6

I have a dataset which contains various clicks which a user did while going through the website. It also contains uniqueid of each user, date and time of the click and their name. Essentially my main aim is to predict what will be next click of the user. For example, if it starts from home then goes to clothing then goes to Denim then from Denim what is the highest probability of its next click?There are 50000 unique click patterns in a month which a user has clicked. Will Markov chain be feasible for such data?

 

Clicks                                                            Time                                  Id
Home,Clothing,Men,Denim,Home         02/10/2018/3:22pm               1234
Home,Kitchenware,glass,purchase       03/10/2018/4:00pm                4567
Home,Clothing,Men,Denim,Purchase   04/10/2018/3:55pm                7891
Home,Clothing,Men                               05/10/2018/2:56pm                6789

 

 

1 REPLY 1
Ksharp
Super User

It is more like Market Basket Analysis.Change your data like:

Home,Clothing,Men,Denim,Home  1234

-->

Home,Clothing, 1234

Clothing ,Men, 1234

Men,Denim, 1234

Denim,Home  1234

 

After that count the frequency.

If MC could apply to it , calling @Rick_SAS 

sas-innovate-2024.png

📢

ANNOUNCEMENT

The early bird rate has been extended! Register by March 18 for just $695 - $100 off the standard rate.

 

Check out the agenda and get ready for a jam-packed event featuring workshops, super demos, breakout sessions, roundtables, inspiring keynotes and incredible networking events. 

 

Register now!

What is Bayesian Analysis?

Learn the difference between classical and Bayesian statistical approaches and see a few PROC examples to perform Bayesian analysis in this video.

Find more tutorials on the SAS Users YouTube channel.

Click image to register for webinarClick image to register for webinar

Classroom Training Available!

Select SAS Training centers are offering in-person courses. View upcoming courses for:

View all other training opportunities.

Discussion stats
  • 1 reply
  • 540 views
  • 0 likes
  • 2 in conversation