Lets assume we have the following table with different categories from a data base.
id <- c('id1','id2','id3','id4','id5')
visit_number <- c(3, 8, 10, 7,8)
page_views<- c(35, 85, 102, 7,18)
channel <-c('Natural Search', 'Paid Search', 'Social Network', 'Paid Search', 'Social Network')
country <- c('ES','ES','NL','DE','DE')
device <- c('PC','PC','Other','PC','Other')
sex <- c('F','F','M','M','M')
age <- c('18-30','18-30','18-30','>50','<40')
product <-c('SRSX11W.MX', 'SRSX11W.MX', 'DSC-HX400V //SF-8C4', 'SLT-A58Y//Q E38', 'DSC-HX400V + LCS-U21 + SF-8C4')
producy_category <- c('audio','audio','camera','tablets','camera')
price <- c(33.45, 82, 104.44, 72,88.45)
# Join the variables to create a data frame
df <-data.frame(id,visit_number,page_views,channel,country,device,sex,age,product,producy_category,price)
df
I would like to calculate the top-performing combinations based on countries, channels, and product categories. The table is much bigger. what kind of combinations should I do to predict this?