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A have a data frame with shops with a number of independent variables (all categorical), I'm trying to find a decent model to predict a dependent variable that is ordered and categorical (ie volume of foot traffic: "low", "medium", "high"). The dependent variable is defined for about 20% of the observations, and I want to predict for the missing 80%.

What models/techniques would you suggest?

For now, I started by learning some association rules with arules (manual here):

require(arules)
train # my data frame with the complete observations
target_field = 'foot.traffic'
# filter rules to observe only the relevant ones
appearance_list = list(rhs=paste0(target_field,"=", unique(train[,target_field])))


rules <- arules::apriori(train,
            parameter = list( support=0.1, confidence=0.2 ),
            appearance = appearance_list)
rules <- sort(rules, by="lift")
inspect(rules)

Now I can inspect these rules, and they generally make sense. They mostly refer to low and high cases, indicating that rules for medium are harder to identify.

How can I apply them back to the training set to see how well they predict the foot.traffic variable? Is there a good off-the-shelf strategy to apply the rules?

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