I have two datasets:   data one; input id acctype$ catalogtype$ amount; datalines; 1 credit issued 10 1 credit refund 5 1 credit cancelled 20 2 debit issued 20 2 debit refund 20 3 cash issued 20 run;   data two; input id acctype$ catalogtype$ amount; datalines; 1 credit issued 10 1 credit refund 10 1 credit cancelled 10 2 debit issued 20 2 debit refund 10 4 cash refund 10 run;   I want a output that should get matched records from left table, and non matching records from right table like this:   1 credit issued 10 matched 1 credit refund 5   not-matched 1 credit cancelled not-matched 2 debit issued 20 matched 2 debit refund 20 not-matched 3 cash issued 20 not-matched   I want this specifically using proc sql, please. I tried left outer join and union all of both datasets but not able to get solution.    ... View more

I have data on customer purchase history. I want to score each of these customers based on the attributes. For this, I want to calculate the score by assigning weights to variables, (ex: 10% to v1, 20% to v2, 50% to v3 etc.,) and then sum up these weights. The resultant score should tell me how good a customer is. For instance, a score above 500 means they are good/loyal customers and we can expect good sales from them next time. While the threshold can be decided once we get a score, I want to know how I can approach this problem.    I decided to run PCA, from which I can get the PCA scores and hence use coefficients as weights.  For example, if I select the first principal component and take the coefficients, y1=0.5v1+0.8v2-0.2v3 ,  replacing v1, v2 , v3 with the values of the attributes, I can get a score of each observation.    I am not sure if this is a clever approach. Is there a better way to optimize the weights and calculate the score of each customer? Any thoughts are appreciated.   ... View more