I have a dataset that includes 6 variables about prospective sales opportunities (probability of closing, days until expected close, age of opportunity, etc.). 2 of the columns are categorical and 4 are continuous. I am looking to create a composite score of each opportunity based on these variables.
I have tried manually assigning scores to each column based on arbitrary ranges, but the composite scores ended up being too similar. I want to optimize the scoring for each variable. Is there a way to do this through machine learning? I want to find out which variables are the most important, or "stand out" the most from the rest of the pack. I also would like to see if any variables are redundant.