Estimating and predicting are two similar activities that normally yield a numerical measure as the result. For example, we may find a set of existing customers that have the same profile as a candidate customer. From the set of existing customers we may estimate the overall indebtedness of the candidate customer. Prediction is the same as estimation except that we are trying to determine a result that will occur in a future time. Estimation and prediction can also drive classification.
Numerical estimates have the additional advantage that the candidates can be rank-ordered.
We may have enough money in an advertising budget to send promotion offers to the top 10,000 customers ranked by an estimate of their future value to the company. In this case, an estimate is more useful than a simple binary classification. Specific techniques that can be used for estimating and predicting include standard statistics and neural networks for numerical variables, as well as all the techniques described for classifying when only predicting a discrete outcome. Affinity grouping is a special kind of clustering that identifies events or transactions that occur simultaneously. A well-known example of affinity grouping is market basket analysis.