segPredLifts

Not all predictive analytics tools are born equal

The main objective of any “Advanced Analytic” tool is to generate the best, most accurate, ranking (or “list of candidates”). There are mainly 2 different approaches to generate the ranking: segmentation or prediction. Different softwares use different approaches. Segmentation tools This covers 99% of the available tools. These tools are very easy to create and
kmeans-segments2

Visualize Clustering with SOM in Anatella / R

Note: The data is extracted from Marketing Engineering, with the kind permission of Dr A. De Bruyn. A very complete R code for SOM can be found in the excellent post of Shane Lynn http://www.shanelynn.ie/self-organising-maps-for-customer-segmentation-using-r/ Clustering is a tricky business. while the hardest part of it lies in the business process, there is also a
datasetForLearning

Of the utility of the TEST dataset

Of the utility of the TEST dataset Let’s assume that we want to create a ranking (or a “list of candidates”) for a marketing campaign using predictive technique. Let’s give a practical and real example. Let’s assume that we are end of 2009 and you are selling a “GPS device” (like Garmin or TomTom). You
classification_1

Classification problems: lift curve or classification table?

The common idea of classifying is to look at “small groups” of records, and evaluate if we should put them a 1 or a 0 when it comes to a particular target. For example, if I am interested in figuring out who will get cancer, I can “build” the following logic, without requiring any predictive