The goal of KDD Cup 2009 is training Neural Networks by Customer Relationship Management (CRM) which include more than 50000 customer¡¦s information, and to predict the CRM ranking form different parameters.
The task is to estimate the churn, appetency and up-selling probability of customers, hence there are three target values to be predicted. The challenge is staged in phases to test the rapidity with which each team is able to produce results. A large number of variables (15,000) is made available for prediction. However, to engage participants having access to less computing power, a smaller version of the dataset with only 230 variables will be made available in the second part of the challenge.
Us in order to solve this so many information, the information must be triggered by a smaller study of the rules to train; LVQ (Learning Vector Quantization), is a combination of supervised and Unsupervised learning method to find the dataset of Weight, the dataset to predict the future dataset correctness.