Prediction of Efficient Customer Behaviour Analysis usingLogitBoost&Attribute Selected Classifier Algorithms

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R.P.Kannan, A.S.Arunachalam

Abstract

The Data Mining establishes closed consumer relationships and manages interpersonal relationships consumer in today's business world. Data classification has recently become popular in various applications and classification model is an important data mining technique in the industry modern techniques such as predictive analytics have gained a lot of research attraction these days. In the business world, it is important for the business information for the business people using the predictive analytics,  it is possible to see what a consumer will buy next. The main goal is to increase the profit earned by a supermarket. In this paper, various consumer data have been conducted for analyzing the purchases of consumer behaviors. Initially, the purchases have been analyzed by classify the purchases gender classification and by analyzing what type service like the customer and buy the more products. The proposed approach focuses on the Prediction of the consumer behavior using two classification Algorithms one is logitboost and second one is attribute selected classifier. In this two algorithm come under Meta classifier in weka tool and in this two algorithm to classify consumer data efficiently.

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