Analysis of customer data using hybridized machine learning technique along with data exploration methods - S. Ramesh, G.; T V Rajini Kanth, Dr.; D Vasumaathi, Dr. (International Journal of Engineering and Technology, 2018)
The introduction of Information Communication Technologies (ICT) like POS and sensors into retail and marketing the Sales Data and Customer data are increasing day by day seamlessly without any limitation and boundaries in an exponential growth. This huge voluminous data is threatening research community to develop suitable models for the identifying Target Customers, enhancement of particular Products sales etc. The need of Business Intelligence techniques is very much required in this scenario to address the Entrepreneurs, Business community. The Machine learning algorithms are also useful to Analyze Sales Volume or to Discover Most Likely to Buy Products or to Provide Price Recommendations. This paper addresses these problems with the help of data exploration using Visual Analytics techniques apart from predictive analytics. The Machine learning algorithms like K-mean Clustering, Logistic Model tree together as a hybridized Clustered based Logistic Model tree algorithm was applied apart from machine learning Data exploration techniques. Visual Comparisons were also made along with advanced statistical techniques and summarized the results for better conclusions.Â Â Â