Radial Basis Function Neural Classifier using a Novel Kernel Density Algorithm for Automobile Sales Data Classification

This paper presents a novel approach for classifying the sales data using neural networks, whose result may be helpful in making sales data analysis and optimizing the sales. Radial Basis Function neural networks are widely used for classification problems with multi-class attributes because of their gradient-descent feature. Our objective is to classify the sales data into three classes: high sales items, moderate sales items and poor sales items. The proposed work is to design an efficient algorithm to classify the data for further analysis. The algorithm must take less time to construct a data classifier with an optimized parameter setting to find the center of the classes there by performing an efficient classification.