An analysis on business intelligence predicting business profitability model using Naive Bayes neural network algorithm

Abstract

Nowadays the exponential evolution of repositories in business information data has led to an unorganized, huge, and unstructured data collection. Hence, it has become increasingly difficult to retrieve useful information from these large collections of data. Business Intelligence System Model is therefore proposed as a process-focused design for understanding and evaluating the effectiveness of information retrieval. This is achieved by employing Decision Tree Clusters association time and Naive Bayes Neural Networks Algorithm. The goal of Business Intelligence Profitability Model is to ease the interpretation of the large data volumes. The objective of this paper is to analyse the issues on BI implementation and proposed a model to minimize the issues in Business Intelligence by predicting profitability in Business. The result of the research is a new proposed technique that is beneficial to both businessmen and computational intelligence researchers. Subsequently, implementation in business intelligence system model will be then performed accordingly. The decision making Based-Naive Bayes and Neural Network algorithm is validated in terms of its workability through the implementation of the business intelligence system model. The relevance of this research is to evaluate the new produced method and to provide an effective way of retrieving the raw business data and information.