A Proposed Mining Approach Based on Business Intelligence by Using Data Sciences Techniques

Abstract

Business Intelligence (BI) is a concept of applying a set of technologies to convert data into meaningful information. BI methods include information retrieval, data mining, statistical analysis as well as data visualization. Large amounts of data originating in different formats and from different sources can be consolidated and converted to key business knowledge. Data mining is used to search for patterns and correlations within a database of information. Business Intelligence is becoming an important factor that can help organizations in managing, developing and communicating their valuable goods such as information and knowledge. The main goal of data mining is the search for relationships and distinct patterns that exist in datasets but they are "hidden" among the vast amount of data. Data mining was effectively applied in many areas including: inventory control, fault diagnosis, credit scoring, marketing, financial services, scheduling, fraud detection and network management. Data mining can play a significant role for the exploitation of business intelligence in e-business. While techniques and tools for e-commerce are being actively developed, there are far fewer for e-business intelligence. This paper contributes to the area of web usage analysis for e-business intelligence by 'marrying' e-business with data mining, considering business intelligence as the best environment. Four metrics, applied innovatively for the first time in the field of e-business, provide capabilities for examining data about e-shopping in online stores and detailed insight into the effectiveness of different efforts by answering related business questions.. This paper attempts to propose prescient investigation to foresee Consumer Behavior conduct on Jumia Website in Egypt by utilizing conduct information, informatics and investigation approach by utilizing Data Sciences Techniques through grouping Classification Technique by choice tree Algorithm in particular Random Tree the results show that this the Random Tree Algorithm is reasonable for Jumia Datasets for anticipating International Journal of Intelligent Computing and Information Sciences https://ijicis.journals.ekb.eg/ 133 A. I. EL SEDDAWY et al.consumer conduct with the goal that more profound knowledge into Consumer conduct can be gotten to help prescient examination so as to improve business choice.