LOGISTIC REGRESSION BASED SEQUENCE CLUSTERING ALGORITHM FOR FINANCIAL RISK ANALYSIS WEB MINING

Abstract

Logistic regression is the best fit, the very best the model is a numerical demonstration method. Area requirements to portray the connection between several autonomous explanatory factors and the variable response. In many applications, the response to recurring interest or ward variables must be consistent and, accordingly carry an unlimited number of properties with no upper or lower limit. Scientists have decided to predict each factor in predicting response variables facing various problems and customer confusion behavior. Lack of adequate data is a test of many trade associations. Human experts understand from these tax-hiding examples of trade details that companies alone can ruin business opportunities. It attempts to understand all business transactions and give a bogus answer over the years, hiding information from large data sets, sudden examples and improving effective guidance. Multiple-Criteria Decision Analysis (MCDM) is shown to be complex. This paper proposes a method to calculate the ranking of supermarket decision-makers' ranking of well-known clusters and deal with selecting currency risk check areas. The study's objectives to be explored are to recognize the methods proposed for the adoption of three multidisciplinary decision-making strategies. The results show that innovations' vulnerability is very high, and the investigation of the vulnerability of information mining innovations is seriously focused on the great implementation interface. Besides, it explains that three advanced calculations of membership control can improve information mining performance. The importance of large companies, currency risk, and currency Emergency Alert Timetable (EAT) dynamic model of tree care model idea is in the advanced stage of the chain. In total, currency risk on these calculations, investigations and studies of large companies' structure are aware of the urgent relevance.