Over past decades, to expand the fitness and success of verdict a bunch of decision support models on the base of data mining classification techniques has proposed by numerous researchers. However, introduced practices have benefited the users in different ways but due to inadequate build procedure, use of solitary classification technique or incorporate the functionality of arbitrarily picked methods into a single practice each approach face dissimilar complexity and fail to obtain utmost results with different state of affairs. The selection of the appropriate classification algorithm for a given data-set is an important and complex issue, full of research challenges. On the other hand building different model for dissimilar data sets increase cost and time with lacking of correctness. This dilemma of accessible decision support systems has considered into this paper by proposing a new dynamic ensemble framework of data mining classification method with condensed feature selection procedure. The experimental results depicts that proposed approach has produce more precise outcomes in comparison of classical approaches.
Software And Hardware
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL