A Sequenced Approach for Customer Data Processing in Banking Applications between Various Services with Novel Decision Tree and K nearest Neighbour

The objective of this work is to conduct a sequenced approach for customer data processing in banking applications between various services with decision tree and k nearest neighbour. Materials and Methods: It is considered as two gatherings, for example, decision tree and k nearest neighbor algorithms where N=10 sample iterations to test the accuracy of the model for customer data processing in banking applications for various services. Result: The accuracy results of the Novel Decision Tree classifier model has potential up to (98%) and the K Nearest Neighbour model has an accuracy of (88.98%). It’s been observed that there is a statistical remarkable perfection between the Decision Tree and K Nearest Neighbour (p=0.002). Conclusion: The work results for customer data processing in banking applications between various services show that the Decision Tree algorithm has shown higher results and Significance than K Nearest Neighbour algorithm.