The aim of each enterprise is to achieve high revenue from the business and to stay in a high position from their competitors. To archive high revenue and high position from competitors the need of understanding the business consumers is a crucial one. However the firm business is completely dependent on the consumers the efficient analysis of consumers within the enterprises makes to achieve the business to high position. To perform effective consumer analysis, in this study different machine learning is studied and experimented. ML classifiers make to understand in-depth analysis about the consumer data and further enables to plan wise decision strategies to enhance the business revenue and consumer satisfaction intelligently. The use of different ML classifiers is to sort out how the customer prediction outcome changes accordingly to the ML classifier is applied. This makes to find the best ML classifier for the consumer dataset applied in this study. The experimental procedure is performed using different ML classifiers and the outcome achieved is captured and projected using various validity scores. This work applies different ML classifiers like K-NN, C4.5, Random Forest, Random Tree, LR, MLP and NB for customer analysis. The empirical results illustrate the C4.5 model achieves better accuracy prediction compare to other ML classifiers and also compared with the time complexity NB model works efficiently with running time.
Customer Analysis Using Machine Learning Algorithms: A Case Study Using Banking Consumer Dataset
