Unveiling the Power: A Comparative Analysis of Data Mining Tools through Decision Tree Classification on the Bank Marketing Dataset

The importance of data mining is growing rapidly, so the comparison of data mining tools has become important. Data mining is the process of extracting valuable data from large data to meet the need to see relationships between data and to make predictions when necessary. This study delves into the dynamic realm of data mining, presenting a comprehensive comparison of prominent data mining tools through the lens of the decision tree algorithm. The research focuses on the application of these tools to the BankMarketing dataset, a rich repository of financial interactions. The objective is to unveil the efficacy and nuances of each tool in the context of predictive modelling, emphasizing key metrics such as accuracy, precision, recall, and F1-score. Through meticulous experimentation and evaluation, this analysis sheds light on the distinct strengths and limitations of each data-mining tool, providing valuable insights for practitioners and researchers in the field. The findings contribute to a deeper understanding of tool selection considerations and pave the way for enhanced decision-making in data mining applications. Classification is a data mining task that learns from a collection of data in order to accurately predict new cases. The dataset used in this study is the Bank Marketing dataset from the UCI machine-learning repository. The bank marketing dataset contains 45211 instances and 17 features. The bank marketing dataset is related to the direct marketing campaigns (phone calls) of a Portuguese banking institution and the classification objective is to predict whether customers will subscribe to a deposit (variable y) in a period of time. To make the classification, the machine learning technique can be used. In this study, the Decision Tree classification algorithm is used. Knime, Orange, Tanagra, Rapidminerve, Weka yield mining tools are used to analyse the classification algorithm.