Linear Regression on Internet Banking Adoption Dataset Using WEKA

Abstract

Data mining or knowledge discovery in the database (KDD) is an excellent process to find out valuable information from a large collection of data. Data mining has successfully been used in different fields such as medical, marketing, banking, business, weather forecasting, etc. For the banking industry, data mining, its importance, and its techniques are vital because it helps to extract useful information from a large amount of historical data which enable to make useful decisions. Data mining is very useful for banking sector for better acquiring and targeting new customers and helps to analyze customers and their transaction behaviors. In the recent era, a new technology that has achieved considerable attention, especially among banks, is internet banking. Its large scope of applications, its advantages brings an immoderate change in a common human's life. Linear regression is one of the most commonly used and applied data mining techniques. Linear regression is really a very fast and simple regression algorithm and can give the best performance if the output variable of your data is a linear grouping of your inputs. In this paper, the linear regression is applied on internet banking adoption dataset in order to compute the weights or coefficients of linear expression and provides the predicted class value. The analysis here is done with the help of WEKA tool for data mining.