Linear Regression on Internet Banking Adoption Dataset Using WEKA

Linear Regression on Internet Banking Adoption Dataset Using WEKA

Nidhi Nigam Verma (Dr. A. P. J. Abdul Kalam University, India) and Deepika Pathak (Dr. A. P. J. Abdul Kalam University, India)
DOI: 10.4018/IJSPPC.2020100103
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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.
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2. Methodology

Banking customers having accounts in different bank branches located in various cities of Madhya Pradesh was the target population for this research study. In this study, specifically survey method is used where the data is collected by means of a questionnaire to determine the opinion of a target population of Madhya Pradesh (Sharma & Malviya, 2014).

2.1. Primary Data for the Research

Primary data for this has been collected using a self-structured questionnaire designed purposely for this study. Appropriate secondary sources have as well been relied upon for designing a suitable comprehensive questionnaire to gain deeper insights in this field. Questionnaire also includes questions regarding the satisfaction level of the customers using internet banking on identified factors. For this study data is collected using Google forms and through emailing of the questionnaires to users. Data for the research has been collected from 502 customers which includes both users and non-users of Internet banking. The collected data should to be analyzed by using the appropriate analytical tool or technique in order to understand the various factors and reasons behind Internet banking adoption. For this research study, WEKA tool, a data mining tool, is being used.

2.2. WEKA Tool for Data Mining

“WEKA” stands for Waikato Environment for Knowledge Analysis. Basically, WEKA is named subsequent to a flightless bird of New Zealand. It is a set of various machine learning algorithms that can easily be applied on any data set directly or can be called from your Java code. WEKA basically contains various tools for data mining and data pre-processing which are clustering, classification, association rules, regression and visualization (Palaniappan et al., 2016). It is freely available and an open source software for data mining and its applications under GNU general public license, which is developed by the university of Waikato in New Zealand.

2.3. Sampling Procedure

A sampling, defines the population from which our research sample is drawn. As there is hardly enough money or time to collect information from everyone in overall population, the goal becomes choosing a representative sample, sometimes called as a subset of that population. Area for this research is Madhya Pradesh, India. Madhya Pradesh is situated at the center part of India, so known as the heart of India. For this research sample which is considered are from major cities of Madhya Pradesh. Research Questionnaire was sent to them online, their opinions and concerns are collected in order to measure the adoption rate of internet banking in Madhya Pradesh.

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