Clustering Machine-Learning Technique for Administration and Economic Development: Focusing on Predicting the Purchase of Electric Vehicles to Improve Business Decision Making for Companies

Abstract

There are several ways to forecast electric vehicle sales to help businesses make better decisions. One strategy is to use prescriptive and predictive analysis techniques to examine consumer preferences and buying behavior regarding electric vehicles. Additionally, models for predicting electric vehicle sales can be developed using logistic regression and decision trees. These models let businesses make well-informed judgments about their sales by offering patterns regarding customer mining, classification accuracy, and prediction effects. In this study, we collected survey data from approximately 452 respondents in Qassim State through an online questionnaire to analyze factors influencing electric vehicle (EV) adoption. The data consists of 23 conditional attributes that were evaluated during the analysis. This study suggests evaluating the features of the customer that may influence their decision to purchase electric vehicles. The clustering algorithm was implemented to segment customers based on these features. The current study's findings demonstrated the effectiveness of the model created using the K-means clustering algorithm derived from data mining in predicting the purchase of electric vehicles. The accuracy of the correctly classified instances was evaluated using three distinct classification techniques: BayesNet-D, NaiveBayes, and J48. According to the evaluation results from these algorithms, the overall accuracies in terms of correctly identified cases were 90.4867%, 88.0581%, and 91.8142%, respectively. Through a series of tests on real electric vehicle datasets gathered from individual users, the effectiveness of these classifier-based rule-based models is evaluated. The overall experimental findings and discussions can assist businesses of electric vehicle companies in making decisions on the development of intelligent systems for electric vehicle users. The data-mining algorithm's extraction of information in this   study improved our comprehension of the customer base's composition regarding purchasing electric vehicles and the company's assessment of the appropriate course of action for offering guidance, training, and expertise.