Research on intelligent prediction model consumer behaviour using support vector machine and algorithms optimization

In order to shift towards organic products, consumer behaviour is crucial. This study's major goal is to comprehend how eco-conscious consumers behave and whether they intend to buy organic food. The purpose of the study is to identify the variables affecting customer attitudes towards organic food. Face-to-face interviews with closed-ended questions from a standardised questionnaire served as the method for gathering data. The survey was completed by a total of 463 people. By combining the training of two or more distinct algorithms, the algorithm's capacity for generalization can frequently be significantly enhanced to enhance the prediction effect. An ALRSVMP (Amalgam Logistic Regression Support Vector Machine Algorithms for Prediction) model for customer purchase behavior is the result of this study, which combines logistic regression with support vector machine algorithms. It then conducts an empirical investigation on the model's efficacy. The empirical findings demonstrate that the fusion model outperforms the single model in terms of prediction accuracy. According to this study, businesses may create efficient marketing plans and tactics to have a beneficial impact on consumers. They can highlight the high quality and health advantages of organic food. To encourage people to purchase organic food, they can make these products widely accessible. This study provides insightful information on consumer performance with respect to organic food by investigating the factors that influence consumers' willingness to purchase healthy produce in the Indian context. The marketing of organic foods in other countries can benefit from the lessons learned.