Open AccessPredicting Product Purchase using Linear Classification Algorithms
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d1112.1284s219
Subject(s) - computer science , product (mathematics) , quality (philosophy) , task (project management) , linear discriminant analysis , naive bayes classifier , machine learning , algorithm , competition (biology) , logistic regression , new product development , support vector machine , artificial intelligence , data mining , marketing , mathematics , business , engineering , ecology , philosophy , geometry , systems engineering , epistemology , biology
The customer buys the product based on many factors. There is no adequate and properly defined logic for such matter. The customer must satisfy when they see their product itself. They have to trust its quality, price, lifetime of the product, no side effect behavior, name of the product, packing of the product and finally cost. These factors may vary time to time, day to day and even sec to sec. The competition among sellers is also increasing day by day. The choice of choosing the product for customer is more, confused and risky also. Establishing a good relationship among seller and buyer will increase the customer. The retaining of customer is a challenging task. To solve this problem, a model is developed using machine learning algorithms svm, Naïve Bayes, Logistic Regression and fisher’s linear discriminant analysis. This model predicts the buying habit of a user/customer. The classification is performed on product purchase dataset and its performance is compared to find which algorithm performs well for this particular dataset. This work is implemented in R software.