Predicting Product Purchase using Linear Classification Algorithms
K. Maheswari1, K. Ponmozhi2
1Dr. K. Maheswari, Department of Computer Applications, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
2Dr. K. Ponmozhi, Department of Computer Applications, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
Manuscript received on 02 December 2019 | Revised Manuscript received on 20 December 2019 | Manuscript Published on 31 December 2019 | PP: 692-698 | Volume-8 Issue-4S2 December 2019 | Retrieval Number: D11121284S219/2019©BEIESP | DOI: 10.35940/ijrte.D1112.1284S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Classification, Logistic Regression, Machine Learning and Naïve Bayes.
Scope of the Article: Classification