Analysis on Prediction of Customer Purchasing Decisions using Machine Learning
Nehal Raj1, Rajneesh Kumar Singh2, Satyajee Srivastava3

1Nehal Raj*, Student, Department of School of Computer Science and Engineering, Galgotias University, Greater Noida, India
2Rajneesh Kumar Singh, Student, Department of School of Computer Science and Engineering, Galgotias University, Greater Noida, India
3Satyajee Srivastava, Assistant Professor, Department of School of Computer Science and Engineering, Galgotias University, Greater Noida, India

Manuscript received on March 10, 2021. | Revised Manuscript received on March 22, 2021. | Manuscript published on April 30, 2021. | PP: 43-46 | Volume-10 Issue-4, April 2021. | Retrieval Number: 100.1/ijeat.D23170410421 | DOI: 10.35940/ijeat.D2317.0410421
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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: In our day-to-day life, everyone settles on choices on whether to purchase an item or not. In a couple of cases, the choice depends on cost however on numerous occasions the buying choice is more intricate, still, numerous other reasons may be cogitated prior to the last decision is take. Within large-scale industries, understanding existing consumer’s purchasing behavior towards the product is more important to drive a business to the next level. In the context to expand the business on a large scale understanding, the customer interest is more important. To understand the behavior of customers and their interest in the products we need some new technologies and a large amount of data. In this paper we present a progression of examinations, investigate and analyze the exhibitions of various ML strategies, and talk about the meaning of the discoveries with regards to public arrangement and purchaser buying choice. Utilizing an enormous certifiable informational collection (which will be unveiled after the distribution of this work), we present a progression of examinations, dissect and look at the exhibitions of various ML procedures, and talk about the meaning of the discoveries with regards to public strategy and consumer buying Decision.
Keywords: Purchasing, Clustering, Datasets, Random Forest, Naïve Bayes Classifier.