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Analysis on Prediction of Customer Purchasing Decisions using Machine Learning

Authors: Raj, Nehal; Rajneesh Kumar Singh; Satyajee Srivastava;

Analysis on Prediction of Customer Purchasing Decisions using Machine Learning

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

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Keywords

Purchasing, Clustering, Datasets, Random Forest, Naïve Bayes Classifier., 100.1/ijeat.D23170410421, 2249-8958

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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