Prediction of Consumer Purchase Intension using Machine Learning Classifiers

Abstract

Digital marketing is a part or element of marketing which uses the internet and connected based
on digital mechanisms. It was the main grounds that changed the way trademarks and businesses utilize the
high-technology for retail, trading and marketing. As marketing plans are majorly built over digital
platforms in everyday life, and as people are largely using digital gadgets and appliancesrather than visiting
the stores in person, digital marketing thrush have become widespread, and have become much established.
One major changewhich occurred in traditional marketing was the "emergence of digital marketing". This
led to the reengineering of marketing approaches toadjust to thevital change in traditional merchandise. The
research gives us the fundamentalunderstanding about the customer outlook on the advertisements on social
media and the purchasingbehaviour online. In today’s world, almost everyone share their information on the
internet and also post their advertisements so that they could stay in touch and get connected with the people
much faster. Web scraping is mainly used for retrieval (extraction) of data. This retrieves greater number of
information and data online. The data which is being retrieved is unstructured. These unstructured data are
collected and stored structurally. The purchase intention of the customer is that which helps us to have a
clear idea about individuals, conglomeration or concern and other activities linked with assession,
dispensability of materials and productsalong with the compartment of the customers. This also comprises
the intellectual and psychological reactions which is either the antecedent or the descendant of these tasks.
The goal is to explore the dataset given, using various machine learning techniques for prediction of product
rating by forecasting the results accurately. It also provides an analysis of the parameters with regard to the
performance by detecting the calculation of accuracy. Furthermore, it compares various machine learning
algorithms for the given dataset and examine the performance GUI based User Interface evaluation of
ratings with price of the products.