Investigations on E-commerce Data for Forecasting the Efficient Promotional Platform Using Supervised Machine Learning

The technological advancements provide various platforms for e-commerce companies to leverage the maximum benefits for higher revenue generation. Websites and product applications are the prominently used platforms by the users or customers to identify the various features of the product while also adopting this mode for purchase. E-commerce industries consider the determination of the most ideal platform as a critical task. Datas pertaining to user's inclination towards website or product applications are collected through registration credentials. With the available data which is big in size, it becomes a herculean task to precisely predict. Nevertheless conventional tools have been adopted by the industries whose results are not satisfactory and promote further explorations with advanced tools. Accounting for these complex tasks, this study attempts to employ linear regression, a versatile tool for predicting the most popular platform that generates maximum revenue. Various machine learning libraries have been used to scrutinize each and every parameter like number of registrants and the frequency of visits to arrive at a predicted value that would facilitate e-commerce industries for promoting the specific business platforms and eventually reap higher revenue. The proposed technique may be extrapolated for parametric forecasting by any industry with local customization.