SALES PREDICTION WITH MACHINE LEARNING

Abstract

Using machine-learning models for sales forecasting and forecasting
analytics, we study the usage of machine-learning models in this research. One
of the key aims of this research is to investigate a range of methods fo r using
machine learning for sales forecasting, including case studies, as part of its
overall goal. The fact that machine learning is becoming increasingly wid ely
used has been taken into account in this study. It is possible to use this effect to
produce sales forecasts when there is a limited amount of previous data fo r a
certain sales time series. For example, when a new product or shop is
established. It has been examined if a stacking method may be used to bui ld
regression models for single models in order to develop regression models fo r
single models. The findings suggest that by using stacking methods, we may be
able to improve the performance of predictive models for time series
forecasting in the sales domain. In this day of the internet, there is an
enormous quantity of data being produced, and man is unable to analyse it a l l
on his or her own. This has led to the development of a variety of machine
learning methods that have been used to achieve this goal. This project aims to
predict the sales of a retail to store using a range of machine learning
algorithms with the goal of determining which way will perform the best for
our unique issue statement. Our technique included the use of both classic
regression methods and boosting approaches, and we observed that b oo st ing
algorithms outperformed traditional regression procedures. In this present
numerotrend, all businesses, including non-profit organisations and concerns,
flourish in creating future sales targets and implementing strategies and
approaches to achieve t