Abstract:
"
E-commerce has grown exponentially in the last two years and the pandemic is one of the
important reasons for the huge growth. Moreover, E-commerce has caused tremendous
changes in the transformation of the shopping experience from traditional shopping to digital
shopping. This was made possible due to the wide range of technologies and computer
networks that connect the countries and continents and synchronize them into a global
village. Accurate sales prediction plays an essential role in reducing costs and preventing the
merchants from holding huge amounts of cash flow due to the waste of resources,
replenishment of stocks and loss of profits caused by goods in short supply. This research
study attempts to predict future sales for an E-commerce business based on data of historic
sales. Firstly, this research carries out a thorough critical analysis of multiple previous
research studies to allow the reader to understand the background and the process of this
research. Additionally, it analyses the factors impacting sales forecasting and also, uses the
knowledge gained from this and proposes four different machine learning models which can
be used to forecast the sales based on the historic sales data. The models which are used here
are boosted decision tree regression, neural network regression, decision forest regression and
bayesian linear regression. Finally, the research paper analyzes the accuracy measures for the
above mentioned machine learning models on the available sales data. This study and the
results obtained from this research can help e-commerce businesses to have better
management in their future operations.
"
