Abstract:
Machine learning (ML) and the use of data mining techniques are increasingly important in
real-world situations. Every industry, including education, healthcare, engineering, sales,
entertainment, and transportation, is benefiting from these applications’ innovative nature.
Due to the exponential increase of the enormous volumes of data used in commercial transactions, the business industry has significant obstacles in identifying an accurate technique
and efficient prediction strategy. The conventional strategy for achieving sales and marketing objectives doesn’t help businesses keep up with the pace of the competitive market
since it lacks knowledge about customers’ buying habits. As a result of the advancement in
machine learning, significant changes are observed in the field of sales and marketing. The
majority of commercial businesses rely largely on demand forecasting and knowledge of
market trends. In order to improve prediction accuracy, data mining techniques are serving
as efficient tools for uncovering hidden knowledge from a sizable dataset. The aim of this
project is to develop a software prototype as a web service for predicting the outlet items
sales of companies. The methodology of data mining with machine learning models like
Linear Regression, Decision Tree, Random Forest, and XGBoost Regressor is used in this
project to predict sales, and the best model for prediction is recommended based on the
results analysis. Apart from the prediction, this prototype will show the graphical representation of the impact and correlations of variables as well as the outcome of the models with
the predicted results. This project work will assist companies in gaining a general understanding of how to position products and outlets to give a positive customer experience that
will boost sales and revenue.
