| dc.description.abstract | In everyday business, managers not only have to oversee the day-to-day operations, but also
should be able to anticipate the different possibilities in the future so that they can prepare
accordingly. From inventory management to coming up with new products and services, it is
important that the key decision makers to have the relevant information that can facilitate better
decision making process. By using machine learning and forecasting techniques, the store
managers can assess the upcoming demands in a much more systematic manner instead of
relying solely on intuitions.
This report aims to explore and find the advantages of different machine learnings
methods to predict the sales. From Simple linear regressions, random forest, ARIMA, to
Adaptive Lasso regressions, we aimed to test the forecasting ability of each technique on the
data sets of the sales that we acquire from SandwichworkZ. We also identify different factors
that are both intrinsic and extrinsic to the sales of the restaurant so that we can build a robust
model for forecasting sales not only for the restaurant businesses but also for other businesses
in general.
From the different machine learning models that we have tested on the data that we
have trained, we can see that Random Forest and ARIMA method yields good result in
forecasting the sales trends and cycles. It is worth noting that the Adaptive Lasso capture the
peaks and lows of the data the best among all the models tested and yields lower mean
absolute percentage error (MSPE) than other models.
The models studied can help managers understand the different tools available to
predict their sales not only to know the upcoming trends but also to make better decisions,
reduce preventable losses, and grab the future opportunities. | en_US |
