Authors :
Uwibambe Josiane; Dr. Musoni Wilson
Volume/Issue :
Volume 8 - 2023, Issue 2 - February
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3XH41tX
DOI :
https://doi.org/10.5281/zenodo.7659248
Abstract :
Estimating performance in relation to the
expectation is a key component of many machine
learning algorithms for decision-making. Measuring
performance in accordance with expectations may not be
very useful in many real-world situations. In this article,
with deployment to a public dataset, we examine the
viability and comparative analysis of Deep Learning
techniques to anticipating the demand problem. We
compare Deep Learning performance to that of various
model approaches, such as Random Forest, Gradient
Boosted Trees, and Support Vector Machine, using
RMSE performance criteria. The forecasting issue is
crucial for organizational decision-making. When
making strategic decisions on valuable resources, riskaverse goals should be taken into account. This article
aims to demonstrate the usage of ML models for
forecasting and decision making to improve
performance of data analysis of an organization. And to
demonstrate that, especially when decision-makers are
dealing with complicated limitations data, a Deep
Learning algorithm can be a dominant answer to
Machine Learning challenges for forecasting and
decision-making.
Estimating performance in relation to the
expectation is a key component of many machine
learning algorithms for decision-making. Measuring
performance in accordance with expectations may not be
very useful in many real-world situations. In this article,
with deployment to a public dataset, we examine the
viability and comparative analysis of Deep Learning
techniques to anticipating the demand problem. We
compare Deep Learning performance to that of various
model approaches, such as Random Forest, Gradient
Boosted Trees, and Support Vector Machine, using
RMSE performance criteria. The forecasting issue is
crucial for organizational decision-making. When
making strategic decisions on valuable resources, riskaverse goals should be taken into account. This article
aims to demonstrate the usage of ML models for
forecasting and decision making to improve
performance of data analysis of an organization. And to
demonstrate that, especially when decision-makers are
dealing with complicated limitations data, a Deep
Learning algorithm can be a dominant answer to
Machine Learning challenges for forecasting and
decision-making.
