| dc.description.abstract | Much effort has been devoted to understanding learning and reasoning in artificial
intelligence, giving rise to a wide collection of models. For the most part, these models
focus on some observed characteristic of human learning, such as induction or analogy, in
an effort to emulate human abilities.
Some desirable properties for artificial learning systems include incrementality, non monotonicity,
inconsistency and conflicting defaults handling, abstraction, self organization,
generalization and computational tractability.
Estimation and prediction of customer buying habits in a hyper market a challenge to
many traders. Business organizations have their respective high and low business
seasons. Nevertheless the ability to precisely forecast buying habits of a specific customer
is of great significance to a hyper market. This is significant in determining what products
and/or services to avail to different customers, at what times and places.
Past customer buying habits have been used to determine a pattern of consumer
behaviour hence project into the future. In this proposal, I put forward a forecasting
model capable of giving more precise predictions about a customer response to products.
The project is to use Artificial Neural Network (ANN) to generate a forecasting model
expected to be applied by a hypermarket. The model will also provide a basis of
comparison between other forecast estimation techniques -0uch as experience) and the
Artificial Neural Network based model. | en |
