  Supply and demand are two fundamental concepts of sellers and customers.
Predicting demand accurately is critical for organizations in order to be able
to make plans. In this paper, we propose a new approach for demand prediction
on an e-commerce web site. The proposed model differs from earlier models in
several ways. The business model used in the e-commerce web site, for which the
model is implemented, includes many sellers that sell the same product at the
same time at different prices where the company operates a market place model.
The demand prediction for such a model should consider the price of the same
product sold by competing sellers along the features of these sellers. In this
study we first applied different regression algorithms for specific set of
products of one department of a company that is one of the most popular online
e-commerce companies in Turkey. Then we used stacked generalization or also
known as stacking ensemble learning to predict demand. Finally, all the
approaches are evaluated on a real world data set obtained from the e-commerce
company. The experimental results show that some of the machine learning
methods do produce almost as good results as the stacked generalization method.
