  Many economic applications including optimal pricing and inventory management
requires prediction of demand based on sales data and estimation of sales
reaction to a price change. There is a wide range of econometric approaches
which are used to correct a bias in estimates of demand parameters on censored
sales data. These approaches can also be applied to various classes of machine
learning models to reduce the prediction error of sales volume. In this study
we construct two ensemble models for demand prediction with and without
accounting for demand censorship. Accounting for sales censorship is based on
the idea of censored quantile regression method where the model estimation is
splitted on two separate parts: a) prediction of zero sales by classification
model; and b) prediction of non-zero sales by regression model. Models with and
without accounting for censorship are based on the predictions aggregations of
Least squares, Ridge and Lasso regressions and Random Forest model. Having
estimated the predictive properties of both models, we empirically test the
best predictive power of the model that takes into account the censored nature
of demand. We also show that machine learning method with censorship accounting
provide bias corrected estimates of demand sensitivity for price change similar
to econometric models.
