Аннотации:
The object of the study is the automotive
industry of the Republic of Kazakhstan. The
subject of the study is the management of the
decisionmaking process in assessing the consumer capabilities of potential customers of
car dealerships, the process of forecasting
car pricing.
A method using a global search engine
optimization algorithm, a forest conveyor line
with a random forest model with Bayesian
optimization (RFBO), is proposed.
The algorithm of the method is as follows:
– obtaining and processing initial data
taking into account the degree of uncertainty;
– formation of the optimization vector;
– creation of descendant vectors;
– ordering of vectors in descending order;
– reducing the dimension of the feature
space;
– knowledge base training.
In the presented work, data from websites
www.m.Kolesa.kz, www.Cars.com and the
average values of the median salary in the
Republic of Kazakhstan were used to create
a knowledge base, the program code of the
platform was created using the Visual Studio
Code in the Python language.
The task to be solved was to predict car
prices and assess the consumer capabilities of
potential car dealership customers.
We evaluate our solution based on a dataset that was created by analyzing several car
classified sites and data on potential customers. Our results show that the accuracy of
the model training was 92.1 %, and the accuracy of forecasting car prices and evaluating
the consumer capabilities of potential customers was 87.3 % – this is primarily due to
lower prediction errors than those of the estimated regressors using the same set of input
data, highquality object mapping and a more
competitive RFBO algorithm, superior to simple linear models.
The developed software solution should
be used for making automated management
decisions by car dealerships and credit organizations
