Development of an intelligent system automating managerial decision-making using big data

Abstract

The object of the study is the automotive industry of the Republic of Kazakhstan. The subject of the study is the management of the decision-making 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, high-quality 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