FOCUS ON ARTIFICIAL INTELLIGENCE FOR PREDICTING THE OUTFLOW OF CLIENTS FROM ON-LINE EDUCATION SITES
DOI:
https://doi.org/10.33042/2522-1809-2022-4-171-2-6Keywords:
neural networks, deep learning, machine learning, regression, prediction, estimation, data analysisAbstract
The article examines the task of assessing the cost of housing in the cities of Ukraine. The purpose of this work is to simplify the determination of the value of apartments on the real estate market using machine learning technologies. To solve this problem, it is proposed to use a program module in Python using the Sequential direct distribution model of the keras library. A program was created that estimates the value of apartments according to their parameters using a neural network. The importance of forecasting in the field of real estate is shown, because the housing market is a systemic part of the regional economy. The results of the software application, which consists of two parts, are presented. The first program collects the necessary data for training a neural network about apartments from the OLX site ads, their structuring and recording in a csv file. The second program provides tools for preliminary analysis of the collected data, after which they are cleaned, divided into training and test samples and trained on their basis by a multilayer neural network of direct propagation using a machine learning algorithm. The layers API of the keras library was used to design the neural network, which allows the user to create arbitrary layers. For regularization, the keras.regularizers tool, which is also in the layers API, is used. To configure model metrics, the compile method was used. Three hidden layers were defined, for each of which 512 neurons were introduced and the Relu activation function was chosen. Calculations of the correlation of prediction indicators and error curves of machine learning are given. As a result of testing the trained neural network on a test set of 652 examples, an average absolute error of 3570.88 was obtained, and the accuracy of the model was approximately 85%. Thus, the neural network has reached an acceptable level of accuracy for estimating the cost of apartments in the city of Kharkiv. Ways to reduce test errors and learning errors using cross-validation are proposed. Concepts of learning hyper-parameters and their regularization are considered.
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