Regression

The use of machine learning models for electricity consumption prediction for smart grid has been investigated. It was found that data pre-processing can improve the performance of the energy consumption prediction model, while machine learning algorithms can improve model prediction accuracy through the integration of multiple algorithms and hyperparameter optimization. It was found that the ensemble learning method can provide better prediction accuracy than each individual method by combining the strong features of different methods that have different structural characteristics.

Correct valuation of real estate plays a crucial role in the process of buying and selling. We have carefully studied the existing applications with which we carry out real estate transactions, described their features, advantages and disadvantages. The developed model will help sellers get an estimate of their property according to the parameters entered, which can serve as a starting point for establishing the final value.

This article presents results related to the direct solution of the polynomial regression parameters based on the analytical solving of regression equations. The analytical solution is based on the normalization of the values of independent quantity with equidistance steps. The proposed solution does not need to directly solve a system of polynomial regression equations. The direct expressions to calculate estimators of regression coefficients, their standard deviations, and also standard and expanded deviation of polynomial functions are given.