Result: ЗАДАЧА ПРОГНОЗУВАННЯ В МАШИННОМУ НАВЧАННІ.

The article examines the use of machine learning methods, particularly linear regression, for solving forecasting problems. The main focus is on the development, training, and evaluation of linear regression models using a dataset containing indicators of the happiness levels of populations in various countries, depending on their socioeconomic characteristics. The study demonstrates the stages of model construction: from selecting initial data, preprocessing the dataset, to visualizing the obtained results using Python programming language and its libraries. The research highlights key stages, such as feature scaling, splitting data into training and test subsets, and using primary metrics to evaluate the quality of predictive models, including MSE, RMSE, MAE, and the coefficient of determination R². A comparison of model accuracy on training and test datasets is presented, allowing the assessment of the model's generalization ability. The findings confirm that scaling methods, particularly normalization and standardization, significantly enhance the performance of regression models, while the use of the sklearn library ensures intuitive and straightforward implementation. The article also discusses the features and limitations of these methods, including their sensitivity to input data quality and the necessity of handling outliers. The conclusions emphasize the versatility of linear regression as a method for analyzing and identifying cause-and-effect relationships based on dependencies between variables, as well as the importance of preliminary data analysis and model optimization to improve prediction accuracy and apply further data generalization methods. The article illustrates the potential of machine learning methods based on linear regression for solving practical problems in various fields of human activity. [ABSTRACT FROM AUTHOR]

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