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Методы машинного обучения в ÑÐ¸ÑÑ‚ÐµÐ¼Ð°Ñ Ð¿Ñ€Ð¾Ð³Ð½Ð¾Ð·Ð¸Ñ€Ð¾Ð²Ð°Ð½Ð¸Ñ Ð»ÐµÑÐ½Ñ‹Ñ Ð¿Ð¾Ð¶Ð°Ñ€Ð¾Ð²

выпускная квалификационная работа бакалавра

Методы машинного обучения в ÑÐ¸ÑÑ‚ÐµÐ¼Ð°Ñ Ð¿Ñ€Ð¾Ð³Ð½Ð¾Ð·Ð¸Ñ€Ð¾Ð²Ð°Ð½Ð¸Ñ Ð»ÐµÑÐ½Ñ‹Ñ Ð¿Ð¾Ð¶Ð°Ñ€Ð¾Ð²

Abstract

Тема выпускной квалификационной работы: «Методы машинного обучения в системах прогнозирования лесных пожаров». Данная работа посвящена анализу применения методов машинного обучения в обработке данных о лесных пожарах в парке Монтесиньо. В ходе работы решались следующие задачи:- Изучение предметной области по тематике влияния лесных пожаров на современную жизнь человека.- Анализ и обработка входных данных. - Реализация моделей методов машинного обучения.- Применение различных методов обучения для нескольких вариантов обработанных данных.- Анализ результатов точности, полученных в результате обучения .В результате было получена программа, позволяющая проводить анализ нескольких алгоритмов бинарной классификации при различных параметрах обработки данных с использованием предоставленного набора данных.

Topic of the final qualification work: "Methods of machine learning in forest fire forecasting systems". This work is devoted to the analysis of the application of machine learning methods in the processing of data on forest fires in Montesigno Park. During the work the following tasks were solved:- Study of the subject area on the subject of the impact of forest fires on modern human life.- Analysis and processing of input data. - Implementation of models of machine learning methods.- Application of various training methods for several variants of the processed data.- Analysis of accuracy results obtained as a result of training. As a result, a program was obtained that allows the analysis of several binary classification algorithms with various data processing parameters using the provided data set.

Keywords

нормализация, machine learning, normalization, binary classification, анализ данныÑ, бинарная классификация, data analysis, методы машинного обучения, подготовка данныÑ, data preparation

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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