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CONVERTING CATEGORICAL FEATURES WITH MULTIPLE CATEGORIES USING THE ONE-HOT ENCODING METHOD
Gubaidullina Darya Andreevna
Ufa University of Science and Technology
Neftekamsk branch, Faculty of economic and mathematical, Student
Ufa University of Science and Technology
Neftekamsk branch, Faculty of economic and mathematical, Student
Abstract
The article is devoted to the method of converting categorical features with several unique values into a numerical format using one-hot encoding. A practical example of applying this approach in Python using the pandas library is considered. The importance of correctly encoding categories for the correct perception of machine learning models and the prevention of false relationships between categories is explained. It is emphasized that one-hot encoding is an effective and widely used data preprocessing tool.
Keywords: categorical features, data preprocessing, data transformation, feature binarization, machine learning, numerical encoding, One-Hot Encoding, pandas
Category: 05.00.00 Technical sciences
Article reference:
Gubaidullina D.A. Converting categorical features with multiple categories using the one-hot encoding method // Modern scientific researches and innovations. 2026. № 2 [Electronic journal]. URL: https://web.snauka.ru/en/issues/2026/02/104165
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