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Comparative study evaluates categorical variable encoding techniques in neural network classifiers, highlighting methods for converting qualitative inputs into numbers.
In classification analysis, the dependent variable is frequently influenced not only by ratio scale variables, but also by qualitative (nominal scale) variables. Machine Learning algorithms accept only numerical inputs, hence, it is necessary to encode these categorical variables into numerical values using encoding techniques.
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Potdar et al. (2017) studied this question.
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