Prediction of Women’s Fashion Product Expense in Hanoi City Using Artificial Neural Network

Abstract

A person's fashion product expense plays an important role in building a garment company's product and pricing strategy. This article presents the results of predicting the fashion expenses of women aged 25 to 55 in Hanoi city. A linear multivariable model determined by the Bayesion Model Average (BMA) was applied to predict expenditures based on a set of 150 women survey data. The 3 layers feed-forward neural network model with 8 input neurons, 4 (+1) hidden layer neurons, and 1 output layer neuron, The Sigmoid activation function, trained by the error backpropagation algorithm has been established on software R specifically predicts the value of women's fashion expense with the above data set. The results show that the artificial neural network (ANN) has been set up allowing to prediction of fashion expenses accurately. The expenditure values predicted by the ANN and the actual value are linearly correlated with R2 = 0.987; The prediction results by ANN are more accurate than the defined multivariable linear model.