The Use of Machine Learning for Sneaker Price Forecasting

Abstract

As a self-organizing culture, sneaker culture emerged among teens without external influence. With the advent of the Internet and other digital technologies, sports shoes have acquired monetary and aesthetic value via manual speculation, setting off a flood of such speculation in which every individual is involved. The explosive process behind speculative footwear warrants contemplation as a collective activity involving humans. Fry shoes follow a pattern of practical logic and a series of logical steps. To uncover the ever-changing process of convergence from micro to macro behavior, threshold theory is an essential tool. Hence, the sneaker industry may benefit from machine learning-based pricing predictions. This research use stockx as its data set. The research examines the cost of athletic shoes by analyzing data using a machine learning approach. Predictions were made using four different linear regression models. The accuracy of the OLS regression model is determined to be high. Accordingly, the OLS model works well with this data set.
Predicting the price of shoes using machine learning and linear regression