Model Prediction Of The Next Runway Model Using Decision Tree And Random Forest (case Study: Big Four Fashion Week)

Dwita Adilah, Andry Alamsyah

Abstract

Abstract Fashion industry contributes 4% of global market share with USD 385.7 billion-dollar market value worldwide. Fashion Week, the most prominent event in fashion industry, is a combination between art and commerce. Fashion models is the talent that plays a big role during the fashion week, in which their aesthetic is highly associated in presenting designers’ work. However, their appearance in social network has built a deeper relationship to the industry. Choosing the talent out hundreds of faces is a challenge for casting director. The fast movement of fashion business has never evolved closely to digital utilization. This study aims to imitate the wisdom of traditional talent scouting process into an automation model based on machine learning practice by implementing Decision Tree, and Random Forest. In the era of fashion 4.0, shifting the traditional system into an automation model leads to revolution of fashion production, in this case fashion show production. Our framework is able to imitate talent scouting process to select the upcoming fashion model models appear on 2019 Fall/Winter Fashion Week with accuracy 74.57% using Random Forest. Keywords: Fashion Industry, Machine Learning, Prediction, Decision Tree, Random Forest.

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