Analysis of Reusability of Used Clothes Using Machine Learning Algorithms

Abstract

With $45 billion in apparel exports in 2022, Bangladesh is the second-ranked country in the world for apparel exports also the second largest producer of textile waste. The Bangladeshi apparel sector is expected to generate US$10.15 billion in revenue by 2023. Purchasing power of Bangladeshi people have increased and expenses on apparel product has also increased. The habit of repeating clothes that worn once has decreased which makes our wardrobe filled with lots of rarely used clothes and after a certain time we throw them as a wastage. Our study is aimed to develop a machine learning algorithm to predict clothing reusability. For our model we use clothing type, fabric type, usage duration, damage, distortion and color information of a used cloths. We use Classification algorithms for constructing our predictive model. We have applied five classification machine learning algorithm which are Decision Tree, Random Forest, Naïve Bayes, Logistic Regression and SVM. With the given information of a used cloth our model can predict the reusability option for it, the options are: resale, reuse and turn into jhoot product. By reselling a used cloths one can earn save some money and on the other hand people having less money can get a good product. Reusing clothing items means using to create a new apparel item or using in home craft. The last option of reusing is turning into jhoot products, cloths which have used more than their average life cycle are used in jhoot. This research achieved model accuracy between 79% to 85% on predicting reusability of different apparel items. Future study will explore new machine learning approaches with larger dataset and also enable a system that will be useful for textile industry to achieve sustainability in clothing wastage.