Categorizing Attire through the Fashion MNIST Dataset

Abstract

The ongoing expansion of the online fashion market is leading fashion websites to accumulate increasing volumes of data from diverse brands. Consequently, the task of classifying various garments has become challenging for numerous websites. Addressing this challenge necessitates the implementation of a highly accurate algorithm capable of identifying garments. Such an algorithm can prove instrumental for companies in the clothing sales sector, aiding in comprehending the profiles of potential buyers. It enables businesses to tailor their sales strategies to specific niches, develop targeted campaigns aligned with customer preferences, and enhance overall user experience. This project is aimed to find the best model with the highest accuracy and precision results. Models like Logistic Regression, Decision Tree Classifier, Random Forest Classifier and some other models are used in this project to classify the garments. To train and test these models, the Fashion MNIST dataset is used. Among all the models that are used here, the model that shows the best performance is suggested to the fashion website.