A Subtle Design of Prediction Models Using Machine Learning Algorithms for Advocating Selection and Forecasting Sales of Garments: A Case Study

In this article, the predictive analysis is conducted for a garment retail dataset that contains the attributes of the dresses and sales information. Precisely, Random Forest (RF), Linear Regression (LR), Support Vector Machine (SVM), and Decision Tree (DT) algorithms are used for classification. That is, advising whether the dresses should be kept in store or not by automating the process of the recommendation. Moreover, two variants of the datasets are given as input to the said algorithms apart from the raw dataset. One variant is obtained through feature selection and another uses the concept of dummy variable since the majority of the features are categorical. In addition, the demand for sales is estimated over a period. Auto-Regressive Integrated Moving Average (ARIMA) is applied in particular to achieve the forecasting of the sales. The dataset contains fourteen features of dresses and sales data of alternative days over a month. The experiments on the case study show that RF algorithm is good at the classification although it is marginally better than LR. Also, the sales forecasting is producing results in an acceptable range as per the relevant performance metrics. Overall, the proposed methodology of this paper helps in the decision-making of fashion retail.