A PSO-GBR Solution for Association Rule Optimization on Supermarket Sales

In the era of big data and cloud computing, digital records of supermarket sales data and other accompanying factors are ubiquitous. However, less than optimal WeeklySale s can occur due to several factors influencing it. This study proposes particle swarm optimization with gradient boosting regression (PSO-GBR) as a solution for optimizing the association rule in supermarket sales based on a regression model that can predict WeeklySales. As a benchmark for this research, we compare our proposed GBR with two legacy prediction methods: linear regression (LR) and AdaBoost Regression (ABR). The first step is data preparation. Then we develop a model that can predict sales from the dataset using GBR. The next step is to evaluate the model with benchmark methods. Then with an optimum regression method, we optimize sales using PSO. The last step is to show that the method provides optimum WeeklySale s results. The results show that our proposed GBR has a higher
$R^{2}$
score than LR and ABR, namely 0.95 and 0.94 for train data and test data, respectively. Then PSO is proven to optimize sales using the GBR model as a cost function. PSO can increase the ten lowest WeeklySales of the actual dataset from a total of US$ 45 to a total of US$ 2, 296.9 by selecting a more optimized Department according to the GBR prediction. This research proves that we can use a regression model with good performance for a cost function in PSO optimization for department sales.