The Use of Data Mining to Predict Sales in Building Supply Stores Using the Apriori Method
Abstract
Data mining is a crucial technique for analyzing large-scale data to uncover hidden patterns that support strategic business decision-making. This study applies the Apriori method to analyze sales transaction data in a hardware store, aiming to identify consumer purchasing patterns. The Apriori method is chosen for its ability to discover association rules that reveal products frequently bought together. The analysis identifies significant product combinations, such as related building materials or complementary accessories, providing strategic insights for store owners. The results demonstrate that the Apriori method can assist in designing effective sales strategies, such as bundling offers, optimizing product placement, and targeted promotions. This implementation supports more optimal business planning, enhances customer satisfaction, and contributes to increased store revenue.
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