Retail Shop Sales Forecast by Enhanced Feature Extraction with Association Rule Learning
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Abstract
Sales is a basic standpoint for business growth. Demand for consumer products decides the success rate of every business resulting in a profit. Proper analysis of the consumer interest in a particular product decides future sales. The ordinary tactics for sales and promotion objectives no longer help businesses keep up with the speed of a challenging market because it goes out with no knowledge of consumer buying habits. As a consequence of technological developments, significant changes can be seen in the domains of marketing and selling. As a result of such developments, multiple important factors such as consumers' buying habits, target people, and forecasting sales for the coming years can be readily determined, assisting the sales crew in developing strategies to achieve an upsurge in their company. This paper investigates the use of Association Rule Learning with Feature Extraction to forecast sales performance in order to recognise buyers. The consumer's related goods are identified using the association framework. Data on buying activities are derived from purchase invoices provided by the business. The outcome of both is utilized to create a company strategy. Support, Confidence, and Lift are the metrics used for evaluating the quality of association rules produced by the model. Based on the buyers’ preferences this paper forecasts retail shop sales and predicts the association relation between the products by feature extraction with Association rule learning to improve future sales. The suggested approach is employed to discover the most common pairings of items found in the business. This will assist with promotion and revenue. This method can help you find intriguing cross-selling and connected goods. The WEKA tool was used to evaluate the correctness of the Association rule that was created.
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