Retail Shop Sales Forecast by Enhanced Feature Extraction with Association Rule Learning

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