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Market Basket Analysis (MBA), it is the process of modeling technique, if the customer buys the certain group of item, at the next period of purchase they may like to buy the same items. MBA, it has determination and prediction of customer’s behavior based on the pattern of previous customer. MBA is applied not only in retail shop. In the existing, Market basket prediction, i.e., according to the customer’s purchase, it will provide the shopping list for the next purchase. The same approaches are not used for all the time it may differ based on their decision. The following four approaches are used to identify the individual customer’s behavior. They are co-occurrence, sequentiality, periodicity and recurrence. This project defines a Temporal Annotated Recurring Sequence (TARS) to identify the individual purchasing behavior and TARS based predictor to solve the basket prediction problem (i.e., suggestion for next purchase).
MBA based on multidimensional model is used to conduct a study of Market basket analysis, to make a choice of purchasing and sale of stocks in an equity market. In this thesis using MBA for improving methods of arranging products on shelves are identified. The proposed project is mainly focuses on association rule for recommendation system and cosine model for transaction similarity. The best sales of the market are only predicted by using profit and loss. MBA it also provides profit and loss the market.
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