Abstract: With changing customer trends and increasing competition, retailers are expected to react quickly and execute their machine learning (ML) pipeline strategies in a very short window and find which algorithm/model works better for their problem statement. Present disclosure provides method and system for dynamic retail requirements. The system uses algorithm container framework that helps in identifying best fit algorithm to solve retail problem. In particular, algorithm container framework finds best hyperparameter and functional parameters of algorithm to attain desired optimal result. The system also uses algorithm calibrator for fine tuning parameters and hyperparameters used for algorithm to achieve optimal result. Once optimal results are obtained, system logs inputs such as best algorithm and their associated optimal results from ML pipeline into algorithm calibrator repository which is then used for fine tuning of stages to obtain accurate results.

Abstract: A method and system is provided for normalizing a unit of measures (UOM) of a product. Generally different retailers use different product and format for providing their product description. According to the present invention, the UOM string is extracted from the product information, which is retrieved by the web scrapper. The UOM string is then converted into a standard UOM based on the review by the UOM data dictionary. And finally the converted standard UOM is normalized using a normalization module. The normalized UOM can be used for further application. Another embodiment of the present disclosure also provides a method for improving the product matching efficiency for deciding a competitive pricing of the product.