SLIIT International Conference on Engineering and Technology [SICET]

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SLIIT International Conference on Engineering and Technology is organized by the Faculty of Engineering. SICET welcomes submissions from various disciplines, focusing on emerging trends in Engineering, Technology, and Applied and Natural Sciences. The conference will encompass research in theory, practical applications, and education. This event offers a unique platform for academics, student researchers, and industry practitioners to present innovative ideas and engage with professionals from diverse engineering fields

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Author: search.filters.author.Alwis, TSubject: search.filters.subject.Sales prediction

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    PublicationOpen Access
    Special Event Item Prediction System for Retails – Using Neural Network Approach.
    (SLIIT, 2022-02-11) Alwis, T; Pemarathna, P
    Selling and buying is the general process marketing field follows. Nowadays marketing field bonded with the modern technology, and it highly effected to field expandability. Marketing become fruitful when it achieves its key points which are called sales and profit. Mostly people are move to the retails because all the essentials and other things can buy from one place. There are many technological concepts involve with marketing field as an enhancement. Prediction processes, data analysis, item designing and profit calculation are some representatives for those concepts. This study is a prediction process, developed for retails using machine learning approaches. Item sales data analyzed and generated prediction results on set of items which are given maximum or expected profit margins and which items satisfy the customer most. Item suppliers are key stakeholder type a retail can have, there is a recommender system in this approach for suppliers and the recommendation is based on past sales data. There are certain types of machine learning approaches used in sales item prediction, sales item feature prediction, sales price prediction and etc. Novelty of this research is, it focused only special event items such as items in Christmas season, items specialized for Mother’s Day, Valentine Day, Sinhala, and Tamil new year and etc. This research process had completely followed the machine learning neural network concept. Recurrent Neural Network is subpart of neural networks and this research study followed up through this RNN method. Neural network had applied using a form of machine learning called deep learning. This model had worked on sequential data therefor LSTM (Long Short-Term Memory) layers were used and to avoid overfitting issue several dropout layers were used. The results prove neural network method has highest accuracy.