Abstract: Lack of planning is one cause of small business failure according to Business know-how. When releasing a new product, a company is particularly vulnerable to failure if it doesn’t follow a well-designed and thought-out plan. Before a product is manufactured, it is important to conduct due diligence to investigate whether this is a smart business decision. Demand prediction is an iterative process and is a critical part of the supply chain that links supply to demand so that consumers and service providers have products available when and where they need them.
A web enabled generic application is developed which help the business to predict the demand of new products prior to their manufacturing. This prediction is based on a data mining technique called as classification rules. The input data for a classification task is a collection of company’s previous similar products sales data on which naïve bayesian classifier algorithm is applied.
Keywords: Demand forecasting, Data mining, Naïve bayesian classifier.
Title: Naïve Bayesian Classifier for Product Demand Forecasting
Author: Latanjali Shinde, Nagarathna
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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