Publication Type:

Journal Article


International Journal of Applied Engineering Research, Research India Publications, Volume 10, Number 5, p.12843-12854 (2015)



With the latest advancement in technology the number of products available to consumers has increased exponentially. There has also been a significant rise in competitiveness in every business segment. It is paramount for a product to cater to the needs of the users explicitly to remain successful in the market. Every company now has to balance product innovation and new product feasibility; else they will suffer financial loss. In this environment, it is important for the companies to have a system which can predict the success or failure of the proposed product, by identifying its similarities with those of successful products currently existing before mainstream production take place. Our project aims at predicting the profit level of a proposed product by comparing it with the sales data of existing products in the same category to gain meaningful insight to its profitability. With this project we propose to automate the profitability prediction which exists as a largely manual process today. We propose to use the data mining techniques for prediction of profit of proposed product based on the financial performance of the existing products. Specifically, we have identified classification using the naive Bayes algorithm as the most suitable for our project. Our system will not only work with any generic product, but will also deployable on the internet so as to have maximum reach. The input dataset will be the sales data of existing products in the same category of products along with the features & cost of the proposed product. The output generated will specify the likely profitability level of the proposed product. Thereby increasing both the efficiency and speed by which companies can take better product decisions. © Research India Publications


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Cite this Research Publication

M. S. Pallavi, Shanker, M. K. Shanmuga, and K. Bhat, N., “Product demand forecasting: Will this product sell”, International Journal of Applied Engineering Research, vol. 10, pp. 12843-12854, 2015.