Abstract: In order to predict the sales of their goods and services, organisations must analyse the daily sales data. By using this forecasting, manufacturers can raise product output to keep up with demand or make necessary adjustments to boost sales. With the help of the data science techniques Nave Bayesian classifier and KNN Classifier, this research introduces a fresh approach to sales forecasting. In order to demonstrate the effectiveness of the suggested mechanism, experiments are conducted utilising sales data from prior years gathered from numerous stores situated in various cities. The best algorithm will be determined by comparing the two .We used datasets from both feature phones and smart phones in the suggested method.

Keywords: Product, forecasting, demand, classifier, ML techniques

| DOI: 10.17148/IARJSET.2023.10842