Sale Forecasting for Hot-Drink Productivity Using Naïve Bayesian Classification

Abstract

Classification is one of the most popular data mining tasks with a wide ranges of application and lots of algorithms have been proposed to build scalable classifiers. Several data mining techniques and classification methods have been widely applied to extract knowledge from databases. Naïve Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its competitive performance in classification is surprising, because the conditional independence assumption on which it the conditional independence assumption on which it is based, rarely true in real-world applications. This system will present sale forecasting productivity using Naïve Bayesian Classification.