Increasing Trend in Accuracy Score for Machine Learning Algorithms

Abstract

Most business applications rely on historical data to predict their future. This process helps them gather important information about their customers and develop effective marketing strategies. Due to the nature of data, the development of various industries depends on it. For instance, education and health services need to analyze and predict the future growth of these sectors. Data mining is a type of statistical process that uses techniques to predict statistical data in various business applications. One of the most widely used techniques is the classification. The paper aims to introduce a comprehensive analysis of the literature on classification algorithms for different business applications. It shows that the most accurate algorithm is the FFBPN. The Random forest algorithm is the most accurate method to classify social networks activities. The Nave Bayes algorithm is also the most accurate method to classify agriculture datasets.