Computational Complexity and Analysis of Supervised Machine Learning Algorithms

Data is generated at a much faster pace, and it is increasing exponentially day by day. Machine learning methods are being used to extract patterns and trends from data to streamline different business activities for more profit with fewer resources. Machine learning models need to be trained with lots of data before being deployed for predictive analysis (Lecture notes in Computer Science, 2012 [1]). Training time depends upon the complexity of an algorithm. We are analyzing the space and time complexity of various machine learning algorithms so that it becomes easier to select and deploy the most efficient and appropriate model for a particular dataset. This research work primarily focuses on data analytics for supervised machine learning algorithms in industrial research domains.