  Machine learning algorithms have enabled computers to predict things by
learning from previous data. The data storage and processing power are
increasing rapidly, thus increasing machine learning and Artificial
intelligence applications. Much of the work is done to improve the accuracy of
the models built in the past, with little research done to determine the
computational costs of machine learning acquisitions. In this paper, I will
proceed with this later research work and will make a performance comparison of
multi-threaded machine learning clustering algorithms. I will be working on
Linear Regression, Random Forest, and K-Nearest Neighbors to determine the
performance characteristics of the algorithms as well as the computation costs
to the obtained results. I will be benchmarking system hardware performance by
running these multi-threaded algorithms to train and test the models on a
dataset to note the differences in performance matrices of the algorithms. In
the end, I will state the best performing algorithms concerning the performance
efficiency of these algorithms on my system.
