Design engineers working in construction machinery industry face a lot of complexities and uncertainties while taking important decisions during the design of construction equipment. These complexities can be reduced by the implementation of a data-driven decision support tool, which can predict the behaviour of the machine in operational complexity and give some valuable insights to the design engineer. This data-driven decision support tool must be supported by a suitable machine algorithm. The focus of this thesis is to find a suitable machine algorithm, which can predict the behaviour of a machine and can later be involved in the development of such data-driven decision-support tools. In finding such a solution, evaluation of the regression performance of four supervised machine learning regression algorithms, namely SupportVector Machine Regression, Bayesian Ridge Regression, DecisionTree Regression and Random Forest Regression, is done. The evaluation was done on the data-sets personally observed/collected at the site which was extracted from the autonomous construction machine byProduct Development Research Lab (P.D.R.L). An experiment is chosen as a research methodology based on the quantitative format of the data set. The sensor data extracted from the autonomous machine in time series format, which in turn is converted to supervised data with the help of the sliding window method. The four chosen algorithms are then trained on the mentioned data-sets and are evaluated with certain performance metrics (MSE, RMSE, MAE, Training Time). Based on the rigorous data collection, experimentation and analysis, Bayesian Ridge Regressor is found to be the best compared with other algorithms in terms of all performance metrics and is chosen as the optimal algorithm to be used in the development of data-driven decision support tool meant for design engineers working in the construction industry.