One of the key method in the machine learning process is regression analysis. There are several regression models available, and hence choosing the best regression model for a specific dataset might be challenging. This paper aims to conduct an experimental study for implementing eight regression models in context of machine learning. Eight regression models with various parameter values and regression measurement matrices are covered in this work. The experiment is conducted using several benchmarking datasets in order to determine the optimal regression model that is produced. Detailed comparative analysis is presented as the results. The experiment shows that the optimal regression model result depends more on the features selection and appropriate data imputing procedures, proper data cleaning than it does on the data type. The data cleaning process, which comprised appropriate methods for data imputation, feature scaling, feature extraction, and feature selection, determines the correctness of the regression model. Even though the data cleansing is done effectively, more research on data scaling strategies is necessary.