The construction sector is embossed with uncertainty, where cash flow prediction, time delays, and complex feature interaction make it hard to predict which future projects will be profitable or not. This thesis explores the prediction of project yield and project success for a company in the construction industry using supervised learning models. Leveraging historical project data, parametric traditional regression and machine learning techniques are employed to develop predictive models for project yield and project success. The models were chosen based on previously related work and consultations with employees with domain knowledge in the industry. The study aims to identify the most effective modeling approach for yield prediction and success in construction projects through comprehensive analysis and comparison. The features influencing project yield are investigated using SHAP (SHapley Additive exPla-nations) and permutation feature importance (PFI) values. These explainability techniquesprovide insights into feature importance in the models, thereby enhancing the understandingof the underlying factors driving project yield and project success. The results of this research contribute to the advancement of predictive modeling in the construction industry, offering valuable insights for project planning and decision-making. Construction companies can optimize resource allocation, mitigate risks, and improve projectoutcomes by accurately predicting project yield and success and understanding the keyfactors influencing it. The results in this thesis reveal that the machine-learning models outperform the parametric models overall. The best-performing models, based primarily on accuracy and ROI, were the random forest models with both binary and continuous outputs, leading to a suggested data-driven guideline for the company to use in their project decision-making process.