This thesis examines whether explainable machine learning can support bid/no-bid decision-making for large Dutch construction projects. The study is conducted as a case study at Count & Cooper, a Dutch project-management and tender advisory consultancy active in complex construction tenders. The bid/no-bid decision is a critical early decision point, because contractors must decide whether to invest substantial time and resources in preparing a tender while important project information is still uncertain. To address this problem, the thesis first analyses the Dutch tendering context and identifies decision factors from literature, desk research, and expert validation. These factors are then operationalised into a structured dataset based on 101 historical tender opportunities. Several supervised learning models are compared, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and XGBoost. Because of the limited dataset size and data-quality constraints, model performance is evaluated using repeated 5-fold cross-validation. In addition to predictive accuracy, the thesis emphasises interpretability by analysing model coefficients and SHAP-based feature importance. The results show that bid/no-bid outcomes can be predicted to a moderate extent using characteristics that are available early in the tendering process. Random Forest achieved the strongest overall predictive performance, with Logistic Regression and linear SVM performing similarly well. Across models, tender duration, contract duration, project value, project type, and contract form emerged as influential drivers. At the same time, the analysis shows that important internal decision factors, such as strategic fit, current workload, and client capability, were not consistently documented in the available historical records and therefore remain outside the current modelling scope. The thesis concludes that explainable machine learning can provide useful and transparent screening support for bid/no-bid decisions, but that the current results should be treated as a proof-of-concept rather than a deployable decision tool. The main practical recommendation is therefore to improve the structured capture of tender characteristics, internal decision context, and decision rationale, so that future models can become more robust, realistic, and decision-relevant.