Worldwide, machine learning is increasingly used to achieve optimal analytics and outcomes in business and governmental organisations. However, it has become evident that structural guidance for creating machine learning projects is needed. Machine learning projects are dependent on multiple factors. This thesis focuses on three factors influencing machine learning projects: the technical aspects of machine learning, the organisational aspects of successful implementation of machine learning projects and last the ethical aspects, since difficulties coming with meeting ethical standards contribute to the suboptimal use of machine learning projects. The research was done in accordance with Design Science Research Methodology (DSRM). Ten steps to setup machine learning projects were identified and a method has been designed, demonstrated and evaluated. The research included an extensive literature search which provided the main contributing theories of Knowledge Discovery in Databases and Ethical Impact Assessment. Furthermore, multiple experiments in Standard Business Reporting (SBR) context were done. Expert interviews provided input for optimisation and evaluation. In this thesis a method for setting up creating machine learning projects, including ten steps with accompanying sub steps, was created. A single method combining and integrating technical, ethical and organisational aspects was not yet available. At this moment, it is unique in comparison to other methods, as it combines interdisciplinary aspects into one method and provides guidance for management and engineers. Apart from the developed method, this thesis also provides structured insights in the use of machine learning in SBR context.