When explaining machine learning to clients unfamiliar with the concept, the scope and purpose of the project, as well as the level of detail and depth required by the clients should be considered. Generally, it is useful to cover topics such as the problem and goal, data and features, models and algorithms, results and recommendations. For instance, when discussing the problem and goal, one should explain what is being solved and what is being achieved with machine learning. Additionally, it is important to emphasize how this approach differs from other methods or solutions. Moving on to data and features, one should elaborate on what data is being used, how it is prepared and transformed for machine learning purposes, what features are being used, how they are selected and engineered for models. Moreover, it is important to ensure the quality, validity and reliability of data and features as well as address any issues or challenges that may arise. Furthermore, when discussing models and algorithms, one should explain what models are being used and how they are built and trained for machine learning. Additionally, one should explain how different models are compared and optimized for the problem at hand. Finally, when presenting results and recommendations to clients, it is useful to create a dashboard that shows how different factors affect customer buying behavior. Moreover, any limitations or assumptions associated with results and recommendations should be addressed or mitigated.