The machine learning roadmap is filled with trial and error. Engineers and scientists, who are novices at the concept, will constantly tweak and alter their algorithms and models. During this process, challenges will arise, especially with handling data and determining the right model. When building a machine learning model, it's important to know that real-world data is imperfect, different types of data require different approaches and tools, and there will always be tradeoffs when determining the right model. The following systematic workflow walk through describes how to develop a trained model for a cell phone health monitoring app that tracks user activity throughout the day.