Selecting the appropriate model is potentially the most important portion of the machine learning engineering process. The appropriate model will allow you to extract additional information based on significant features and other metrics of success that you may be able to draw conclusions on. There are a number of models that you can test, we will go into more detail on these later.
Working through the curriculum at Flatiron School I came to the final capstone project, deciding what topic to complete my project was difficult overall. However, I decided to push for a project that I not only found interesting, but had previous knowledge on the data sets, which made it fairly natural to work on. The idea of the project was to grab data from the Riot API and utilize machine learning tools in order to create a production models that would provide recommendations on how you can win more games.
Originally my choice to join a coding bootcamp was a tough one, I could not decide where I wanted my career to go. With my background in computer science and mathematics I figured Data Science would be a good way to go, my initial understanding of Data Science was next to nothing. I knew it had some math sprinkled with coding, but when I heard Data Science I always thought Big Data, and the top tech companies such as Amazon, Apple, Microsoft, etc. This understanding was quickly altered when I began the Flatiron program, I learned that there is almost a harmonic relationship between the mathematics behind these machine learning algorithms and aspects of software development.