It has now been a few months since my capstone team wrapped up work on our forest fire management machine learning project. It was a great experience that ultimately resulted in us winning the 2020 Ontario-Wide Software Engineering Capstone Competition! As school gets underway and students begin new projects, I thought it’d be useful to share some tips that helped me while working on this project.

Overall, my big takeaway from working on a capstone involving machine learning was that 90% of the work is whipping the data into submission (or becoming friends with the data, depending on your perspective).

Understanding the importance of clean data, exploratory data analysis, data wrangling was absolutely vital to the success of our project. I think this point is often underemphasized in course work and can lead people to have a skewed perception of the work involved in such a project. So here are a few tools and techniques (with examples) that helped my team keep a healthy relationship with the data and made the project much more enjoyable.

#data-science #machine learning

3 Tips for First-Time Machine Learning Projects
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