Our Bayesian hierarchical model studies geocache data from Utah and California, asking whether the Terrain and Difficulty rating of a cache affects the number of favorites it receives.
Working alongside the Lord Mayor of London, City, University of London, and Imetrum, we began uncovering answers to questions about the Great Fire of London monument created by scientist Robert Hooke.
Support Vector Regression (SVR) is a powerful supervised learning technique that balances complexity and generalization by minimizing a regularized risk function. This document explores the theoretical foundations of SVR, its implementation, and its application in predictive modeling.
For those who have been with me this entire journey, I hope you enjoyed the project as much as I did! This project started with a desire to know what is going on with the weather here in the West, specifically in Provo, Utah.
John Tukey once said, “The greatest value of a picture is when it forces us to notice what we never expected to see”. There is something beautiful about taking a hodgepodge of data and transforming it into a story. What we could not see is now unveiled for us to discover and ask more questions.
North America is in a crisis. A crisis that trumps all other crises we are experiencing. The west is enduring one of the longest droughts ever recorded, which is bringing many dire consequences. While this problem looms over our heads, many are stepping up to the plate to solve this issue of water usage.
If you are wanting to know how to implement random forests in R, then you have come to the right place! Machine learning is a powerful tool that helps us accurately predict certain outcomes by creating a model with the information we give it. In other words, it ‘learns’ with what is given and predicts with data that hasn’t been seen yet.