Scraping for Data – Week #6

This week the team made significant progress with our data scraper and model optimization, and learned about diversity, equity, and inclusion (DE&I) during the class presentation.

In terms of the project, the team reassessed the accuracy of our drought and fire models and worked to increase the accuracy to the level of the Technical Performance Measure that was set last semester. To increase accuracy, the CS team added new data sources, changed model parameters, and worked on developing new types of models to see which gave the best result when compared to test data. Additionally, the CS and BE team that is working on the API scraper continued development to find new fire data sources for the accuracy improvements.

During class, the team had the pleasure of listening to a DE&I presentation from Yvette Carter. During this presentation, the team was able to learn about our own implicit biases and how these can affect our performance and interactions on teams and in the workplace, as well as in daily life. Ms. Carter was able to teach us how to recognize and work to overcome these biases, a difficult undertaking that we will have to work for throughout our lives. It was extremely helpful to have these tools presented to us, and working towards diverse and inclusive workplaces is something we are all passionate about.

Day 12: Implicit Bias – YWCA Spokane
A graphic demonstrating the concept of implicit bias. (credit: https://ywcaspokane.org/2021day12/)

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