Inspiration
We, as a team, analyzed and interpreted the data on pesticide usage from the PSP Database and its effect on the migration patterns of the Monarch Butterflies.
What it does
Our team used a mixture of pandas, Python, CODAP, and LLMs to analyze the whole, raw data files to find the top five pesticides used in each state. After that, we created another program which found number of pesticides used each state along the migration path. After each file was analyzed for the information of the top five pesticides in the 20 states along the migration path, we uploaded this data to CODAP and built map graphics to interpret the data we found on the pesticide usage by state on the most common migration paths.
How we built it
As a team, we converted the raw data to CSV files and used a mixture of Pandas, CODAP, LLMS, and python to create a number of programs to assist our data analysis journey.
Challenges we ran into
Our main challenge was using and interpreting the Excel files as it was challenging for us as beginners. We also found that importing the data to COPAD and getting the desired results was a struggle as there were several issues we faced due to lack of previous knowledge.
Accomplishments that we're proud of
We are proud of then work we are submitting as it was a struggle for us as beginners but with hard work and determination, we succeeded in our goal.
What we learned
We learned many valuable lessons on coding and data science, mainly involving how to create code that helps do the number crunching and grunt work that would be impossible for humans.
What's next for Assessing Pesticide Risks in Monarch Migration Habitats
The next step would hopefully be to initiate a state wide investigation into pesticide usage and further research potential changes the population could make as Texas is a crucial point in the migration path. After a state wide trial, the effort would hopefully expand nation wide.
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