Joseph Noble

· Built a machine learning model in Python to classify and analyze data, utilizing supervised learning techniques. Focused on feature selection, model training, and evaluation by splitting data into training and test sets, with efforts to optimize accuracy and prevent overfitting.

Created a Python script to dynamically generate math quizzes, leveraging randomization and custom difficulty settings to provide a variety of problem types. Focused on algorithm design for question generation and ensured code reusability and scalability.

• Installed artificial nesting cavities for the endangered Red-cockaded Woodpecker, utilizing sectional ladders to safely work at heights of 40 feet in pine forest habitats.
• Conducted detailed inspections of woodpecker cavities with specialized camera equipment to monitor species occupancy and detect invasive competitors like flying squirrels.
• Deployed and calibrated advanced acoustic monitoring devices ("bat boxes") to capture and analyze echolocation data from local bat populations.
• Gathered, documented, and analyzed ecological data on the Red-cockaded Woodpecker, contributing to conservation research and habitat restoration strategies.