Web app that can generate battery cycle graphs from Arbin and Neware battery software.

Alexandra Hernandez

Personal project-based [web app](https://battery-cycle-visualizer.streamlit.app/) that easily visualizes battery cycle data, using user-based parameters to visualize given cycles (single cycle, all cycles, or multiple certain). Takes in `.csv`, `.xls`, and `.xlsx` files, and allows for user to download cycle-specific data as a `.csv` file. Made with Python, Pandas, MatPlotLib, and Streamlit. This app is currently used by the Chen Battery Lab at the University of California, San Diego.

Source code can be found [here](https://github.com/alexavndra/battery-cycle-data-viz).


Group final project for COGS 118B. Examines the relationship of location prediction from accent data labeling.

Examining differences/similarities between Accents from AccentDB database by using unsupervised machine learning algorithms via clustering techniques. Done all in Python and utilized its respective packages, and found that most ML algorithms had overlapping clustering, suggesting that overall accents and their similarities to one another are categorized similarly, yet could be differentiated into different (overlapping) clusters.

Source code can be found [here](https://github.com/alexavndra/cogs_118b_project/tree/main).


Accent Atlas

Data science group research project that analyzes the relationship between caption keywords and DeviantArt content using Python.

Data science project that analyzes the relationship between DeviantArt content popularity and the keywords contained in their captions with Python, primarily in DeviantArt API, NumPy, Seaborn, MatPlotLib, and SciPy. Completed as part of COGS 108: Data Science in Practice class at the University of California, San Diego. When analyzing the relationship between caption keywords and popularity, we found to be a weak *positive* correlation between the two, suggesting that other factors such as follower count and overall visibility on the platform play a (possible) bigger role than captions themselves.

Source code can be found [here](https://github.com/alexavndra/digital-art-keywords/tree/main).
