During September 2025, Axel developed a Playlist Analyzer Lab for the pcamarillor/O2025_ESI3914B repository, focusing on extracting actionable insights from user play data. He designed and implemented analytics to identify unique plays, calculate per-user unique song counts, and determine the most played song, supporting data-driven evaluation of playlist engagement. Leveraging Python programming within Jupyter Notebook, Axel utilized sets and dictionaries for efficient data manipulation and metric derivation. The lab artifact was structured for reproducibility and clarity, enabling straightforward analysis workflows. While the work addressed a single feature, it demonstrated a methodical approach to data analysis without reported bugs or issues.