Vaibhavi Amarnath worked on foundational data exploration for the CreditPathAI repository, developing a Jupyter Notebook in Python to load and analyze the Loan_default.csv dataset. Using Pandas, Vaibhavi implemented features to display dataset head and tail, summarize data types, generate descriptive statistics, and identify missing values, establishing a clear baseline for model readiness. To improve repository maintainability, Vaibhavi also removed an obsolete exploratory notebook, ensuring a cleaner project structure and clearer commit history. The work accelerated data understanding and reduced time-to-model readiness, demonstrating a methodical approach to data analysis and exploration while enhancing data governance for future contributors.