Uber Trip Data Analysis
Python Pandas Seaborn
Project Overview
Performed EDA on large-scale ride-hailing data to identify peak demand hours and usage patterns for operational planning.
The Challenge
Identifying inefficiencies in existing workflows and handling large-scale datasets was the primary hurdle. The data was unstructured, requiring significant cleaning and normalization before analysis could begin.
The Solution
Leveraging Python for data processing and SQL for querying, we built a robust pipeline. Power BI was utilized to visualize the results, providing stakeholders with real-time insights.