Analysis of bank rates using web data
Objective
A US-based buy-side-firm wanted to track deposit rates of various banks and harvest deposit rates of US and Australia based banks.
Challenges
- Large volumes of structured and unstructured data.
- Manual tracking of data was taxing on firm resources.
Approach
- Scraped and consolidated web data using Python.
- Performed data cleansing, pattern recognition and structuring operations to ensure data quality.
- Stored data using Google Cloud and Excel.
- Connected to Tableau for visualizations and reporting.
Impact
- Developed a dynamic visualization tool to efficiently summarize and analyze large data sets.
- Automated data processing to ensure limited manual intervention and enhance accuracy.
Questions