AI-Driven Emotion Insights for Billboard Ads
Master's Dissertation
A performance-driven Loan Analytics and Reporting System using Looker Enterprise, BigQuery, SQL, and Python to monitor portfolio health, assess loan risks, and optimize decision-making. Automated data workflows reduce manual effort by 40%, ensuring real-time insights and improved efficiency.
For my capstone project in the Google Cloud Data Analytics course, I performed a detailed analysis of large-scale financial datasets and collaborated with stakeholders to pinpoint key performance metrics and loan risk trends. I evaluated market benchmarks and regulatory guidelines to ensure a thorough understanding of the financial landscape.
I designed an intuitive dashboard using Looker Enterprise that clearly visualized portfolio health and loan risk trends. The design incorporated BigQuery as the primary data repository, enabling real-time data reporting and efficient decision-making.
I transformed raw financial data into precise, actionable insights using SQL and Python in BigQuery. I also automated data workflows with Python, reducing manual intervention by 40% and enhancing the overall efficiency of financial reporting.
For my capstone project in the Google Cloud Data Analytics course, I aimed to build a Loan Analytics and Reporting System that transforms raw financial data into actionable insights. The system was designed to provide real-time portfolio health assessments and streamline financial decision-making through automation.