Santhosh G

Developed an XGBoost forecasting pipeline, achieving high predictive accuracy (R²=0.9088, RMSE=604.64) by leveraging advanced statistical modeling and temporal/weather feature engineering.

Implemented rigorous time-series cross-validation, leakage checks, and hyperparameter tuning to optimize model performance and ensure generalization across diverse datasets.

Operationalized model deployment by integrating forecasts via Streamlit, enabling stakeholders with real-time access and interactive scenario simulations for informed decision-making.

Ensured model reliability and maintainability through the implementation of input validation, runbook procedures, and versioned experiment notebooks in Git, enhancing reproducibility and supporting business decisioning.

Conducted comprehensive Exploratory Data Analysis (EDA) to uncover critical data patterns and inform feature selection, improving the foundation for predictive model development.

Processed and cleaned 103,700+ Uber trip records, integrating Calendar and Location tables to create a robust data model for in-depth analysis.

Developed interactive Power BI dashboards utilizing custom DAX measures, KPI cards, slicers, and drill-throughs to visualize key metrics like $1.6M in revenue, $15 average fare, and 3-mile average trip distance/16 minutes.

Surfaced crucial demand patterns, including weekend peaks (18.7K on Saturday, 19.2K on Sunday) and lowest volume (9.3K on Friday), along with a 73%/27% day/night split, to inform scheduling and zone prioritization.

Automated data refresh processes and implemented data quality checks with documented schema and incremental refresh logic, ensuring data integrity and timeliness.

Created parameterized queries, DAX documentation, and user guides to empower self-service analytics, significantly reducing ad-hoc reporting requests and improving data accessibility.