"Transforming raw data and spatial coordinates into intelligent, risk-driven decisions."

I bridge the gap between Spatial Intelligence and Data Science. My focus is on solving real-world environmental and business problems by moving beyond simple metrics to uncover deep, actionable patterns using Machine Learning, Statistical Analysis, and GIS Technologies.

Domain	Tools & Methodologies
AI & Stats	Descriptive Statistics · Hypothesis Testing (T-Test) · Regression Analysis
Languages
Data Ops
Geo-Spatial
Viz

> Machine Learning | Environmental Policy Compliance

• The Challenge: Architecting an AI system to predict vehicle CO₂ emissions for government procurement.
• The Execution: Engineered a Multivariate Linear Regression model (R² = 97.9%) leveraging One-Hot Encoding and statistical feature selection (Welch's T-test).
• The Impact: Delivered a data-driven strategy to ensure 100% compliance with strict carbon mandates.
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> Clinical Data | Inferential Statistics

• The Challenge: Moving beyond basic intuition to mathematically validate physiological markers associated with heart disease.
• The Execution: Utilized Hypothesis Testing (T-Test) on 1,025 patient records to analyze max heart rates and fasting blood sugar levels.
• The Impact: Proved statistical significance (P-value < 0.001) and identified a critical "Sugar Paradox" indicating medication interference.
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> Exploratory Data Analysis | Python

• The Challenge: Identifying operational bottlenecks and seasonal disruption patterns across Australia's top 5 domestic flight routes.
• The Execution: Developed an end-to-end Python pipeline using Pandas and Seaborn to analyze the "Winter Crunch" and benchmark airline stability.
• The Impact: Provided actionable operational buffers for high-risk routes during Q2/Q3.
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