EDA on medical insurance data using Python and R to explore how age, BMI, smoking, and other factors influence claim amounts and health risk profiles.

This project analyzes health and lifestyle factors influencing heart attack risk using statistical methods and machine learning, with Ridge Regression identified as the best predictive model.

A Power BI dashboard for analyzing the systemic impact of smoking on patient organ health built from synthetic patient data to visualize how smoking behavior correlated with organ damage across heart, lungs, liver, and kidneys.

Executed complex SQL queries to assess patient health metrics, focusing on BMI and glucose levels. Improved data integrity and query performance through schema enhancements and indexing. Created patient views and ranked data by glucose levels for insightful analysis.