Database creation using SQL

End-to-End Data Pipeline & Database Project In this project, I designed and implemented a complete healthcare data pipeline starting from synthetic data generation to a fully structured and analysis-ready database. Data Generation: Generated realistic healthcare data using Synthea, and developed custom Python scripts to simulate patient vital signs, creating a rich and dynamic dataset. Exploratory Data Analysis (EDA): Performed in-depth data exploration to understand patterns, detect anomalies, and identify data quality issues before processing. Data Cleaning & Transformation: Applied data cleaning techniques using Python, including handling missing values, removing duplicates, and standardizing inconsistent records to ensure high data quality. Data Storage: Designed and built a structured SQL database to store the processed healthcare data efficiently, enabling fast querying and scalability. Key Highlights: * Built an end-to-end data pipeline from data generation to storage * Worked with healthcare data, simulating real-world scenarios * Combined data engineering with analytical thinking (EDA) * Created reusable and scalable data processing workflows Tools & Technologies: Python, SQL, Synthea, Data Cleaning, EDA Outcome: A clean, well-structured healthcare dataset stored in a database, ready for analytics, reporting, and future machine learning applications.