End-to-End Healthcare Data Pipeline using Python & SQL

End-to-End Healthcare 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.