This project analyzes a synthetic healthcare dataset to explore patient demographics, medical conditions, hospital admissions, insurance coverage, billing patterns, and healthcare utilization trends.
The goal is to demonstrate an end-to-end data analytics workflow using SQL and Power BI, including data cleaning, exploratory analysis, and dashboard development.
⚠️ Note: The dataset is synthetic and does not represent real patient data.
- Cleaned and analyzed 55,500+ healthcare records
- Built a structured SQL-based data pipeline in Google BigQuery
- Performed data quality checks, cleaning, and transformation
- Developed insights across:
- Patient demographics
- Medical conditions
- Hospital activity
- Billing patterns
- Insurance distribution
- Admission trends
- Length of stay
- Built an interactive Power BI dashboard
The dataset is a publicly available synthetic healthcare dataset from Kaggle.
- ~55,500 records
- Patient demographics, admissions, billing, and treatment data
🔗 Dataset Link:
Healthcare Dataset
The analysis was performed in Google BigQuery and includes:
- Data validation (NULLs, duplicates, anomalies)
- Data cleaning (removed duplicates + invalid billing records)
- Exploratory analysis across key healthcare dimensions
- Time-based trend analysis (monthly & yearly)
- KPI calculations for business insights
- Gender distribution is nearly equal (~50/50)
- Medical conditions are evenly distributed across categories
- Average billing is ~$25.5K per record
- Length of stay averages ~15.5 days
- Insurance providers are evenly distributed (~20% each)
- Admission types are balanced (Emergency, Urgent, Elective)
- Hospital activity is highly fragmented across many facilities
- Billing trends remain stable over time
An interactive Power BI dashboard was built to visualize:
- Total & average billing
- Admissions by type
- Medical condition distribution
- Insurance coverage breakdown
- Hospital activity
- Test result distribution
- Admission & billing trends
A complete step-by-step SQL analysis is available on Kaggle:
🔗 Kaggle Notebook:
Healthcare Data Analysis Using SQL
This notebook includes:
- Data validation
- Cleaning process
- SQL queries
- Business insights
- Final conclusions
- SQL (BigQuery) – Data analysis & transformation
- Power BI – Dashboard & visualization
- Kaggle – Dataset & notebook publishing
- Git/GitHub – Version control & documentation
Healthcare Analysis/
│
├── data/
│ ├── raw/
│ │ └── healthcare_dataset.csv
│ │
│ └── clean/
│ └── healthcare_cleaned.csv
│
├── docs/
│ ├── findings.md
│ └── dashboard_preview.png
|
├── kaggle/
│ ├── Healthcare Data Analysis Using SQL.md
│ └── healthcare_data_analysis_using_sql.ipynb
│
├── powerbi/
│ └── Healthcare_Analysis.pbix
│
├── sql/
│ ├── 01_data_quality/
│ │ ├── 01_row_count.sql
│ │ ├── 02_date_range.sql
│ │ ├── 03_numeric_validation.sql
│ │ ├── 04_categorical_validation.sql
│ │ ├── 05_null_check.sql
│ │ ├── 06_negative_billing.sql
│ │ ├── 07_duplicate_check.sql
│ │ └── 08_length_of_stay_validation.sql
│ │
│ ├── 02_cleaning/
│ │ └── create_healthcare_cleaned.sql
│ │
│ ├── 03_analysis/
│ │ ├── 01_patient_demographics.sql
│ │ ├── 02_medical_conditions.sql
│ │ ├── 03_hospital_activity.sql
│ │ ├── 04_billing_analysis.sql
│ │ ├── 05_insurance_analysis.sql
│ │ ├── 06_admission_analysis.sql
│ │ ├── 07_length_of_stay.sql
│ │ ├── 08_test_results.sql
│ │ ├── 09_medication_analysis.sql
│ │ └── 10_admission_trends.sql
│ │
│ └── 04_dashboard/
│ ├── kpi_metrics.sql
│ ├── admissions_by_type.sql
│ ├── patients_by_condition.sql
│ ├── billing_by_condition.sql
│ ├── hospital_activity.sql
│ ├── insurance_distribution.sql
│ └── test_result_distribution.sql
│
└── README.md
This project demonstrates a complete SQL-based healthcare analytics workflow, from raw data to actionable insights and dashboard visualization.
It highlights strong skills in:
- SQL analysis
- Data cleaning & validation
- Business intelligence thinking
- Dashboard development
- Analytical storytelling