Comprehensive Healthcare Analytics Project

This project demonstrates an end-to-end data analysis and visualization workflow for healthcare data using SQL, Python, Power BI, and Pandas. The analysis focuses on understanding patient trends, billing insights, and doctor performance while showcasing advanced data cleaning, transformation, and visualization techniques.

Objective

The goal of this project is to analyze healthcare data to provide actionable insights for stakeholders, including billing trends, doctor performance, and patient demographics, using:

• SQL for data cleaning and transformation.
• Python for advanced data manipulation.
• Power BI for interactive visualizations.

Tech Stack

• SQL Server Management Studio (SSMS): Data cleaning and transformation.
• Python (Pandas, Matplotlib, Seaborn): Data analysis and preparation.
• Power BI: Data visualization and dashboard creation.
• GitHub: Version control and project repository.

Dataset Description

The project uses healthcare data consisting of multiple CSV files:

1. Patient.csv

   • PatientID, FirstName, LastName, Email

2. Doctor.csv

   • DoctorID, DoctorName, Specialization, DoctorContact

3. Billing.csv

   • InvoiceID, PatientID, Items, Amount

4. Appointment.csv

   • AppointmentID, Date, Time, PatientID, DoctorID

Data Preparation

SQL Data Cleaning

• Removed duplicates from the Billing and Appointment tables.
• Ensured PatientID and DoctorID integrity across tables.
• Standardized missing values using NULL or defaults.

Python Data Analysis

• Loaded cleaned data using Pandas.
• Performed exploratory data analysis (EDA) on billing and appointments.
• Generated Python visualizations for preliminary insights.

Power BI Dataset

• Imported the cleaned datasets into Power BI.
• Established relationships between tables using primary and foreign keys.

How to Run the Project

1. SQL Server:

   • Import all datasets into SQL Server.
   • Run the provided SQL scripts for data cleaning.

2. Python Scripts:

   • Use dataanalysis.py to perform additional data preprocessing.
   • Ensure all dependencies are installed (pandas, matplotlib, seaborn).

3. Power BI:

   • Load the cleaned datasets.
   • Recreate the relationships and visualizations as described.

4. GitHub:

   • Clone the repository and access all necessary files here.

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Key Insights

• Revenue Trends: Consistent growth in monthly billing revenue.
• Doctor Analysis: Specializations like Cardiology generated higher revenue.
• Patient Behavior: Certain patients showed repeat visits, driving revenue.

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Future Scope

• Integrate predictive analytics to forecast appointment trends.
• Use advanced Power BI features like drill-through and what-if analysis.
• Expand datasets to include more demographic details for patients.

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Repository

Find the complete project on GitHub: Healthcare Analytics Project