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🏥 Healthcare Data Analysis using Python

Objective

This project focuses on analyzing patient health data to discover key insights such as age patterns, average health metrics, and overall trends in patient statistics.
The analysis is performed using Python (Pandas, NumPy, Matplotlib, Seaborn) in a Jupyter Notebook.

Tools & Libraries

  • Python
  • Pandas
  • NumPy
  • Matplotlib
  • Seaborn
  • Jupyter Notebook

Dataset Description

The dataset (patient_data.csv) contains 1,500 records with the following columns:

Column Description
PatientID Unique ID of each patient
Age Age of the patient
Gender Male / Female
Disease Type of diagnosed disease
HospitalCharges Total hospital bill (₹)

Steps Performed

  1. Data Import & Exploration
  2. Data Cleaning
  3. Exploratory Data Analysis (EDA)
  4. Data Visualization
  5. Insights & Conclusion

Insights

  • Elderly patients tend to have higher hospital expenses.
  • Common diseases include Diabetes, Heart Disease, and Asthma.
  • Gender distribution is nearly equal.
  • Average hospital charges increase with age.

Conclusion

This analysis helps hospitals and data analysts understand cost patterns and patient demographics for better planning and resource management.

How to Run

pip install -r requirements.txt
jupyter notebook Healthcare_Data_Analysis.ipynb