https://github.com/liuba9999/healthcare---analytics
Analysis of hospital admissions, patients demographics, and billing trends.
https://github.com/liuba9999/healthcare---analytics
healthcare jupyter-notebook matplotlib panda python visualisation
Last synced: 2 months ago
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Analysis of hospital admissions, patients demographics, and billing trends.
- Host: GitHub
- URL: https://github.com/liuba9999/healthcare---analytics
- Owner: Liuba9999
- Created: 2025-08-14T19:56:53.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-08-14T20:48:27.000Z (3 months ago)
- Last Synced: 2025-08-14T22:27:54.737Z (3 months ago)
- Topics: healthcare, jupyter-notebook, matplotlib, panda, python, visualisation
- Language: Jupyter Notebook
- Homepage:
- Size: 6.42 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Hospital-Dataset-Analysis
Analysis of hospital admissions, patient demographics, and billing trends to uncover actionable insights into healthcare utilization and costs.
# Project Overview
This project explores hospital data to identify patterns in patient admissions, age groups, conditions, and billing amounts.
# Dataset
Source: [https://www.kaggle.com/datasets/soniyabablani/healthcare-dataset]
Contents: Patient demographics, hospital admissions, diagnoses, billing amounts.
Format: CSV
# Objectives
Identify peak hospital admission periods.
Analyze patient demographics and age group utilization.
Examine billing patterns conditions.
Highlight trends to support hospital resource planning.
# Tools & Technologies
Python 3.11
Pandas, NumPy
Matplotlib
Jupyter Notebook
# Results
Adults aged 25–64 make up the majority of admissions.
Certain hospitals and conditions contribute to the highest billing amounts.
Peak months and seasonal trends identified.
Clear visualizations make interpretation and decision-making easier.
# Conclusion
This analysis provides a data-driven overview of hospital utilization, patient demographics, and cost distribution. Insights can guide administrators in improving healthcare service allocation and resource planning.
# Usage
1. Clone this repository.
2. Open the Jupyter Notebook file.
3. Run all cells to reproduce the analysis.