🏥 Healthcare Data Analysis Dashboard

📊 An interactive data analysis project built with Python (NumPy, Pandas, Matplotlib, Seaborn) to explore healthcare costs, patient demographics, disease burden, hospital efficiency, and insurance impact.

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📂 Project Files

• Healthcare Data Analysis Dashboard.ipynb
• Healthcare Data Analysis.ipynb → Jupyter Notebook with complete analysis & visualizations
• Health_Care.csv → Dataset used for analysis

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🔍 Key Objectives

• Analyze hospital costs and efficiency.
• Identify disease burden and treatment patterns.
• Study patient demographics (age, stay duration, doctor visits).
• Evaluate impact of insurance coverage.
• Explore outcomes (recovery, readmission, death) on costs.

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📊 Insights & Results

• Highest Avg Cost Hospital → Specialty Center (~12,106)
• Costliest Age Group → 51 years (~13,498)
• Longest Avg Hospital Stay → Diabetes patients (~20.4 days)
• Most Expensive Outcome → Dead (~12,161)
• Doctor Visits Correlation → No strong correlation with cost
• Insurance Impact → Insurance covers majority of costs (~12,002)

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✅ Business Recommendations

1. Focus on preventive care for chronic diseases like Diabetes, Heart Disease, and Cancer.
2. Share best practices from hospitals with lower average treatment costs.
3. Improve elderly patient management to reduce length of stay.
4. Collaborate with insurance providers to optimize healthcare policies.
5. Reduce readmissions with strong follow-up programs.

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⚙️ Tech Stack

• Python Libraries → NumPy, Pandas, Matplotlib, Seaborn
• Jupyter Notebook for interactive analysis
• CSV Dataset for healthcare records

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🚀 How to Run

1. Clone this repository:

   git clone https://github.com/YOUR-USERNAME/Healthcare-Data-Analysis-Dashboard.git

2. Open Jupyter Notebook:

   jupyter notebook

3. Run Healthcare Data Analysis Dashboard.ipynb Healthcare Data Analysis.ipynb.

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