🩺 Medical Expense Analysis

This repository contains an exploratory data analysis (EDA) of a medical insurance dataset. The goal is to investigate how various demographic and health-related factors such as age, BMI, smoking habits, gender, and diabetes status influence the insurance claim amounts.

This project includes both Python and R-based analysis, enabling cross-platform exploration and reproducibility of results using tools like Jupyter Notebooks and R Markdown.

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📂 Files Included

• medical_expenses.csv – Cleaned dataset used for analysis
• 1_data_preprocessing.ipynb – Handling missing values and outliers (Python)
• 2_eda.ipynb – Detailed visual EDA with insights (Python)
• Insurance EDA.py – Python script version of the analysis
• insurance_data.docx – Summary, recommendations, and discussion questions
• medica_expense.Rmd & medica_expense.html – R Markdown notebook and rendered HTML output for EDA using ggplot2 and dplyr

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📊 Dataset Overview

The dataset includes the following columns:

• age: Age of the policyholder
• gender: Male/Female
• bmi: Body Mass Index
• bloodpressure: Blood pressure level
• diabetic: Yes/No
• children: Number of children covered by insurance
• smoker: Smoking status
• region: Residential region
• claim: Insurance claim amount

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🔍 Analysis Highlights

• Age & Claim: Older individuals tend to have higher claims.
• BMI & Smoking: Smokers and people with higher BMI incur higher medical expenses.
• Regional Trends: Southeast region shows higher average claim amounts.
• Diabetes & Risk: Diabetic individuals have distinct trends in BMI and claim distribution.
• Outlier Treatment: Handled outliers in BMI and filled missing values in age and region.

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💡 Recommendations

• Tailored Insurance Plans: Customize offerings based on demographic segments.
• Health Incentives: Encourage wellness programs in high-risk areas.
• Risk Assessment Models: Use data insights to improve pricing and risk models.
• Policyholder Education: Help customers understand how lifestyle affects insurance cost.

See insurance_data.docx for detailed conclusions and discussion questions.

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🛠️ How to Run

1. Clone the repo

   git clone https://github.com/yourusername/medical-expense-analysis.git
   cd medical-expense-analysis

2. Install required Python libraries

   pip install pandas numpy matplotlib seaborn

3. Run the Jupyter notebooks or Python script.

4. To run the R analysis, open medica_expense.Rmd in RStudio and knit to HTML.

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📘 Author

Saurabh Singh Bhandari
Data Science Enthusiast | EDA & Automation Specialist

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📎 License

This project is for educational and portfolio purposes only. No commercial use allowed without permission.