An analysis of leading factors influencing health insurance premiums using Tableau.
This project involved the development of a comprehensive business intelligence dashboard to visualize the key drivers of medical insurance charges. By analyzing a dataset of 1,338 records, I identified how lifestyle factors (smoking), demographics (region), and physical metrics (BMI) correlate with healthcare costs.
Utilizing a multi-layered bar chart, I compared regional average charges against smoker status.
- Primary Finding: Lifestyle choice (smoking) is a significantly higher cost-driver than geography.
- Regional Variance: The Southeast region was identified as having the highest average costs for smokers ($33,949), while the Southwest had the lowest average for non-smokers ($7,213).
A dual-axis line chart was implemented to track how the number of children affects total charges, segmented by smoker status.
- Trend Analysis: For smokers, costs peaked at 2 children ($33,783) before showing a significant reduction as the number of children increased to 5.
- Comparative Baseline: Non-smokers maintained a relatively stable cost profile regardless of the number of dependents.
The final dashboard segment utilizes an area chart and scatter plot to visualize the complex relationship between Age, BMI, and Charges.
- Significant Trend: A scatter plot with an integrated trend line confirms that as age increases, charges rise significantly (P-value < 0.05).
- Risk Clusters: The analysis identifies a "High Risk" group: smokers with excessive BMI, who pay an average of $12,000 more than lower-BMI smoking counterparts.
- Dynamic Filtering: Users can filter by smoker status and number of children to see real-time charge updates.
- Statistical Validation: Integrated trend lines and P-value analysis to ensure visual observations are statistically significant.
- Heatmapping: Use of color gradients to quickly identify high-cost geographic regions and BMI categories.
The interactive analysis successfully identified lifestyle and demographic variables that serve as primary escalators for insurance premiums.
- Smoking as a Primary Cost Driver: Across all regions, smoker status remained the most significant predictor of high charges, with average costs exceeding $30k in the Southeast.
- BMI & Age Correlation: The scatter plot confirms a statistically significant trend (P < 0.05) where increasing age and high BMI create a compounding effect on healthcare spend.
- Strategic Recommendations: Insurance providers should prioritize wellness and smoking-cessation programs in the Southeast and Northwest regions to mitigate high-cost regional claims.