A comprehensive data analysis project exploring correlations between lifestyle factors and insurance costs for a London investment firm considering employee medical benefits
Imported essential Python libraries (pandas, numpy, matplotlib, seaborn) and loaded the anonymised insurance dataset containing demographic and lifestyle information
Conducted comprehensive exploratory data analysis including descriptive statistics, scatter plots, and histograms to understand data distribution and initial relationships
Performed rigorous correlation analysis using Pearson correlation coefficients and calculated p-values to determine statistical significance of relationships
Interpreted correlation coefficients and p-values, identifying strong, weak, and non-significant relationships between variables and insurance costs
Critically evaluated limitations of correlation analysis, potential biases, and acknowledged that correlation does not imply causation
Documented the entire analysis process, reflected on findings, and considered ethical implications for business decision-making
Strong Positive Correlation
P-value: 2.06e-153 (highly significant)
Interpretation: Higher BMI strongly correlates with increased insurance costs. This relationship is highly statistically significant.
Weak Positive Correlation
P-value: 0.012 (significant)
Interpretation: Age shows a weak but statistically significant positive correlation with insurance costs.
No Meaningful Correlation
P-value: 0.332 (not significant)
Interpretation: Number of children shows virtually no correlation with insurance costs.
BMI is the strongest predictor of insurance costs among the variables analysed, with a strong positive correlation (r = 0.709)
Both BMI and age correlations are statistically significant, while number of children shows no meaningful relationship
The data provides evidence-based justification for BMI-based insurance pricing differentials
Analysis followed systematic methodology with proper statistical testing and limitation acknowledgement
Implement BMI-based pricing tiers with transparent, evidence-based criteria. The strong correlation (r = 0.709) justifies differential pricing based on BMI ranges.
Use the statistical evidence to clearly communicate why certain employees may pay additional contributions, emphasising the data-driven approach rather than discriminatory practices.
Develop targeted wellness initiatives focusing on BMI management and healthy ageing, offering incentives for lifestyle improvements that could reduce insurance costs.
Consider additional variables (smoking status, exercise habits, medical history) to build a more comprehensive risk assessment model for future insurance offerings.
Ensure implementation adheres to equality legislation and consider offering support programmes to help employees achieve healthier BMI ranges before pricing takes effect.
Establish regular review processes to monitor the effectiveness and fairness of the insurance scheme, with annual correlation analysis to validate ongoing relationships.
Analysis completed for London Investment Firm | Data-driven insurance benefit decision making