The Diabetes Self-Management Data, loaded and parsed as CSV.

This dataset contains responses from participants in Austin, Texas, who completed a diabetes self-management program. It includes various health indicators and socioeconomic factors.

Tasks

1. Compare Glucose Levels Across Different Age Groups:

   • Visualize and compare the average glucose levels of individuals across different age groups to understand how glucose management varies with age.

2. Analyze Blood Pressure Trends:

   • Track changes in blood pressure levels over time or across different medical conditions to identify trends or anomalies.

3. Explore the Distribution of Diabetes Medication Use:

   • Examine how different types of diabetes medications are distributed among individuals to see which medications are most commonly used.

4. Correlate Physical Activity with Diabetes Management Metrics:

   • Investigate the relationship between physical activity levels and diabetes management metrics like HbA1c levels to determine if increased activity correlates with better diabetes control.

5. Identify Subgroups with High Risk Factors:

   • Use the dataset to identify subgroups of individuals who exhibit high-risk factors for poor diabetes management, such as high glucose levels or low physical activity.

6. Investigate the Impact of Socioeconomic Factors on PAID Scores:

   • Analyze how socioeconomic factors, such as income level or insurance type, influence the PAID scores.