Diabetes vs. Sugar Intake Analysis
A data-driven exploration of the potential correlation between sugar consumption and diabetes prevalence using public health datasets, statistical modeling, and visual analytics.
Role: Data Analyst | Focus: Data Cleaning, Visualization, and Statistical Analysis
Python
Matplotlib
Pandas
Data Visualization
Statistical Analysis
Project Highlights
- Acquired and merged multiple public health datasets for analysis.
- Cleaned and standardized data, removing incomplete or inconsistent entries.
- Plotted scatter charts comparing sugar intake and diabetes prevalence.
- Applied regression modeling to identify potential trends.
- Highlighted socioeconomic and lifestyle factors impacting results.
Why It Matters
Understanding the relationship between sugar consumption and diabetes prevalence is essential for public health policy. While correlation does not imply causation, identifying patterns can help guide educational campaigns, dietary guidelines, and preventive healthcare strategies.
Key Analytical Takeaways
- Higher average sugar consumption often coincides with higher diabetes prevalence.
- Visual analytics can simplify complex health trends for decision-makers.
- Additional factors such as income, healthcare access, and lifestyle choices are crucial for deeper insights.
Gallery
Chart