Medical Data Visualizer

This project visualizes medical examination data with pandas, seaborn, and matplotlib. The analysis explores the relationship between cardiovascular disease, body measurements, blood markers, and lifestyle factors.

Portfolio Review

This is a strong health-data analytics project because it connects clinical risk factors with reproducible Python analysis. The work demonstrates data cleaning, BMI-based feature engineering, categorical normalization, exploratory visualization, and correlation analysis.

The most valuable portfolio angle is not just that charts were produced, but that the workflow shows how raw examination records can be transformed into interpretable evidence for cardiovascular risk review.

Methods

• Loaded medical_examination.csv with pandas.
• Created an overweight indicator from BMI.
• Normalized cholesterol and gluc so 0 consistently represents a healthier result and 1 represents elevated risk.
• Reshaped categorical health indicators into long format for grouped comparison.
• Cleaned implausible blood-pressure, height, and weight records before correlation analysis.
• Generated a masked correlation heatmap to reduce redundant visual noise.

Visualizations

Categorical Risk Comparison

This visualization compares counts for cholesterol, glucose, smoking, alcohol intake, physical activity, and overweight status across patients with and without cardiovascular disease.

Correlation Heatmap

The heatmap shows relationships among cleaned medical measurements and risk indicators. The masked upper triangle keeps the chart readable while preserving the major correlation patterns.

Skills Demonstrated

• Python data analysis
• pandas transformation and cleaning
• seaborn categorical plots
• matplotlib figure export
• health-data interpretation
• reproducible analytics workflow

Files

• medical_data_visualizer.py: analysis and plotting functions
• medical_examination.csv: source data
• examples/Figure_1.png: categorical plot reference
• examples/Figure_2.png: heatmap reference
• test_module.py: validation tests