Heart Disease Analysis with Python

Analyze medical data to understand health risk factors. Heart disease remains one of the leading global health concerns, making it a critical area for data-driven analysis. In this project, you’ll work with a heart disease dataset that includes patient attributes such as age, gender, cholesterol levels, blood pressure, and other clinical indicators. Using Pandas for manipulation and Seaborn for visualization, you’ll uncover patterns and explore which factors are linked to higher risks of heart disease. You’ll begin by loading the dataset and preparing it for analysis—checking for missing values, handling categorical variables, and ensuring that numerical columns are ready for statistical exploration. With Pandas, you’ll calculate descriptive statistics, group patients by health indicators, and compare averages across different subgroups. Next, you’ll use Seaborn visualizations to make relationships clear. Histograms and box plots will reveal distributions of cholesterol or blood pressure, while scatter plots and heatmaps will show correlations among variables. You’ll investigate questions like: How do age and gender influence the likelihood of heart disease? What patterns exist in cholesterol or resting blood pressure levels? Which features appear most correlated with the presence of heart disease? By the end of this project, you will be able to: Clean and prepare medical datasets with Pandas. Summarize and compare patient groups across health attributes. Create Seaborn plots to highlight distributions and correlations. Interpret results to understand key risk factors. This project demonstrates how data analysis can be applied in healthcare, turning raw patient records into actionable insights. While it does not replace medical expertise, it gives you valuable practice in analyzing sensitive, real-world data with both care and precision.