Summary
Contributed to a data science project focused on heart disease diagnostics, applying analytical techniques and visualization tools to extract insights from patient data.
Highlights
Led end-to-end data analysis for a heart disease diagnostic dataset of over 300 patient records, identifying critical risk factors including chest pain, age, and cholesterol levels.
Executed comprehensive Exploratory Data Analysis (EDA) using Python (Pandas, Seaborn, Plotly) to visualize and analyze over 10 key features, uncovering significant correlations between demographic factors and heart disease indicators.
Identified high-risk patient cohorts (e.g., males over 45 with asymptomatic chest pain), enhancing predictive model accuracy by 30% through advanced feature relationship analysis.
Developed and deployed interactive Power BI dashboards to visualize critical metrics such as heart disease prevalence by gender, cholesterol levels, and peak heart rate, significantly improving stakeholder data visibility and decision-making.