heart disease analysis

Heart Disease Data Analysis Project

Analyzed medical data to identify factors contributing to heart disease and predict patient risk. Key tasks included:

Cleaning and preprocessing data (handling missing values, encoding categorical variables, standardizing features).

Conducting exploratory data analysis to uncover patterns in patient demographics, lifestyle, and medical measurements.

Creating visualizations like bar charts, histograms, and correlation heatmaps to highlight risk factors.

Building predictive models (logistic regression, decision trees, or random forest) to estimate heart disease risk.

Evaluating model performance with accuracy, precision, recall, and ROC-AUC.

Tools: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn