Heart diseases

Heart Disease Prediction Project

We developed a complete machine-learning pipeline to predict the likelihood of heart disease using the UCI Heart Disease dataset. The project included:

Data Preparation: Cleaning, handling missing values, feature scaling, and exploratory data analysis.

Feature Engineering: Selection of key health indicators such as age, cholesterol, blood pressure, and heart rate.

Modeling: Training and comparing several algorithms (e.g., Logistic Regression, Random Forest, and Gradient Boosting) to identify the best-performing model.

Evaluation: Achieved high accuracy and balanced precision/recall using cross-validation and ROC-AUC metrics.

Deployment: Delivered a ready-to-use model that can provide real-time predictions through a simple user interface or API.