Heart Disease Prediction with Logistic Regression

This project aims to predict the likelihood of heart disease in patients based on clinical and demographic data. Utilizing Logistic Regression, a powerful machine learning technique, I analyzed a comprehensive heart disease dataset containing key attributes such as age, gender, cholesterol levels, heart rate, chest pain type, and other relevant factors. The model helps identify patterns and risk factors, providing valuable insights for early detection and informed decision-making in healthcare.

The dataset was processed and analyzed using IBM Watson Studio, a robust cloud-based platform for data science and machine learning. I utilized SPSS Modeler to construct a visual workflow that streamlined the data preparation and model-building process. The model was trained on key features from the dataset, enabling accurate predictions of whether a patient is at risk of heart disease. This approach simplifies the predictive modeling pipeline and enhances the efficiency of identifying potential health risks.

I recently completed a heart disease prediction project using Logistic Regression on IBM Watson Studio, under the guidance of Vaishnavi Ma'am.