🫀 A machine learning project using logistic regression to predict heart disease risk from clinical data. Built with Python, scikit-learn, and Jupyter notebooks. Achieves 85%+ accuracy on 303-patient ...

Objective: There is limited study on predictive models for live births in patients with polycystic ovarian syndrome (PCOS). The study aimed to develop and validate a nomogram for predicting live ...

Understanding the derivative of the cost function is key to mastering logistic regression. Learn how gradient descent updates weights efficiently in machine learning. #MachineLearning ...

Have you ever felt limited by Power BI’s default visuals, wishing for something more dynamic, interactive, or tailored to your unique needs? While Power BI excels at transforming raw data into ...

A dataset called titanic.csv is used. The task is to build a logistic regression model that will predict how many of the passengers on board survived and how many died ...

Have you ever felt the frustration of being locked out of a tool you were excited to explore, simply because you didn’t have the “right” kind of email address? If you’ve tried signing up for Power BI, ...

1 Department of Business Information System, Central Michigan University, Mount Pleasant, MI, USA. 2 Department of MPH, Central Michigan University, Mount Pleasant, MI, USA. 3 Department of ...

Background: Sepsis is a life-threatening disease associated with a high mortality rate, emphasizing the need for the exploration of novel models to predict the prognosis of this patient population.