Developed an end-to-end customer churn prediction ML pipeline using Python, pandas, and scikit-learn. Implemented and trained a logistic regression model, then deployed it as a REST API service using ...

A burst of experimentation followed ChatGPT's release to the public in late 2022. Now many people are integrating the newest models and custom systems into what they do all day: their work. Chefs are ...

🫀 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 ...

Abstract: Optimization algorithms are essential for machine learning models to enhance prediction accuracy. There is a surge in the number of people suffering from diabetes in this present day, it is ...

Learn how to implement Logistic Regression from scratch in Python with this simple, easy-to-follow guide! Perfect for beginners, this tutorial covers every step of the process and helps you understand ...

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.

1 Department of Mobility and Infrastructure Planning and Management, College of Urban Development and Engineering, Ethiopian Civil Service University, Addis Ababa, Ethiopia 2 Faculty of Civil ...

eSpeaks’ Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of k-nearest neighbors regression to predict a single numeric value. Compared to other machine learning ...