Williams, A. and Louis, L. (2026) Cumulative Link Modeling of Ordinal Outcomes in the National Health Interview Survey Data: Application to Depressive Symptom Severity. Journal of Data Analysis and ...

Discover the power of predictive modeling to forecast future outcomes using regression, neural networks, and more for improved business strategies and risk management.

Abstract: Outsourcing logistic regression classification services to the cloud is highly beneficial for streaming data. However, it raises critical privacy concerns for the input data and the training ...

Model making is a great hobby, but knowing how and where to start is another story. It can be overwhelming for novices: Injection-molded plastic models have been on the market for close to a century, ...

Abstract: Software Defect Prediction improves the software's stability and ensures the testing process is streamlined by pointing out issues in code. Software developers can manage and use their ...

🫀 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 ...

Many businesses are just beginning to grapple with the impact of artificial intelligence, but some have been using machine learning (ML) and other emerging technologies for over a decade. Also: Most ...

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 ...

This repository contains an analysis of the Kaggle Machine Learning & Data Science Survey dataset, focusing on salary prediction using ordinal logistic regression and other classification models.

There are numerous ways to run large language models such as DeepSeek, Claude or Meta's Llama locally on your laptop, including Ollama and Modular's Max platform. But if you want to fully control the ...