Abstract: Accurate rainfall forecasts are important throughout Agriculture, Water Resources, and Disaster preparation. In this example, we use two complex devices study design, Logistic Regression and ...

Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...

Implement Logistic Regression in Python from Scratch ! In this video, we will implement Logistic Regression in Python from ...

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 ...

ABSTRACT: Road traffic accidents are one of the global safety and socioeconomic challenges. According to WHO (2024), it has caused over 1.19 million annual fatalities. It is also projected to cause ...