Predictive ML shifts fulfillment from guesswork to data-driven precision, turning a major challenge into a competitive ...

In today’s fast-paced digital world, the need for skilled data scientists who can harness the power of Artificial Intelligence (AI) and Machine Learning (ML) is skyrocketing. Data Science combined ...

This project analyzes influencer marketing and promotional budgets' impact on sales using ML and statistical modeling in Python. It includes EDA, data cleaning, and simple and multiple linear ...

We developed a modified method for SV estimation that combines a validated 1-D model of the systemic circulation with machine learning. Our approach replaces the traditional optimization process ...

The goal of a machine learning ... most regression techniques are implicitly 50th percentile quantile techniques. Classical quantile regression techniques exist but usually do not work very well. They ...

This repository contains the code and resources for a machine learning project aimed at predicting car prices based on various features. The project utilizes both Linear Regression and Random Forest ...

What are the disadvantages of least-squares regression? *As some of you will have noticed, a model such as this has its limitations. For example, if a student had spent 20 hours on an essay, their ...