Abstract: The conventional multiple linear regression model is limited by its inability to process high-dimensional datasets, susceptibility to multicollinearity, and challenges in modeling non-linear ...

School of Computing and Engineering, University of West, London, UK. In recent years, inflation has been a worrying factor for every country, which has become particularly high due to various ...

ABSTRACT: Accurate forecasting of the system marginal price (SMP) is crucial to improve demand-side management and optimize power generation scheduling. However, predicting the SMP is challenging due ...

In this video, we will implement Multiple Linear Regression in Python from Scratch on a Real World House Price dataset. We will not use built-in model, but we will make our own model. This can be a ...

This lesson will be more of a code-along, where you'll walk through a multiple linear regression model using both statsmodels and scikit-learn. Recall the initial regression model presented. It ...

Background: The aim of the present study was to establish a predictive model to predict the peritoneal cancer index (PCI) preoperatively in patients with pseudomyxoma peritonei (PMP). Conclusion: This ...

Abstract: This paper focuses on a multidimensional indicator prediction assessment method based on time series and multiple linear regression modelling. Through in-depth analysis of relevant data, ...