3.Import the Logistic Regression model from sklearn. 4.Train the model using the training dataset. 5.Use the trained model to predict placement for new student data ...

Department of Mathematics, Statistics and Actuarial Science, Faculty of Health, Natural Resources and Applied Sciences, Namibia University of Science and Technology, Windhoek, Namibia. Food insecurity ...

In Table 3, the VIF values for each variable are < 5, which has been reduced as multicollinearity between variables. 3.3. Use the Entropy Weight Method to Weight the Data When exploring the factors ...

Abstract: The purpose of this work is to investigate the effectiveness of DensNet and Logistic Regression in terms of accurately predicting the classification of footwear trends.In this study, there ...

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.

This project aims to classify human actions based on daily activities using a custom Logistic Regression model. The model is implemented from scratch, providing a clear understanding of the underlying ...

Large language model AIs might seem smart on a surface level but they struggle to actually understand the real world and model it accurately, a new study finds. When you purchase through links on our ...

Abstract: This study tries to detect fake job advertisements online using Novel Logistic Regression and compares its accuracy with linear regression. Data collection and model training are essential ...