This project provides an end-to-end solution for predicting credit scores and loan approval outcomes using machine learning models and a ... network architectures: A regression network for credit ...

While multiple machine learning (ML) algorithms offered similar predictive performance, the cost-effective analysis revealed ...

Budoen, A.T., Zhang, M.W. and Edwards Jr., L.Z. (2025) A Comparative Study of Ensemble Learning Techniques and Classification Models to Identify Phishing Websites. Open Access Library Journal, 12, ...

It’s called Flow, and Google is announcing it alongside its new Veo 3 video generation model, more controls for its Veo 2 model, and a new image generation model, Imagen 4. With Flow ...

Multicollinearity exists when there is a correlation between multiple independent variables in a multiple regression model. This can adversely affect the regression results. A variance inflation ...

Finally, we design a classifier, GraphSAT, which integrates Graph-SAGE and GAT to deeply learn Flow Graph features, enabling accurate encrypted traffic identification. FG-SAT exhibits both efficient ...

recurrent neural network reservoir classification model with optimal parameters that can make full use of logging sequence information. The data used in this work originate from 3 wells in the BZ gas ...

A predictive model can be developed on its own ... that enhances the consistency and precision of the algorithms used through statistical classification and regression. It also significantly reduces ...

There are a total of 4 script in this repo. Data_preprocessing deals with the preprocessing of the data, by cleaning it and splitting it into 2 parts (train and test). Neural_train is a script just to ...