Predict and prevent customer churn in the telecom industry with this project. Leverage advanced analytics and ML on a diverse dataset to build a robust classification model. Gain a deep understanding of customer behavior and identify key factors influencing churn. Clone the repository, explore insights, and enhance customer retention startegies.

Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models

Variable Selection Network with PyTorch

Estimating Copula Entropy (Mutual Information), Transfer Entropy (Conditional Mutual Information), and the statistics for multivariate normality test and two-sample test, and change point detection in Python

The goal of the project is to predict Life Expectancy using various factors and to determine the relationship that exists between them.

The aim of this project is to develop a machine learning model to predict the levels of CO in the air using historical datasets containing atmospheric variables. The project makes use of variables selection, decision trees, and cross-validation techniques to ensure robustness and model accuracy.

🧲 Multi-step adaptive estimation for reducing false positive selection in sparse regressions

R package for fitting semiparametric accelerated failure time models in high dimensions

Package for analyzing GWAS summary statistics data

Boosting Functional Regression Models. The current release version can be found on CRAN (http://cran.r-project.org/package=FDboost).

Projection predictive variable selection

📈 Ordered Homogeneity Pursuit Lasso for Group Variable Selection

Variable Selection with Knockoffs

A novel meta-analysis framework of the R-squared (R2)-based mediation effect estimation for high-dimensional omics mediators

Methods for selecting diverse (molecular) database.

BAS R package https://merliseclyde.github.io/BAS/

BranchGLM

OmicSelector - Environment, docker-based application and R package for biomarker signiture selection (feature selection) & deep learning diagnostic tool development from high-throughput high-throughput omics experiments and other multidimensional datasets. Initially developed for miRNA-seq, RNA-seq and qPCR.

Linear Model Interaction Terms Optimizer

Performs Variables selection and model tuning for Species Distribution Models (SDMs). It provides also several utilities to display results.