🤠 📿 The Highly Adaptive Lasso

An R package for modern methods for non-probability samples

Integrating LASSO and bootstrapping algorithm to find best prognostic or predictive biomarkers

The diversity of RNA structural elements and their documented role in human diseases make RNA an attractive therapeutic target. However, progress in drug discovery and development has been hindered by challenges in the determination of high-resolution RNA structures and a limited understanding of the parameters that drive RNA recognition by smal…

Nonparametric regression and prediction using the highly adaptive lasso algorithm

Implementation of the block-descent-CoCoLasso, inspired from the article https://arxiv.org/pdf/1510.07123.pdf

Fast Sparse Linear Models for Big Data with SAGA

biostatistical workflows in R covering regression and classification models

King County House Sales

Methods for data segmentation under a sparse regression model

Credit risk analysis using the LASSO, Random Forests and the SMOTE technique for balancing

Loan Default Prediction, Individual Level Loan Data, Machine Learning, Logistic regression, Ridge, LASSO, Gradient Boosting, SVM, Random Forest

Employee Attrition Predictor in R Shiny based on the HR-employee-Attration Data in kaggle

Real-time generalizable model to predict final selling prices of houses using advanced regression techniques.

Advanced Regression Techniques to predict housing prices.

Applied Random Forest, PCA, and Lasso Regression to analyze 75 financial ratios of Dow30 stocks to predict stock prices, and achieved a 97% accuracy

A tool for visualizing the coefficients of various regression models, taking into account empirical data distributions.

I created multiple models to predict the discharge volume of a 100 year flood on rivers in NY state. The discharge of 100 year flood events is dependent upon watershed drainage area, and elevation among other variables.

workspace for feature/model selection on different projects