An R library dedicated to helping users build regression models, providing text output to ease interpretation of the model’s results, and optimizing model specifications.

Various variable selection methods are explored

This project was in collaboration with University Hospital Birmingham

Comparison of model selection methods for Boston dataset

Model Selection Using PCR, PLSR, Best subsets, Ridge Regression and Lasso Regression

D-probabilities of parametric models using nonparametric model reference

Exploring the utility of surface approximation using non-radial basis functions.

comparing many classification algorithms (Naive Bayes, Logistic Regression, Support Vector Machine) on Spiral Data with tuning SVM's parameters with mentioning Decision Trees and K-Nearest Neighbors implementation.

workspace for feature/model selection on different projects

An R package for doing repeated k-fold cross validation

Mind Foundry OPTaaS R Client

Work done for University of Pittsburgh course "Principles of Data Science" (STAT 1261) with Dr. Junshu Bao in Fall semester of 2018.

Generación de reportes para los mejores modelos Logit obtenidos con el paquete glmulti

Exercises From Book "Applied Predictive Modeling" by "Kuhn and Johnson (2013)"

StAtistical Models for the UnsupeRvised segmentAion of tIme-Series

Predict number of students on private and public school in Hawaii

Determined the best regression model which represents the data

In this project we can see in action and in detail a big part of the ML pipeline (data wrangling,model building, model evaluation) that comprises different algorithms and approaches such as Decision Trees (RPART), Linear Discriminant Analysis (LDA), Gradient Boosting Machne (GBM), Random Forest (RF) Support Vector Machine (SVM) with or without M…

Supervised learning and unsupervised in R, with a focus on regression and classification methods.