Advanced Regression Techniques to predict housing prices.
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Updated
Mar 28, 2017 - R
Advanced Regression Techniques to predict housing prices.
Comparison between the implementations of the Lasso algorithm between the Spark MLib library and the R glmnet package.
A multi-response Gaussian model capable of accurately estimating the composition of blood samples from their gene expression profiles. Fit on Affymetrix Gene ST gene expression profiles using the glmnet R package.
This project was in collaboration with University Hospital Birmingham
Understand what influences bike rental usage. 🚲
Elastic Net, Lasso and Ridge models can be analyzed by the formula format.
Trabajo Especialización 2018 (ALR)
We use machine learning techniques for identification of the best cognitive markers for cocaine dependence.
Feature Selection using Elastic net function in the glmnet R package
stress detection in social networks
Multivariate linear regression, CART and Random Forest dataset analysis
LASSO, elastic net, Adaptive LASSO, SCAD methods for determining top predictors for each method
The MCB for variable selection identifies two nested models (upper and lower confidence bound models) containing the true model at a given confidence level.
A web application implementing models to predict ICU admission for COVID-19 patients based on clinical, laboratory and imaging parameters
Research project to measure the firm Expected Investment Growth (EIG) based on a combination of machine learning tools and text regression.
Fake News analysis and prediction in R Script. Naive Bayes, Random Forest, SVM, NNET, ROC, Confusion Matrix, Accuracy, F1 score.
Code of the least angle regression solution path by hand for an example( p=5). Then we compute the solution path for a dataset and compare it with the LASSO path.
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