Estimation and inference from generalized linear models using explicit and implicit methods for bias reduction

R package for automatic hyper parameter tuning and ensembles with deep learning, gradient boosting machines, and random forests. Powered by h2o.

This time I am doing it using R language. let's see the results. The solutions includes eda(exploratory data analysis), data visualizations, modelling with Machine learning Models such as XgBoost and AdaBooost etc and check the performance using rmse metrics etc to compare the results.

As part of this project, I have developed algorithms from scratch using Gradient Descent method. The first algorithm developed will be used to predict the average GPU Run Time and the second algorithm will be used to classify a GPU run process as high or low time consuming process.

Primeiros passos com R

Implementing the algorithms of Kim et al. 2014 for regressing multiple symmetric positive definite matrices against real valued covariates.

Binary Logistic Regression Analysis using the Broyden-Fletcher-Goldfarb-Shanno Algorithm on the Quasi-Newton Method

Comparing the different types of Regression

My MovieLens Project

Time Series Forecasting with ARIMA GARCH

Implementing Multi-Linear Regression using R.

Building a Logistic Regression model using R

Data Analytics: Predicting Customer Preferences 2020

Practicing R in different Analytic activities.

Putting 'R' into Autos-R-Us - an analysis of automobile manufacturing.

This repository has scripts that are part of the programming assignments of the course Linear and Generalized Linear Models taught at FME, UPC Barcelonatech.