Implementing Multi-Linear Regression using R.

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

My MovieLens Project

Time Series Forecasting with ARIMA GARCH

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

Practicing R in different Analytic activities.

Building a Logistic Regression model using R

Data Analytics: Predicting Customer Preferences 2020

🌍 Implement climate prediction models to transform global data into localized forecasts for effective water resource management and impact assessment.

PROTEUS -- SOLMA -- ETI

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

Primeiros passos com R

Numerical Computational Research (2009–2014): Developed and validated a comprehensive suite of prediction and statistical downscaling methods for short- and long-term projections of climate, water resources, and their associated impacts.

Comparing the different types of Regression

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.

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

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.

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