Machine Learning in R

Linear Regression, Classification, Naive Bayes Spam Classification

Linear-Regression-using-Gradient-Descent

A logistic regression model to classify the number of comments that a blog receives based on 278 features. Data-set from UCI Irvine

CAP5615 Intro to Neural Networks class at FAU, Summer 2018

This project goes over data science theories and data preprocessing, leveraging the Titanic data set provided by Kaggle. The goal is to determine which passengers will likely survive or perish the monumental tragedy. The binary classification problem is addressed using two methods, each with three machine learning algorithms. The first approach …

Reports of the assignments: Decision Models a.y. 2018/2019

Flexible deep learning neural network. Implements a multi layer perceptron and autoencoders.

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.

By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.

In this example I show how Extreme-Gradient-Boosting can be used in a Survival Analysis database. At the end the importance of the variables is shown.

Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language

FAST Change Point Detection in R