My Master's thesis on Bayesian Classification with Regularized Gaussian Models

Machine Learning exercises for my subject of Machine Learning at University of Granada (UGR).

This repository contains various assignments that I have done as a part of the Machine Learning course.

Binary classification to predict donor vs. non-donor & regression to prediction donation amount. Logistic regression, QDA, LDA, Random Forests, SVMs, Bagging, Boosting, Ridge Regression

Boosted regression trees for multivariate, longitudinal, and hierarchically clustered data.

sciblox - Easier Data Science and Machine Learning

An R package that makes xgboost models fully interpretable

notes on things I learnt about

This repository not only contains experience about parameter finetune, but also other in-practice experience such as model ensemble (boosting, bagging and stacking) in Kaggle or other competitions.

CS:GO Bash Scripts for Linux

The objective of the project is to demonstrate a model which can identify risky bank loans i.e., the one's likely to default. In doing so, I have implemented decision tree algorithm and applied boosting to further make the code more accurate.

Quick visual example of the boosting technique for fitting arbitrary functions

Boosting base learner simulation in xgboost

Supervised learning based on census data to predict income to identify potential donors

A compilation of different models that predict a home's value (in Melbourne, Australia) and determine which model performs better and why.

Implementation of Modified Online Adaboost-ing algorithm

Performance analysis of Decisions Trees, Boosting & Bagging, KNN, Neural Network and Linear Regression algorithms. Over two Data Sets (meant-to-be) very different in nature and volume.