Feature Crawler used for a Fraud Prevention competition

In this project, we have analyzed, explored and processed the data, developed and evaluated various classification and regression models to provide strategies for high returns with low risk for investors.

This project compares multiple bagging and boosting methods for anomaly detection for the Gecco challenge.

A self-generalizing, hyperparameter-free Gradient Boosting Machine

Analysed the features for breast cancer data and predicted the diagnosis using Random Forest, Gradient Boosting Machine (GBM)

A prediction model that uses logistic regression and gradient boosting to classify population income.

[SIGE-MII-UGR-2016-17] Competición en Kaggle: Titanic

Data Cleaning and modeling Approaches

Attrition Prediction using GBM (Classification)

(National Rank: 22) Analyze This' 18, American Express Data Science Competition

An extension of Py-Boost to probabilistic modelling

Kernels for machine learning problems

Ⓜ️Machine Learning with Python

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

The objective of this project is to predict whether or not an applicant will be able to repay a loan using historical data of user requesting a loan

Run a TensorFlow Decision Forests on an Ardwino.

House Sales Prediction using GBM (Regression)

Implementation of Decision Tree and Ensemble Learning algorithms in Python with numpy

Blackbox feasibility prediction with machine learning to optimize a CMA-ES algorithm