Feature Crawler used for a Fraud Prevention competition
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Updated
Aug 2, 2018 - Python
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
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
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