Insight Data Science Fellowship Project: Predicting campaign contributions using demographic data.
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Updated
Feb 14, 2017 - HTML
Insight Data Science Fellowship Project: Predicting campaign contributions using demographic data.
Predicting housing prices from categorical and numerical data with gradient boosted regression trees.
ArtBooster.jl turns images into abstract figures by predicting their features with a gradient booster in real-time.
Predictive Machine Learning Project
My contributions in Kaggle, mostly in a notebook format. Just for fun.
[SIGE-MII-UGR-2016-17] Competición en Kaggle: Titanic
An insight to analyzing Titanic survival using decision trees and ensemble methods
Exploration of ensemble methods on a imbalanced binary classsification problem
How to build classification models using H2O in R
Tuning GBMs (hyperparameter tuning) and impact on out-of-sample predictions
NTUEE Machine Learning, 2017 Spring
Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)
Linear regression at transformed features by gradient boosting machine
Pump It Up: Data Mining the Water Table
Python and R data analysis
Analysis of Spotify user data to create a predictive model using machine learning.
R package for automatic hyper parameter tuning and ensembles with deep learning, gradient boosting machines, and random forests. Powered by h2o.
useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html
Predict sales prices and practice feature engineering, RFs, and gradient boosting
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