An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.

It's a github repo star predictor that tries to predict the stars of any github repository having greater than 100 stars.

Automated Essay Scoring on The Hewlett Foundation dataset on Kaggle

Computer Intelligence subject final project at UPC.

Open source gradient boosting library

Machine Learning model for price prediction using an ensemble of four different regression methods.

Predicting the Residential Energy Usage across 113.6 million U.S. households using Machine Learning Algorithms (Regression and Ensemble)

This is a hybrid recommender system that combines the paradigms of content based filtering(using gradient boosting regressor) and collaborative filtering to recommend destination spots for users/tourists based on their demography and spots liked by tourists with similar demography and likes.

Unified interface for Gradient Boosted Decision Trees

This repository contains codes, datasets, results, and reports of a machine learning project on air quality prediction.

Gradient Boosting Regressor in Go

A Machine Learning Model built in scikit-learn using Support Vector Regressors, Ensemble modeling with Gradient Boost Regressor and Grid Search Cross Validation.

Using publicly available data for the national factors that impact supply and demand of homes in US, build a data science model to study the effect of these variables on home prices.

This repository contains several machine learning projects done in Jupyter Notebooks

MSBoost is a gradient boosting algorithm that improves performance by selecting the best model from multiple parallel-trained models for each layer, excelling in small and noisy datasets.

This project aims to predict home prices using various economic indicators from the Federal Reserve Economic Data (FRED). The project involves data collection, data preparation, model building, and analysis of the results.

Example machine learning implementation to predict the residual bending moment capacity of corroded reinforced concrete beams tested under monotonic three or four-point bending. Data is collected from 54 experimental programs available in the literature.

Machine learning demonstration of the Gradient Boosting algorithm and it's effectiveness on a regression dataset of house prices.

Soil moisture analysis , prediction and decision making to irrigate or drain water from field using Machine Learning ,numpy ,pandas , sklearn , matplotlib , Gradient Boosting Regressor model, linear regression model .

Example machine learning applications for the determination of the residual yield force of corroded steel bars tested under monotonic tensile loading. Data is collected from 26 experimental programs avaialbe in the literature.