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.

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.

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

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.

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

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.

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.

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.

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.

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

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.

It was a competition on KAGGLE for prediction on the most sales products on bikes via their features

U.S.A. house prediction