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

🥉2023 Power Consumption Prediction AI Competition🥉

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.

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 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.

This repository contains several machine learning projects done in Jupyter Notebooks

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

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

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

MediaEval challenge 2019 - to predict the memorability of the Videos

A collection of machine learning models for predicting laptop prices

Predicting House Prices with Machine Learning

I code from scratch various Machine Learning algorithms.

Machine Learning

Codes for food store presence, density and popularity predictor. Merges census tract-level demographic data from ACS, neighborhood amenities from heterogenous sources, and Point of Interest (POI) data from anonymized cellphone GPS ‘pings’ to identify food retailer location and foot traffic information.