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

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.

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 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

Predict the employee burn out rate

This project aims is to predict whether an employee will leave or remain in the organization depending upon various factors using an ML classification model. Also if the employee leaves, we predict within how much time he/she leaves by using an ML regression model and deploy the Machine Learning model using FLASK.

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.