gradient-boosting-regressor
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An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.
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Jul 25, 2025 - PHP
It's a github repo star predictor that tries to predict the stars of any github repository having greater than 100 stars.
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Mar 1, 2018 - Jupyter Notebook
Automated Essay Scoring on The Hewlett Foundation dataset on Kaggle
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Apr 26, 2018 - Jupyter Notebook
Computer Intelligence subject final project at UPC.
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Jan 28, 2020 - Python
Open source gradient boosting library
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Oct 21, 2021 - Python
Machine Learning model for price prediction using an ensemble of four different regression methods.
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Apr 12, 2022 - Python
Predicting the Residential Energy Usage across 113.6 million U.S. households using Machine Learning Algorithms (Regression and Ensemble)
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Sep 1, 2022 - Jupyter Notebook
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.
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Jan 8, 2024 - Jupyter Notebook
Unified interface for Gradient Boosted Decision Trees
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Dec 1, 2025 - Jupyter Notebook
This repository contains codes, datasets, results, and reports of a machine learning project on air quality prediction.
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Apr 15, 2023 - Python
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.
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Nov 17, 2023 - Jupyter Notebook
A Machine Learning Model built in scikit-learn using Support Vector Regressors, Ensemble modeling with Gradient Boost Regressor and Grid Search Cross Validation.
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Nov 10, 2022 - Python
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.
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Nov 15, 2024 - Jupyter Notebook
This repository enables an engineer to generate predictions for the mechanical bending performance of corroded beams, using a database of 725 corroded beams tested under monotonic bending. Outputs include the maximum bending moment, residual capacity percentage, yield load, yield displacement, and ultimate displacement.
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Jun 28, 2024 - Python
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.
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Apr 11, 2025 - Jupyter Notebook
This repository contains several machine learning projects done in Jupyter Notebooks
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Jun 10, 2019 - Jupyter Notebook
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.
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Jun 28, 2024 - Python
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.
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Jun 28, 2024 - Python
A gradient-boosted tree framework to model the ice thickness of the World's glaciers
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Dec 8, 2025 - Python
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