An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.
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Updated
Jan 14, 2023 - PHP
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.
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
I code from scratch various Machine Learning algorithms.
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.
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