extra-trees

using Drebin dataset to distinguish between malwares and not malwares

Rice Crop Yield Estimation Using Satellite Data - EY Open Science Data Challenge 2023

End-to-End Used Car Price Prediction using Ensemble Learning | Extra Trees, Random Forest, Gradient Boosting | Python • Scikit-learn

This repository contains the source code to reproduce the paper "Feature-based No-Reference Video Quality Assessment using Extra Trees".

Project 2 Group C - Predicting FinTech Bootcamp Graduate Salaries

Fast gradient-boosted decision trees for Apple Silicon. A lightweight, sklearn-style Python API powered by MPS and Metal.

Machine learning pipeline for predicting employee attrition using ensemble models and feature engineering.

Prediction of forest cover type in Python.

Mitsui Kaggle — ExtraTrees with single-lag & group-lag features

An AI-driven hotel classification system using Extra Trees with SHAP-based explainability. Includes Streamlit frontend and FastAPI backend for deployment.

Predicting Appliance Energy use in Residential Buildings.

Precision-preserving optimized inference for one-dimensional reactive contaminant transport, with validated ADR1D-ML-Compact and ADR1D-NN-Fast models and reproducible Python workflows.

Sampling Assignment: Download dataset, balance classes, apply ML models with different sampling techniques to evaluate performance.

Data Science Project (Autonomous Driving Dataset Analysis)

End-to-end Data Science project on Amazon Sales. Features data cleaning, EDA, outlier detection, and predictive modeling using Python, Pandas, and Scikit-learn.

A Python tool for trading signals on global markets based on machine learning

A dashboard where a pretrained ML model predicts whether a user is diabetic, or estimates their diabetes risk score, while gpt-5-mini uses its shap values and prediction to generate a report, which is then formatted into a downloadable PDF.

LaTeX source for the article 'Improving Bimonthly Landscape Monitoring in Morocco, North Africa, by Integrating Machine Learning with GRASS GIS' (Lemenkova, Geomatics 2025, 5(1):5). Decision Tree and Extra Trees classification of a bimonthly Landsat time series in GRASS GIS.

An explainable machine learning project for heart disease prediction, featuring exploratory data analysis, model comparison, hyperparameter tuning, and SHAP explanations.

This project aims to predict the price of laptops based on their technical specifications using various machine learning models. The dataset includes attributes like brand, processor, RAM, memory type, GPU, screen size, and operating system.