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

Project 2 Group C - Predicting FinTech Bootcamp Graduate Salaries

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

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

Prediction of forest cover type in Python.

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

Predicting Appliance Energy use in Residential Buildings.

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

Data Science Project (Autonomous Driving Dataset Analysis)

Credit Card Fraud Detection with Python. Implemented various classification algorithms in scikit-learn and Built a Neural Network in Tensorflow

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

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.

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 website visualising various classification algorithms

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.

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.

Decision Tree Classification on the Forest Cover Type dataset with overfitting analysis, hyperparameter tuning, feature importance and Random Forest comparison.