经典机器学习算法的极简实现

this a project for predicting the next word in a sequence using various models.

A machine learning project for predictive maintenance, designed to forecast equipment failures and optimize maintenance schedules to reduce downtime and operational costs

Two algorithms based on linear programming to discover classification rules for interpretable learning.

AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2

A collection of boosting algorithms written in Rust 🦀

Machine learning models for predicting car prices

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

A machine learning project to classify news as real or fake using multiple algorithms, with visualizations for performance comparison. Perfect for exploring NLP and fake news detection techniques.

Credit Card Approval Prediction using Soft Voting, Hard Voting, ADABoost, and XGBoost

Insanely fast Open Source Computer Vision library for ARM and x86 devices (Up to #50 times faster than OpenCV)

This project applies sentiment analysis on Twitter data, classifying tweets as positive, negative, or neutral using machine learning models and BERT. It includes data cleaning, TF-IDF vectorization, and data augmentation techniques to enhance model performance.

👾my personal implementation of classical machine learning algorithms from first principles (as a phd student at stony brook university)

DVboost R package

An algorithmic trading strategy incursion using Adaboost machine learning classifier, to create the first volatility security suitable for long term investors.

A repository contains more than 12 common statistical machine learning algorithm implementations. 常见机器学习算法原理与实现

A hybrid classification and prediction model for credit risk analysis in R

Finding donors for charity using Machine Learning.

a classification problem using ensemble methods on the Titanic dataset.

This project combines microplastic detection through computer vision with a machine learning-based water potability prediction tool, using AdaBoost for accurate results.