[ICDE'20] ⚖️ A general, efficient ensemble framework for imbalanced classification. | 泛用，高效，鲁棒的类别不平衡学习框架

tools for scalable and non-intrusive parameter estimation, uncertainty analysis and sensitivity analysis

AI-CryptoTrader is a state-of-the-art cryptocurrency trading bot that uses ensemble methods to make trading decisions based on multiple sophisticated algorithms. Built with the latest machine learning and data science techniques, AI-CryptoTrader provides a powerful toolset and advanced trading stratgies for maximizing your cryptocurrency profits.

Handwritten digit recognition with MNIST & Keras

Winning 2nd place🥈at NUS CS5228 in-class Kaggle competition 2018!

This project focuses on using the AWS open-source AutoML library, AutoGluon, to predict bike sharing demand using the Kaggle Bike Sharing demand dataset.

Time series forecasting with Fourier-adjusted time dummies

🛰️ Advanced ML system for geomagnetic storm prediction | 96.5% AUC | LightGBM + BiGRU meta-ensemble | 29-year space weather dataset | Production-ready

Mathematical theory, code examples, and production implementations of classification, regression, trees, SVMs, ensemble methods, and neural networks

Project for Titanic survival prediction, achieving 83.28% accuracy through advanced feature engineering, hyperparameter optimization, and ensemble methods.

Capstone project #2 for the Harvard University Professional Certificate in Data Science

A financial fraud detection & credit risk scoring system utilizing a variety of techniques

Official Implementation of Track2Vec: Fairness Music Recommendation with a GPU-Free Customizable-Driven Framework EvalRS-CIKM-2022

User documentation website for the Sulis tier 2 HPC service. Built using Jekyll.

This project studies different possibilities to make good predictions based on machine learning algorithms, but without requiring great theoretical knowledge from the users. Moreover, a software package that implements the prediction process has been developed. The software is an ensemble method that first predicts a value taking into account di…

Production-ready ML pipeline for telco customer churn prediction using advanced ensemble methods (XGBoost, CatBoost, Random Forest). Handles class imbalance, provides business insights, and includes modular MLOps architecture. Built with scikit-learn, featuring comprehensive EDA, feature engineering, and business impact analysis.

Predict sale prices via regression models, using PCA, k-means clustering, ensemble models, pipelines, etc.

Comparison of ensemble learning methods on diabetes disease classification with various datasets

Ensemble Deep Random Vector Functional Link with Skip Connections (edRVFL-SC) No GPU required • 100× faster training

Build a classification model to predict clients who are likely to default on their loans. Give recommendations to the bank on important features to consider while approving a loan. Concepts Used: Logistic Regression, Decision Trees, Random Forests, and Ensemble Methods