[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

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

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

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

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

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

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…

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

This project presents a ML based solution using Ensemble methods to predict which visa applications will be approved and thus recommend a suitable profile for applicants whose visa have a high chance of approval

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

This is an assignment from my Machine Learning for Mechanical Engineers course that demonstrates an understanding in decision trees and ensemble methods using scikit-learn.

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.

(Week 13) Rakamin Homework : Supervised Learning 2

A collection of AI and ML projects demonstrating various techniques, algorithms, and applications.