Popular GitHub Repositories for Ensemble Machine Learning

A collection of companion Jupyter notebooks for Ensemble Methods for Machine Learning (Manning, 2023)

OptimalFlow is an omni-ensemble and scalable automated machine learning Python toolkit, which uses Pipeline Cluster Traversal Experiments(PCTE) and Selection-based Feature Preprocessor with Ensemble Encoding(SPEE), to help data scientists build optimal models, and automate supervised learning workflow with simpler coding.

A curated list of Awesome-LLM-Ensemble papers for the survey "Harnessing Multiple Large Language Models: A Survey on LLM Ensemble"

An improved method for predicting toxicity of the peptides and designing of non-toxic peptides

Projects which were completed as part of assignments of Great Learning's PGP in Artificial Intelligence and Machine Learning

This is our second project at neuefische DS Bootcamp. Silas Mederer (https://github.com/sls-mdr) and me applied different ML models and for credit default prediction of the P2P platform Lending Club.

This project focuses on the prediction of lung cancer using multiple machine learning models and comparing their performances. The dataset used for this project consists of various features related to lung cancer, which were preprocessed, normalized, and then used for training different classifiers.

A end-to-end ML project for River Flow prediction in Southampton using data from multiple Gov websites

Text Mining (PubMed Search) with NLP & LLM

? Stock Movement Study ? Time-series forecasting of S&P500 trends using machine learning models and ensembling techniques, highlighting interpretable approaches in financial prediction.

End to End machine learning project on classification using cloud based deployment

Advanced Machine Learning Regression: Predicting Car Prices

Real time bill data detection, classification and classified expenditure generation using machine learning algorithms and natural language processing techniques

模型预测2025年日本大奖赛的冠军是马克斯·维斯塔潘，他将赢得比赛！

This project implements functions for building an ensemble of classifiers and regressors using the TPOT and auto-sklearn libraries with the help of a genetic algorithm evolved using the deap library.

Developed a machine learning model using scikit-learn, implementing ensemble techniques, PCA, correlation analysis, and extensive feature engineering. The goal was to classify documents as either human-generated (0) or AI-generated (1) based on document embeddings, word count, and punctuation.