Recommender system using XGBOOST, Neural_Network, Ensemble and LGBM
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Updated
May 31, 2024 - Jupyter Notebook
Recommender system using XGBOOST, Neural_Network, Ensemble and LGBM
Train Gradient Boosting models that are both high-performance *and* Fair!
Time series forecasting with scikit-learn models
📘 The MLOps stack component for experiment tracking
An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Generate insights and rankings for potential candidates
Simple and Distributed Machine Learning
Cambridge UK temperature forecast python notebooks
Clustering employee performances to predict resignation likelihood and develop strategies for employee retention
A repository for almost every machine learning algorithms
R package for automation of machine learning, forecasting, model evaluation, and model interpretation
Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
gradient-boosted regression and decision tree models on behavioural animal data
All Relevant Feature Selection
Behavior-Based Malware Detection using GBDTs
Amazon SageMaker Local Mode Examples
Python interface to automatically formulate Machine Learning models into Mixed-Integer Programs
Forecasting Ethereum return quantiles using a handful of different statistical learning models and selecting the best based on out of sample error. Hopsworks feature store and model registry is used to automate the process. Ethereum quantile returns are predicted daily and displayed on a Streamlit dashboard.
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