skforecast
Time series forecasting with machine learning models
lightgbm
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.
lleaves
Compiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x.
mljar-supervised
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
pyscipopt-ml
Python interface to automatically formulate Machine Learning models into Mixed-Integer Programs
eland
Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch
https://github.com/nixtla/mlforecast
Scalable machine 🤖 learning for time series forecasting.
lightsnip
Hard fork of curso-r/treesnip specifically for CCAO LightGBM regressions
https://github.com/amr-yasser226/intrusion-detection-kaggle
End-to-end pipeline for multi-class cyber-attack detection using per-flow network features: data profiling, deduplication, skew-correction, outlier treatment, feature engineering, imbalance handling, and tree-based modeling (XGBoost, LightGBM, CatBoost, stacking), with a final Kaggle submission scoring 0.9146 public / 0.9163 private.
https://github.com/ccao-data/report-model-benchmark
Benchmark of timing for CCAO models on different hardware
https://github.com/ahmedshahriar/customer-churn-prediction
Extensive EDA of the IBM telco customer churn dataset, implemented various statistical hypotheses tests and Performed single-level Stacking Ensemble and tuned hyperparameters using Optuna.
crop-yield-estimate
Harness the power of machine learning to forecast rice and wheat crop yields per acre in India, aiming to empower smallholder farmers, combat poverty and malnutrition, utilizing data from Digital Green surveys to revolutionize agriculture and promote sustainable practices in the face of climate change for enhanced global food security.