hgboost
hgboost is a python package for hyper-parameter optimization for xgboost, catboost or lightboost using cross-validation, and evaluating the results on an independent validation set. hgboost can be applied for classification and regression tasks.
mljar-supervised
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
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.
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.