A python library for decision tree visualization and model interpretation.
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Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
- machine-learning
- scikit-learn
- statistical-learning
- xgboost
A library for debugging/inspecting machine learning classifiers and explaining their predictions
- scikit-learn
- machine-learning
- xgboost
- lightgbm
📊⚽ A collection of football analytics projects, data, and analysis by Edd Webster (@eddwebster), including a curated list of publicly available resources published by the football analytics community.
- football
- xg
- expected-goals
- statsbomb
Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
- visualization
- python3
- python
- xgboost
Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models
- forecasting
- scikit-learn
- machine-learning
- python
Scalable machine 🤖 learning for time series forecasting.
- forecast
- forecasting
- machine-learning
- lightgbm
A standalone embedding of the FOSS drawio / mxgraph package into jupyterlab
Comparing the performance of Stacked Ensemble Learning & machine learning algorithms like Random Forest, Decision Tree, Adaboost, Gradient Boost and XGBoost Classifier in Python for Stock Market Trend Prediction.