Planned tools for making pipeline development and data reproduction easier
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Sep 9, 2020 - Python
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pipeline-development
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Production-ready ML pipeline for telco customer churn prediction using advanced ensemble methods (XGBoost, CatBoost, Random Forest). Handles class imbalance, provides business insights, and includes modular MLOps architecture. Built with scikit-learn, featuring comprehensive EDA, feature engineering, and business impact analysis.
python machine-learning random-forest scikit-learn xgboost data-preprocessing feature-engineering catboost ensemble-methods mlops pipeline-development telco-analytics
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Aug 23, 2025 - Jupyter Notebook
A simple library for the implementation of the Pipeline Development pattern in .NET
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Mar 12, 2024 - C#
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