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Understanding Model Predictions with SHAP - XGBoost vs Neural Networks (375)
In this tutorial, I walk you through SHAP (SHapley Additive exPlanations) - one of the most powerful tools for understanding what your machine learning models are actually doing. I explain SHAP using a simple game theory analogy: if a team of 5 people wins $1000, how do you fairly divide the prize based on each person's contribution? SHAP ...
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🚀 Excited to share my latest machine learning project: Bank Customer Churn Prediction! I've built a comprehensive ML pipeline that predicts which bank customers are likely to churn with 87% accuracy using: 🔹 **Algorithms Used:** LightGBM, XGBoost, CatBoost 🔹 **Key Features:** Age, Balance, Geography, Activity Status 🔹 **Performance:** 0.872 ROC-AUC Score **Key Insights:** • German customers have 32% higher churn rate • Inactive members are 2.5x more likely to leave • Customers with 2 product
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🚀 Excited to share my latest machine learning project: Bank Customer Churn Prediction! I've built a comprehensive ML pipeline that predicts which bank customers are likely to churn with 87% accuracy using: 🔹 **Algorithms Used:** LightGBM, XGBoost, CatBoost 🔹 **Key Features:** Age, Balance, Geography, Activity Status 🔹 **Performance:** 0.872 ROC-AUC Score **Key Insights:** • German customers have 32% higher churn rate • Inactive members are 2.5x more likely to leave • Customers with 2 product
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Tugas Akhir 14 – Implementasi Ensemble Learning dengan XGBoost Pada video ini ditampilkan proses pembuatan sistem prediksi kepuasan pelanggan e-commerce menggunakan algoritma XGBoost. Proses dimulai dari pengolahan dan pelatihan data pada Jupyter Notebook, evaluasi model menggunakan Confusion Matrix dan ROC Curve, hingga implementasi model ke dalam aplikasi web interaktif berbasis Streamlit untuk melakukan prediksi kepuasan pelanggan secara langsung. Sistem ini diharapkan dapat membantu dalam me
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Tugas Akhir 14 – Implementasi Ensemble Learning dengan XGBoost Pada video ini ditampilkan proses pembuatan sistem prediksi kepuasan pelanggan e-commerce menggunakan algoritma XGBoost. Proses dimulai dari pengolahan dan pelatihan data pada Jupyter Notebook, evaluasi model menggunakan Confusion Matrix dan ROC Curve, hingga implementasi model ke dalam aplikasi web interaktif berbasis Streamlit untuk melakukan prediksi kepuasan pelanggan secara langsung. Sistem ini diharapkan dapat membantu dalam me
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