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Sep 23, 2021 - Jupyter Notebook
📱 통신 서비스에 있어서 고객 이탈 요인을 분석하고, 🏃♂️이탈 가능성을 예측하는 모델을 제작했습니다.
Customer churn, also known as customer attrition, customer turnover, or customer defection, is the loss of clients or customers. Telephone service companies, Internet service providers, pay-TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business…
This project explores customer churn trends for a company in California using an IBM dataset. Built in a Jupyter Notebook, it employs pandas, NumPy, matplotlib, seaborn, plotly, and scipy to clean, analyze, and visualize data. SKlearn predictive model was trained using three main algorithms Decision Tree, Naive Bayes, and Random Forest
💰 Probability-based churn prediction pipeline with threshold tuning, risk segmentation, and business-focused evaluation.
Churn Prediction for Waze Users Using Random Forest and XGBoost
Exploring high-recall churn prediction (82%) for telecom data.
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