Predict churn with logistic regression + gradient boosting, then explain every prediction with SHAP so the model is decision-ready.
Domain: business_analytics · Relevant roles: Data Analyst, Product Analyst, Business Intelligence Analyst
Why this matters and the decision it informs.
Source: Telco Customer Churn (Kaggle/IBM)
Place raw files in data/ (git-ignored). Document how to obtain them here.
- Data cleaning & EDA
- Modeling / analysis
- Validation & interpretation
- Business recommendation
Add the headline chart and the one-sentence takeaway here (this is what recruiters read).
Python, XGBoost, SHAP, classification
pip install -r requirements.txt
jupyter notebook notebooks/01_analysis.ipynbPart of Ela Kumuk's analytics portfolio.