🌐 Customer Churn Prediction: Cell2Cell Dataset

Libraries: scikit-learn, matplotlib, seaborn, XGBoost, TensorFlow, SHAP
Dataset: Cell2Cell Churn Dataset

In this project, we explore the Cell2Cell dataset and build three supervised machine learning models - Decision Tree, XGBoost, and a Neural Network (TensorFlow) — to predict customer churn risk. The workflow includes feature engineering, model tuning, performance evaluation, and SHAP-based interpretability.

🧠 Analytical Approach

• Feature engineering with tenure, billing, call usage, and derived behavioural indicators
• Categorical Optimisation via native support in XGBoost and embedding layers in the neural network
• Model Evaluation using accuracy, precision, recall, and $F_1$ score on both validation and test sets
• Global Interpretability through SHAP to compare feature importance across architectures
• Confusion Matrix Analysis to diagnose prediction trade-offs and guide threshold tuning
  
  

📊 Results

• XGBoost emerged as the top-performing model, with validation recall of 75.1% and a stable $F_1$ score of 0.495 on test data
• Neural Network achieved higher validation accuracy but struggled with recall, indicating overfitting to the majority class
• SHAP analysis revealed consistently influential features across models
• Confusion matrix analysis highlighted a recall-focused strategy: most churners were correctly flagged, though false positives remained substantial

🔮 Next Steps

• Refine the Neural Network with dropout, class weighting, and early stopping to improve recall
• Extend SHAP analysis

📖 Jupyter Notebook: GitHub | CoLab