Getting Started
1
BEGINNER
Quick Start Guide
Set up your environment and make your first prediction in 5 minutes.
15 min
Start →
2
BEGINNER
Understanding Your Data
Learn to explore and prepare customer data for churn modeling.
30 min
Start →
3
BEGINNER
First XGBoost Model
Train your first churn prediction model with XGBoost.
45 min
Start →
Intermediate
Feature Engineering
Create powerful features from raw customer data.
60 min
Start →
Hyperparameter Tuning
Optimize model performance with Optuna.
75 min
Start →
SHAP Explanations
Make your model interpretable with SHAP values.
60 min
Start →
Advanced
Production Deployment
Deploy models with FastAPI, Docker, and Kubernetes.
2 hrs
Start →
Model Monitoring
Track drift, performance, and automate retraining.
90 min
Start →
A/B Testing Framework
Design and analyze retention experiments.
2 hrs
Start →
Recommended Learning Path
1
Week 1-2: Foundations
Quick Start → Data → First Model
2
Week 3-4: Optimization
Features → Tuning → SHAP
3
Week 5-6: Production
Deploy → Monitor → A/B Test
Prerequisites
- • Python basics
- • pandas/numpy
- • ML fundamentals