- 3.1 Churn prediction project
- 3.2 Data preparation
- 3.3 Setting up the validation framework
- 3.4 EDA
- 3.5 Feature importance: Churn rate and risk ratio
- 3.6 Feature importance: Mutual information
- 3.7 Feature importance: Correlation
- 3.8 One-hot encoding
- 3.9 Logistic regression
- 3.10 Training logistic regression with Scikit-Learn
- 3.11 Model interpretation
- 3.12 Using the model
- 3.13 Summary
- 3.14 Explore more
- 3.15 Homework
Did you take notes? You can share them here (or in each unit separately)
- Notes from Kwang Yang
- Notes from Jaime Hipólito Cabrera
- Notes from Sebastián Ayala Ruano
- Notes from Nikhil Shrestha
- Default of Credit Card Clients (Additional Project) from Nikhil Shrestha
- Notes from Alvaro Navas
- Notes from froukje
- Notes from Hareesh Tummala
- Notes from Giorgos Verikios
- Notes from Memoona Tahira
- Notes from Marcos Benício
- Notes from Oscar Garcia
- Notes from Peter Ernicke
- Notes from Kemal Dahha
- Notes from Maximilien Eyengue
- Add your notes here