03-classification

3. Machine Learning for Classification

• 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

Community notes

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
• Add your notes here