This repo includes codes for ML Zoomcamp. If you can follow the tutorials from the link here: https://www.youtube.com/watch?v=rowoDjPc8HU&list=PL3MmuxUbc_hIhxl5Ji8t4O6lPAOpHaCLR 👩🏼💻
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
4. Evaluation Metrics for Classification
4.1 Evaluation metrics: session overview
4.2 Accuracy and dummy model
4.3 Confusion table
4.4 Precision and Recall
4.5 ROC Curves
4.6 ROC AUC
4.7 Cross-Validation
4.8 Summary
All notebooks links are here 👉 https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp