This repository contains machine learning notebooks focused on supervised learning (regression and classification). Each one is a guided Jupyter Notebook that walks through a key stage of the data-science workflow — combining conceptual explanations, analytical exploration, live coding exercises, and sample solutions.
Interactive Mode
Click the Launch Binder button above to experience the notebooks in interactive mode. It may take a minute to load the first time you launch.
Static Mode
Head to the supervised-learning repository to experience the notebooks in static mode. Note that if you wish to download notebooks to experience them locally, it is highly recommended that you first set up environment.yml (available in the repository).
- Script 01 - Base Modeling
- Script 02 - Missing Value Analysis and Transformations
- Script 03 - Feature Engineering
- Script 04 - Feature Engineering with Discrete Data
- Script 05 - The Model Building Framework
- Script 06 - Advanced Linear Models
- Script 07 - KNN and Distance Standardization
- Script 08 - Nonparametric Modeling with Regression Trees
- Script 09 - Hyperparameter Tuning
- Script 10 - Nonparametric Ensemble Models
- Script 01 - Preparing the Titanic Dataset
- Script 02 - From Regression to Classification
- Script 03 - Changing the Prediction Threshold
- Script 04 - Classification Trees
- Script 05 - Hyperparameter Tuning and Ensemble Modeling
- Script 06 - Classification Modeling with Unsupervised Data
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© 2025 Chase Kusterer. All rights reserved.
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You are free to share and adapt the material for non-commercial purposes, as long as you give appropriate credit and indicate if changes were made.
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