Capstone I: Regression

This capstone is the first student-directed project in the curriculum. Rather than walking through a provided dataset with step-by-step instructions, you choose a dataset, define a regression problem, and execute the full workflow independently — from EDA through final model evaluation.

The capstone spans three lessons. The first introduces the project structure, deliverables, and a curated list of suggested datasets. The second provides scaffolding for EDA, cleaning, and feature engineering. The third guides you through training, comparing, and tuning regression models and writing your findings.

The deliverable is a single Jupyter notebook on GitHub that tells the complete story of your analysis, plus a three-paragraph written summary.

Lessons

  1. Intro to Capstone — Project overview, deliverables, suggested datasets, and the assessment rubric.
  2. Project Planning & Dataset Exploration — Problem framing, EDA templates, cleaning checklist, and feature engineering scaffold.
  3. Modeling & Evaluation — Training three models, cross-validation, GridSearchCV hyperparameter tuning, residual analysis, and conclusion.