Recommending Similar Board Games is a practical Python and NumPy project for learners who want to turn a small catalog into useful, understandable alternatives for a game night. You will first decide which games can actually be used, then compare their play time and complexity with one fixed, published distance rule.
Two Guided Labs teach identity-safe eligibility, full-catalog scaling, complete distance tables and cutoff ties. An independent Challenge applies the same workflow to another library. Your deliverables are a rerunnable program and real saved results for an ordinary night, a night with few alternatives and a night with none.
What You Will Learn
After completing this course, you will be able to:
- associate catalog and borrowing records by stable game ID, preserving distinct editions with the same title;
- separate player-count and availability requirements from content similarity;
- derive one full-catalog MinMax scale and reuse it for the reference and every eligible candidate;
- compute transparent Euclidean distances and explain candidates with signed original-unit differences;
- retain every cutoff tie and handle insufficient or empty candidate sets honestly;
- save and read actual JSON results that can be regenerated from unchanged CSV inputs.
Who This Course Is For
This course is for learners with basic Python experience who want a small, explainable data project rather than a model competition.
Prerequisites: Python functions, dictionaries, basic NumPy arrays, introductory table reading and basic terminal input. Familiarity with K-nearest-neighbor classification is helpful, but no scikit-learn API knowledge is required. The Guided Labs teach the no-label retrieval, ID association and scaling rules used here.
Learning environment: Each unit provides an independent browser-based WebIDE with Python, NumPy, original synthetic data and small input/output helpers ready to use. You complete the decision functions and inspect real saved files. No local installation or external dataset account is needed.
Frequently Asked Questions
Will this predict which game someone will enjoy?
No. It retrieves usable games with similar play time and complexity under an explicit metric. Personal preference and large-scale recommendation performance are outside the course.
Do I need machine-learning labels or scikit-learn?
No. There is no target label to predict and no supervised training/test split. You use NumPy to calculate a transparent distance; scikit-learn is not a dependency.
What makes the Challenge independent?
It supplies another catalog, reference, lending-state table and requests. You apply the same tools and acceptance rules taught in the Guided Labs, with a complete environment of its own.
