Machine Learning Foundations / Module 02: Your first prediction experiment
All files contain fictional learning data. Keep an untouched source copy.
1. Extract practice_pack.zip to a new folder on your own device.
2. Read data_dictionary.txt and attempt learner_worksheet.txt.
3. For code tasks, use Python 3.12.14 in a local virtual environment. macOS: python3 -m venv .venv then source .venv/bin/activate. Windows: py -m venv .venv then .venv\Scripts\activate.
4. If requirements.txt is present: python -m pip install -r requirements.txt. This is your local environment; no automatic installation is performed by the website.
5. Rename the selected downloaded .py.txt reference copy to .py. Do not rename the learner scaffold as the worked script.
6. Command: python pilot_reference.py
7. Keep the reference separate from your own implementation. Compare worked_solutions.txt only after attempting the exercises.
8. Commands write only to a NEW requested output path. For a rerun choose run-02 or experiment-02.json. No uploads, website login, hosting database or live service is needed.
9. The worked_reference.html is a static offline lesson reference; it does not execute Python, install software, save progress or refresh from adjacent files.
Payments, public registration and new paid enrolment remain disabled. Paid lesson files require existing approved course access on the website. No certificate or career outcome is promised.

FILES
pilot_parcels.csv: Pilot parcels
data_dictionary.txt: Source dictionary & timing
learner_worksheet.txt: Three practical exercises
worked_solutions.txt: Worked solutions with reasoning
prediction_contract_template.txt: Prediction contract template
error_review_template.csv: Error review template
pilot_reference.py.txt: Pilot worked Python code
requirements.txt: Pilot dependency
expected_results.json: Checked reference results
setup_guide.txt: Local setup and reproduction
review_rubric.txt: Evidence-based self-review
worked_reference.html: Complete offline worked reference