Build a preprocessing step that cannot see the future

Before you begin

• Python + ML prerequisite; inspect the worked fixture before beginning.
• A text editor/spreadsheet for the manual route. Hosted Python/Jupyter for code exercises; applicable dependencies are in requirements.txt.
• For actual AI calls, a legitimate available cloud service and authorised learner credentials; no particular provider or country is mandatory.

Free original, non-accredited practice. Self-review is not independent assessment. No particular provider, payment platform or country is mandatory; use a legitimate available route or explicitly labelled manual practice.

Mark the training boundary

Preprocessing learns parameters such as means from training data. Including test values in that calculation leaks information before fitting a predictor. Keep fitted parameters explicit so the boundary can be checked even in a tiny fixture. Write down which rows the fitted transformation is allowed to inspect. Inspect the supplied input before using any tool. Write the decision rules in your own words and calculate one small result independently. The reference output is an answer key for this deliberately bounded fixture, not proof of general capability. Policy for this project: Fit a mean imputer on training values only, ignoring null. Transform train and test using that one mean; never refit on the test set. For learners across regions underserved or excluded by mainstream providers: verify country eligibility, language, account/identity requirements, data terms, cost, quota and connectivity before choosing a cloud service. Use only legitimate available alternatives; never bypass restrictions or invent a country/account. No service access? Complete the labelled manual/fixture route without claiming a live AI run.

Worked example

SOURCE (authored fixture):
{
  "train": [
    2,
    null,
    4
  ],
  "test": [
    100,
    null
  ]
}
INDEPENDENT FIXTURE TARGET:
{
  "training_mean": 3,
  "train_filled": [
    2,
    3,
    4
  ],
  "test_filled": [
    100,
    3
  ]
}

Your practical task

1. Write down which rows the fitted transformation is allowed to inspect.
2. Open input.json and expected.json from the practice pack; keep an unmodified original.
3. Record a manual prediction and the relevant rule before consulting the answer key.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Mark the training boundary — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Including 100 in the fit would change the mean: detect the leakage.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Calculate the permitted mean

Preprocessing learns parameters such as means from training data. Including test values in that calculation leaks information before fitting a predictor. Keep fitted parameters explicit so the boundary can be checked even in a tiny fixture. Use only the observed training values. Establish a baseline before changing prompts or code. Keep the original input immutable. Record expected values, observed values and whether each comparison passed. If the expected file conflicts with a justified policy, investigate rather than forcing a convenient match. Policy for this project: Fit a mean imputer on training values only, ignoring null. Transform train and test using that one mean; never refit on the test set.

Worked example

MANUAL BASELINE / compare every named field
{
  "training_mean": 3,
  "train_filled": [
    2,
    3,
    4
  ],
  "test_filled": [
    100,
    3
  ]
}
Keep a separate observed-result column; the answer key is not a model response.

Your practical task

1. Use only the observed training values.
2. Open input.json and expected.json from the practice pack; keep an unmodified original.
3. Record expected and observed values separately; explain every mismatch.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Calculate the permitted mean — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• All training values missing: stop and request a stated fallback policy.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Implement fit then transform

Preprocessing learns parameters such as means from training data. Including test values in that calculation leaks information before fitting a predictor. Keep fitted parameters explicit so the boundary can be checked even in a tiny fixture. Return fitted parameters separately from transformed data. Build one inspectable transformation or bounded AI prompt. Keep calculation and validation deterministic where possible. If you make a permitted hosted call, save the actual response separately from the authored examples and verify its material claims; the supplied reference never simulates a live model. Policy for this project: Fit a mean imputer on training values only, ignoring null. Transform train and test using that one mean; never refit on the test set.

Worked example

PROJECT CONTRACT:
Fit a mean imputer on training values only, ignoring null. Transform train and test using that one mean; never refit on the test set.
HOSTED REFERENCE: open hosted-lab.ipynb or run reference.py with all fixture files together.
EXPECTED CONSTRUCTED RESULT:
{
  "training_mean": 3,
  "train_filled": [
    2,
    3,
    4
  ],
  "test_filled": [
    100,
    3
  ]
}

Your practical task

1. Return fitted parameters separately from transformed data.
2. Open hosted-lab.ipynb and the reference code from the practice pack; keep an unmodified original.
3. Record expected and observed values separately; explain every mismatch.
4. Perform A new extreme test value must not change stored training parameters.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Implement fit then transform — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• A new extreme test value must not change stored training parameters.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Inject an extreme test value

Preprocessing learns parameters such as means from training data. Including test values in that calculation leaks information before fitting a predictor. Keep fitted parameters explicit so the boundary can be checked even in a tiny fixture. Demonstrate that training parameters do not change. Change one assumption at a time and predict the result first. Retain the failure, the revision and the rerun evidence. A failure is useful information: hiding a bad output or editing a test target after seeing the answer destroys the value of the experiment. Policy for this project: Fit a mean imputer on training values only, ignoring null. Transform train and test using that one mean; never refit on the test set.

Worked example

CHANGE TESTS — predict before running:
1. Including 100 in the fit would change the mean: detect the leakage.
2. All training values missing: stop and request a stated fallback policy.
3. A new extreme test value must not change stored training parameters.
Keep the original expected.json unchanged.

Your practical task

1. Demonstrate that training parameters do not change.
2. Open failure-tests.md from the practice pack; keep an unmodified original.
3. Record expected and observed values separately; explain every mismatch.
4. Perform Including 100 in the fit would change the mean: detect the leakage.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Inject an extreme test value — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Including 100 in the fit would change the mean: detect the leakage.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Extend to time and class imbalance

Preprocessing learns parameters such as means from training data. Including test values in that calculation leaks information before fitting a predictor. Keep fitted parameters explicit so the boundary can be checked even in a tiny fixture. Keep chronological splits and resampling inside training only. Review what the fixture cannot establish. Explain who owns the decision, where human approval is needed and which access or professional constraints remain. Do not describe a small educational success as production readiness, certification, legal compliance or a guarantee. Policy for this project: Fit a mean imputer on training values only, ignoring null. Transform train and test using that one mean; never refit on the test set. For learners across regions underserved or excluded by mainstream providers: verify country eligibility, language, account/identity requirements, data terms, cost, quota and connectivity before choosing a cloud service. Use only legitimate available alternatives; never bypass restrictions or invent a country/account. No service access? Complete the labelled manual/fixture route without claiming a live AI run.

Worked example

REVIEW BOUNDARY:
Fit a mean imputer on training values only, ignoring null. Transform train and test using that one mean; never refit on the test set.

ACCESS / NOT TESTED: unavailable provider, unsupported language and absent external feedback must remain explicit.

Your practical task

1. Keep chronological splits and resampling inside training only.
2. Open README.md and your evidence log from the practice pack; keep an unmodified original.
3. Record the human decision owner, limitations and any untested service access.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Extend to time and class imbalance — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• All training values missing: stop and request a stated fallback policy.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Ship a leakage-resistant pipeline contract

Preprocessing learns parameters such as means from training data. Including test values in that calculation leaks information before fitting a predictor. Keep fitted parameters explicit so the boundary can be checked even in a tiny fixture. Attach tests for missingness, refit attempts and parameter provenance. Deliver a reproducible bundle rather than a screenshot alone. Include inputs, rules, output, failure tests, a short README and residual limitations. Ask another person to follow the instructions if possible; self-review and actual external review must be labelled separately. Policy for this project: Fit a mean imputer on training values only, ignoring null. Transform train and test using that one mean; never refit on the test set.

Worked example

HANDOFF MANIFEST:
input.json — immutable source
expected.json — fixture answer key
actual.json — result actually observed
evidence-log.csv — baseline plus three changes
README.md — reproduction and access route
limitations.md — unresolved failures, rights, approvals and scope
Never bundle a credential.

Your practical task

1. Attach tests for missingness, refit attempts and parameter provenance.
2. Open README.md and your evidence log from the practice pack; keep an unmodified original.
3. Record the human decision owner, limitations and any untested service access.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Ship a leakage-resistant pipeline contract — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• A new extreme test value must not change stored training parameters.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Capstone self-review

Score each criterion 0 (missing), 1 (partial), or 2 (demonstrated). A self-review pass needs at least 8/10 and 2/2 on the critical safety/limits criterion. This is learner practice feedback, not verified assessment.

• Defined task and preserved input: Original input and the project policy are retained; the actual question is clear.
• Checked baseline: The supplied baseline reconciles field by field; unsupported live-run claims are absent.
• Three failure/change tests: All three specified changes have predictions, observed results and explanations.
• Repeatable handoff: Another person can follow the README; supporting practice evidence and unresolved limits are included.
• Safety, rights and access honesty — critical: Fit a mean imputer on training values only, ignoring null. Transform train and test using that one mean; never refit on the test set. Regional access route is legitimate; credentials and private data are absent; manual/constructed work is clearly labelled.

Review questions

Practice files

• README.md
• evidence-log.csv
• expected.json
• failure-tests.md
• hosted-lab.ipynb
• input.json
• lab_engine.py
• reference.py
• requirements.txt

References and editorial limits

Original authored practice material; deterministic fixtures build-tested. Not independently expert-reviewed or learner-validated. Time estimates unvalidated.

• Scikit-learn pipelines

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