Coding The Brains · Published 8 September 2026 · Updated 7 October 2026

Data and software engineering resources

Start a data project by checking the source data, comparing a candidate with a realistic baseline, and documenting the decision the output will support. These practical guides include runnable examples, a blank audit handoff and a project case note. Choose the resource that answers your current question; a model is not automatically the next step.

Where should you start?

• You have data, but are not sure it is usable: start with the data-quality audit. Establish the target, record meaning, permissions and information available at prediction time before paying for model development.
• You already have predictions or a supplier's evaluation report: use the baseline and segment-error walkthrough. Compare alternatives on the same held-out rows, including groups where a wrong result is costly.
• You want to understand a comparable application's scope: read the used-car pricing project case note. It distinguishes documented implementation work from performance and business outcomes that have not been published.

Data-quality audit before a predictive model

Work through missing values, duplicates, source definitions and training/test split risks. Two small synthetic examples demonstrate record checks and information leakage; they are not audits of a client's data. The handoff sheet helps record evidence, unresolved questions and the person responsible for each check.

• Download the standalone split-audit demonstration to exercise shared entities, feature availability and invalid timestamps.
• Download the blank 16-check data-audit handoff (CSV) to document the work on your actual project.

Predictive model evaluation: baselines and segment errors

A four-row arithmetic example shows why a better average error can hide a worse result for one input group. It compares hand-authored candidate predictions with a training-set median, reporting MAE, RMSE and sample counts. It does not train a model or establish statistical confidence.

Download the standalone regression-baseline demonstration. Read the script before running it with Node.js; it has no packages, network requests or file writes. Use the accompanying guide to identify the evaluation records and release criteria needed for a real handoff.

Used-car price prediction: documented project scope

Review the published record of data collection, cleaning, KNN modeling and Django application delivery. The case note does not claim an independently verified prediction score, revenue gain or deployment outcome. It provides questions to ask when scoping or reviewing a similar application.

What should you bring to a project review?

Prepare the business decision, a non-sensitive data schema, source dates, permitted inputs, the current manual or software baseline, and the person responsible for using the result. List known exceptions and how they are handled today. An unresolved data or permissions issue should remain an open question, not be recorded as a passed check.

Our Data Science Solutions service connects data preparation, analysis, model evaluation and application delivery. Discuss the workflow and available evidence; do not upload customer records or credentials into a public form.

For agent implementation budgets, approval workflows and automation buying decisions, visit CTB Agents resources. Those guides cover a different decision from predictive-model evaluation.