Coding The Brains · Published 8 September 2026
Used-car price prediction: a CTB project case note
The published CTB project record describes a used-car pricing application that brought together data from four advertising platforms, data cleaning, a K-nearest-neighbors (KNN) model and a Django-backed web interface. This case note separates those documented components from evaluation details that have not been published.
The documented scope
The project addressed a practical information problem: vehicle listings were spread across sources and needed to be collected into a structured dataset before they could support price estimates. The existing project portfolio records web data collection, preparation, a feature-based price model and a full-stack website.
| Component | What the public record supports |
|---|---|
| Collection | Pricing and vehicle features from four online advertising platforms. |
| Preparation | Cleaning raw source records into structured model inputs. |
| Prediction | A KNN-based model that estimates price from vehicle features. |
| Application | A Django backend with an HTML, CSS and JavaScript frontend. |
Why the data work matters to the application
A pricing interface is only the visible end of the system. Each input needs a clear meaning, and the model's training records must use compatible units and definitions. An estimate from asking-price data should not be described as a verified sale price. These are design considerations for a similar build, not additional claims about the historical implementation.
A useful scoping discussion begins with the intended decision. A buyer comparing listings may need a different error tolerance and explanation from an operator setting inventory prices. The source coverage, update frequency and required handoff also affect the amount of work.
What we cannot substantiate from the public record
The published description does not provide dataset size, source permissions, a test split, baseline comparison, mean absolute error, segment-level errors, deployed traffic or a measured business outcome. We therefore do not attach an accuracy percentage, revenue claim, project price or time-saved figure to this case note.
It also does not establish that the original application is currently available for a public demonstration. A portfolio description establishes the documented scope; it is not an independent performance audit.
How to evaluate a similar proposal
- Confirm source access rights and the exact target to be estimated.
- Ask for missing-value, duplicate, unit and coverage checks before accepting a model score.
- Require a held-out evaluation and a simple baseline, with errors reported in units the user understands.
- Specify what happens for unusual vehicles, unsupported markets and stale data.
- Include monitoring, retraining responsibility and documentation in the delivery agreement.
Our accompanying data-quality audit walkthrough includes a runnable, synthetic example of the first checks. It is new teaching material, not the project's dataset or original implementation.