# data · data.sql
Machine Learning Models
Models trained, evaluated and written up to the standard your course expects.
ML coursework is marked on justification as much as accuracy — a high score with no explanation of the metric choice or an undetected leak between train and test sets reads as luck, not competence. We build the pipeline properly, document every choice, and write the method section so it can survive being questioned.
What you get
- Model matched to the problem type your brief specifies, not the easiest option
- Train/validation/test split done properly, no data leakage
- Evaluation metrics chosen to fit the problem, and explained
- Hyperparameter choices documented, not left as defaults
- Confusion matrix or equivalent included for classification tasks
- A written report section covering method and limitations
What to send first
- The dataset (or a sample of it) and where it came from
- The questions the analysis has to answer, or the model's target
- Required tools — SQL dialect, Python libraries, R, Excel version
- The report format: notebook, PDF write-up, dashboard or slides
How it runs
- 01
Send the spec
The brief, the rubric, any starter code and the versions your marker uses. A photo of the PDF is fine to begin with.
- 02
Scope and quote in writing
We read it properly, ask what is unclear, and confirm a fixed price and delivery date. A small advance confirms the order.
- 03
Build against the brief
Work is tracked task by task against the spec. Longer projects are split into milestones you can review as they land.
- 04
Tested handover
A repo that installs from a README, run against your test cases, with the write-up if your rubric asks for one.
- 05
Fixes and walkthrough
One round of fixes if a marker flags something, and a walkthrough of how it works so you can explain it.
Machine Learning Models: common questions
The notebook or scripts that produced every number, so the analysis can be re-run end to end. Results pasted into a report without the code behind them are hard to defend.
No. Data is used only for your task, never shared or reused, and deleted on request once the work is signed off.
It depends on the scope of the brief, the stack and the deadline. Send the spec on WhatsApp and you get a fixed price and delivery date in writing before any work starts — no hourly meter.
Turnaround depends on scope and is agreed with the quote. Tell us the real deadline, including the timezone, and we will say honestly whether it is workable before you commit.
// related
Often paired with machine learning models
Database Design
Normalised schema design from an ER diagram through to a working database.
data.sqlSQL Query Work
Query sets solved and explained line by line, from joins to window functions.
data.sqlData Pipelines
ETL pipelines built to ingest, clean and load a dataset the way your brief specifies.
./send-brief
Send your dataset.
Paste the machine learning models brief into WhatsApp — the message already names the service. Scope and price come back in writing.