Python Training and Code Review
Sessions and code review while you build your experiments, so the code is yours and you can defend it.
Machine learning and deep learning are now used in PhD theses across engineering, computer science, medicine, agriculture and management. The implementation problems we see are nearly always the same few. In one thesis the model shows 99% accuracy only because images of the same patient went into both the training set and the test set. Another one, the hyperparameters got tuned by peeping at test score after every change. Baselines also we find copied from another paper’s table, never run even once on the scholar’s data. And many times one lucky run is reported, with no idea how much the result moves when the seed changes. None of this shows in the results table till a reviewer asks for the code, or the examiner asks how the split was made.
We help scholars in Python so that their experiment does not fall apart the day somebody checks the code. For that the data pipeline has to be clean, and the train, validation and test splits have to be made properly. Baselines should run under same conditions as your model. Runs get repeated with fixed seeds so the evaluation can show the spread next to the best number. We work with the usual stack of NumPy, pandas, scikit-learn, PyTorch, TensorFlow/Keras and the libraries around them.
By the end your code is documented and running, with an environment file, so it installs the same way on another machine. You write it, and we review it and explain where it is going wrong. The research idea stays yours, and so does the interpretation of results. What we do is check that the experiment is sound and that you understand it well enough to defend it.
Quick answers
- What does Python training and code review include?
You get one-to-one sessions while you build a data pipeline without leakage, with a saved split. You run the baselines on your exact data and code your proposed model with configuration files, and we review each part. Experiments are repeated over several seeds. At the end we help you put together a reproducibility package (README, pinned environment, one command per result), and you do a walk-through with us.
- How long does an ML project take with your guidance?
If the data is already labelled and you have GPU access, the pipeline takes 1–2 weeks and the baselines another 1–2 weeks. Your model needs 1–3 weeks, and the experiments 1–4 weeks. Usually the training time per run is what decides the upper end.
- Can you get my model to beat the baseline, or reach 98% accuracy?
The data decides the number. We check that the baselines are kept at full strength, the test set is used only once at the end, and every run is reported. If a score looks suspiciously high, first we check it for leakage.
- I already have code. Can you just review it?
Yes, we do that. We go through your own code for leakage, evaluation errors and reproducibility. It usually takes 3–7 working days, depending on the size of the code base.
- How do we begin?
Start with a free consultation. Describe your data and share your proposed method and any existing code. You then get a fixed written quote for the stages you choose and their revision rounds. If cloud GPU is needed, that cost is agreed up front as a separate line, at actual cost.
Scope
What’s included
You can take each stage on its own. Scholars with working code are mostly asking for only the leakage and evaluation review before writing up.
Data pipeline and splitting
You load, clean and preprocess your data, and we check that every step is fitted on the training data only. Splits are made at the correct level (patient, subject, session, time period) to stop leakage. You keep one fixed, saved split which everyone can reuse.
Baselines
You run the baselines that suit your problem, from simple models up to the strongest recent methods in your area. We help you choose them, and if needed we pair-program the first one with you. They run on your exact data and splits, with the same tuning budget as the proposed model.
Code review of your proposed model
You code the architecture or method you have designed, in PyTorch, TensorFlow/Keras or scikit-learn, and we review it. Settings go into configuration files and are not hard-coded, so every experiment can be traced.
Experiments and evaluation
Hyperparameter search happens on the validation data only, and runs are repeated over several seeds. Metrics are picked to suit the problem, because plain accuracy is not enough on imbalanced data. We also help you plan the ablation studies and the appropriate statistical comparison.
Reproducibility package
We help you set up a clean repository with a README, requirements.txt or environment.yml and pinned library versions. Seeds are recorded, model weights are saved where practical, and each result comes from one command.
Code review and walk-through
We review your code for leakage, evaluation errors and reproducibility. Then you walk us through it in a recorded session, so you are ready for the examiner’s questions.
Quality
How quality is verified
These are the checks every piece of work goes through before it reaches you.
Leakage audit
We check that preprocessing, feature selection, oversampling and normalisation are fitted on the training data only. Also, no subject, patient or near-duplicate sample should sit on both sides of a split.
Results reported across seeds
Each figure you report should come from several runs with recorded seeds. We check that it is given as a mean with standard deviation or confidence interval, and not as one best run.
Fresh-environment test
At the end, we install your repository from its environment file on a clean machine and run it end to end, to confirm it reproduces the reported results.
Baselines on equal terms
We check that the baselines get the same data, splits, preprocessing and tuning budget as the proposed model. The configuration of each baseline should be saved along with the results.
Documented, readable code
We look for docstrings, and type hints wherever they help. A README should explain the folder structure, and the notebooks should run cleanly from top to bottom.
Time
Realistic timelines
For deep learning, the timeline depends a lot on data size and compute. These ranges assume the data set is already collected and labelled, and that a suitable GPU is available.
| Stage | Typical time | What it depends on |
|---|---|---|
| Data pipeline and splitting | 1–2 weeks | Data size, format and how much cleaning is needed |
| Baselines | 1–2 weeks | Number of baselines and whether public code exists |
| Code review of your proposed model | 1–3 weeks | Novelty and complexity of the method |
| Experiments and evaluation | 1–4 weeks | Training time per run, number of seeds and ablations |
| Reproducibility package | 3–5 working days | State of the code base |
| Code review and walk-through | 3–7 working days | Size of the code base |
Method
Data leakage: the error behind many too-good results
Leakage is when some information from the test data reaches the model during training. The numbers go up and nobody notices, so experienced reviewers are looking for leakage in the first reading itself.
Common ways it happens
- Preprocessing before splitting: scaling, imputation or feature selection is done on the whole data set. The training step has then already “seen” the test distribution.
- Oversampling before splitting: SMOTE or augmentation is applied first, and synthetic copies of test samples land up in training.
- Splitting at the wrong level: the same person has several images, recordings or visits, and they fall on both sides of the split.
- Time ignored: time-series data is split randomly, so the model gets trained on the future and tested on the past.
- Tuning on the test set: when you take any decision by looking at test performance, the test set becomes a second validation set.
In every case the fix comes from one principle. Decide the split first. Fit everything on the training data only, tune on the validation data, and use the test set only once. Pipelines in scikit-learn helps a lot here, and so do data loaders that apply transforms per split.
What “reproducible” means in practice
Another person should be able to clone the repository, make the environment from the file given, and generate your tables and figures again. That person may be your guide, a reviewer, or you yourself one year later. With GPU there is one more issue, some operations are non-deterministic and bit-for-bit output is not always possible. This is one more reason to report averages over seeds and not a single run.
If your work is simulation in MATLAB and not in Python, see MATLAB and simulation guidance. If you need statistical comparison of models, or analysis of survey data next to the ML work, see statistical analysis.
Pricing
How pricing works
We give the quote only after seeing your data description, your proposed method and any existing code. A scikit-learn study on tabular data is a very different amount of work from a multi-modal deep learning model. The quote is fixed for the stages you choose, and we agree on it in writing before the work starts.
Sometimes a project needs more compute than you or your university has. In that case we discuss about the cloud GPU costs up front, and they are paid at actual cost, shown as a separate line from our fee.
What the quote depends on
- Size and state of the data set
- Complexity of the proposed model
- Number of baselines, seeds and ablation studies
- Compute required for training
- Which stages you need
Every quote includes
- A written scope listing exactly what will be delivered
- A fixed price agreed before any payment
- Payment in stages, tied to delivery
- Revision rounds stated in the scope
Process
How it works
Free consultation
A call or message with a specialist in your subject. We review your material and say plainly whether we are the right fit.
Written scope and quote
You get a written scope, a timeline and a fixed price. Work and payment begin once you agree to it.
Staged delivery
Work arrives in stages you review, each with a similarity report where text is involved. You pay stage by stage.
Revisions and hand-over
Revision rounds are included. At the end you get the final files, and an explanation of anything you’ll need to defend.
Keep reading
Guides and related services
MATLAB and Simulation Guidance
Sessions and code review while you build your model, so you can run and explain every line of it.
Research Methodology Consulting
Design, sampling, instruments and analysis plan checked before you collect data, while changes are still easy.
IEEE citation and formatting guide
Numbered citations, reference examples for every common source type, and figure and equation conventions.
FAQ
Frequently asked questions
Do you write the code for me?
No. You write the code, and we teach, review and pair-program with you while you write it. The research idea is your own, and examiners do ask about the code. See our ethics policy.
Can you get my model to 98% accuracy?
Your data decides that. We help you set the experiment up properly. Whatever genuine improvement is possible after that, we help you try it. On a hard problem, a very high score can be a sign of leakage, so that is the first thing we check.
My proposed model doesn’t beat the baseline. Can you fix that?
We can look for bugs and unfair comparisons, and for reasonable improvements to your method. Weakening the baselines so your model looks good, that we won’t do. Tuning happens on validation data only and every run is reported. A negative or mixed result is also a contribution if it is analysed well and ablations show the reason.
Can you use a public data set instead of collecting my own?
Many times yes, if it fits your research question and its licence allows research use. Whether it is acceptable for your thesis is for your supervisor to say. We read the licence terms first. Duplicate samples or wrong labels, those known problems we check also.
Should I use Jupyter notebooks or scripts?
Either one, as you like. For the final package we usually suggest scripts for the pipeline and notebooks for exploration and figures, because scripts run top to bottom reliably. Either way we run your whole project from a clean start at the end.
What if the examiner asks how I split the data or chose hyperparameters?
You will have the split file, the configuration of every run, and the validation results behind each choice. We go through all of these with you, so your answer is simple and true.
Is my data and code kept confidential?
Yes. Only the people working on your project see your data, code and ideas. They are used for your project only, and deleted on request after the work ends. With sensitive data like patient records, we work as per your ethics approval conditions and can use de-identified data. If you want an NDA, we will sign one.
Talk it through with a specialist for free
Send us where you are and what you need. We’ll reply within one working day with clear advice, a written scope and a fixed quote. No obligation.
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