Python Implementation Support
Reproducible experiments with fair baselines — documented code you can run, change and defend.
Machine learning and deep learning now appear in PhD theses across engineering, computer science, medicine, agriculture and management. The implementation problems we see are remarkably consistent. A model that reaches 99% accuracy because images from the same patient ended up in both training and test sets. Hyperparameters tuned by checking the test score after every change. A baseline copied from another paper’s table rather than run on the same data. A single lucky run reported, with no idea how much results vary between seeds. None of these is obvious from the results table until a reviewer asks for the code, or an examiner asks how the split was made.
We help scholars build experiments in Python that hold up: clean data pipelines, proper train, validation and test splits, fair baselines run under the same conditions, repeated runs with fixed seeds, and evaluation that reports variation as well as the best number. We work with the usual stack: NumPy, pandas, scikit-learn, PyTorch, TensorFlow/Keras and the libraries around them.
The code comes to you documented and runnable, with an environment file so it installs the same way on another machine. The research idea and the interpretation of results remain yours; our job is to make sure the experiment is sound and that you understand it well enough to defend it.
Scope
What’s included
Each stage can be taken separately. Scholars with working code often want only the leakage and evaluation review before writing up.
Data pipeline and splitting
Loading, cleaning and preprocessing your data with each step fitted on training data only; splits made at the right level (patient, subject, session, time period) to prevent leakage; and a fixed, saved split everyone can reuse.
Baselines
Implementing or running the appropriate baselines, from simple models to the strongest recent methods in your area, on your exact data and splits, tuned with the same budget as your own model.
Implementing your proposed model
Coding the architecture or method you have designed, in PyTorch, TensorFlow/Keras or scikit-learn, with configuration files instead of hard-coded settings so experiments are traceable.
Experiments and evaluation
Hyperparameter search on validation data only, repeated runs over several seeds, metrics that suit the problem (not just accuracy on imbalanced data), ablation studies and appropriate statistical comparison.
Reproducibility package
A clean repository with a README, requirements.txt or environment.yml, pinned library versions, recorded seeds, saved model weights where practical, and one command per result.
Code review and walk-through
A review of code you wrote yourself for leakage, evaluation errors and reproducibility, or a recorded walk-through of code we worked on together.
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 training data only, and that no subject, patient or near-duplicate sample appears on both sides of a split.
Results reported across seeds
Every reported figure comes from several runs with recorded seeds, shown as a mean with standard deviation or confidence interval rather than a single best run.
Fresh-environment test
Before hand-over, the repository is installed from its environment file on a clean machine and run end to end, to confirm it reproduces the reported results.
Baselines on equal terms
Baselines use the same data, splits, preprocessing and tuning budget as the proposed model, and the configuration for each is saved alongside the results.
Documented, readable code
Docstrings, type hints where useful, a README explaining the folder structure, and notebooks that run cleanly from top to bottom.
Time
Realistic timelines
Deep learning timelines depend heavily on data size and compute. These ranges assume a data set that is already collected and labelled, and access to a suitable GPU.
| 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 |
| Implementing 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 means information from the test data reaches the model during training. It inflates results silently, and it is one of the first things an experienced reviewer checks.
Common ways it happens
- Preprocessing before splitting: scaling, imputing or selecting features on the whole data set, so the training step has already “seen” the test distribution.
- Oversampling before splitting: applying SMOTE or augmentation first, so synthetic copies of test samples end up in training.
- Splitting at the wrong level: several images, recordings or visits from the same person on both sides of the split.
- Time ignored: random splits of time-series data, so the model is trained on the future and tested on the past.
- Tuning on the test set: any decision made by looking at test performance turns the test set into a second validation set.
The fix in each case is the same principle: decide the split first, fit everything on training data only, tune on validation data, and touch the test set once. Pipelines in scikit-learn, and data loaders that apply transforms per split, make this much easier to get right.
What “reproducible” means in practice
Someone else (your supervisor, a reviewer, or you in a year’s time) should be able to clone the repository, create the environment from the file provided, and regenerate your tables and figures. Full bit-for-bit reproducibility on GPUs isn’t always possible, because some operations are non-deterministic; that is one more reason to report averages over seeds rather than a single run.
If your work is simulation-based in MATLAB rather than Python, see MATLAB and simulation support. For statistical comparison of models or analysis of survey data alongside the ML work, see statistical analysis.
Pricing
How pricing works
We quote after seeing your data description, your proposed method and any existing code, because a scikit-learn study on tabular data and a multi-modal deep learning model are very different amounts of work. The quote is fixed for the stages you choose and agreed in writing before work begins.
Cloud GPU costs, if your project needs more compute than you or your institution has, are discussed up front and 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 Support
Models that reproduce your base paper first, then extend it — with code you can run and explain.
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
Will you write the code and I submit it as mine?
We implement with you, and the work stays yours. The research idea is yours, and you must understand the code well enough to explain and modify it, because examiners ask. Every project includes a walk-through, and the code is documented so you can work with it on your own.
Can you get my model to 98% accuracy?
The data decide the number. We build a sound experiment and try genuine improvements. A very high score on a hard problem can point to leakage, so we check for that first.
My proposed model doesn’t beat the baseline. Can you fix that?
We can check for bugs, unfair comparisons and reasonable improvements to your method. Baselines stay at full strength, tuning uses validation data only, and every run is reported. A well-analysed negative or mixed result, with ablations showing why, is still a contribution.
Can you use a public data set instead of collecting my own?
Often, yes, if it fits your research question and its licence allows research use. Whether it is acceptable for your thesis is a question for your supervisor. We check licence terms and known issues such as duplicates or label errors before using one.
Do you deliver Jupyter notebooks or scripts?
Whichever suits you. For the final package we usually recommend scripts for the pipeline, with notebooks for exploration and figures, because scripts run top to bottom reliably. Either way, we test that everything runs from a clean start.
What if the examiner asks how I split the data or chose hyperparameters?
You will have the split file, the configuration for every run and the validation results that informed each choice. We go through these with you so the answer is simple and true.
Is my data and code kept confidential?
Yes. Your data, code and ideas are seen only by the people working on them, used only for your project, and deleted on request after hand-over. For sensitive data such as patient records, we follow your ethics approval conditions and can work on de-identified data. We will sign an NDA if you want 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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