Shodhyantri Engineering Services
Research · Analysis · Editing · Publication Support
Guidance on ML and deep learning experiment design, data splits, baselines, metrics, ablation studies and reproducible code for theses and research papers.
Tell us about your machine learning research support need. We reply within one working day.
Machine learning research support is for PhD and M.Tech scholars whose thesis or paper depends on models and experiments. Reviewers of ML work look for specific things: a clear problem formulation, a fair data split, strong baselines, appropriate metrics, statistical comparison and code that someone else could run. Many good ideas are rejected or sent back because one of these pieces is missing.
We help you put those pieces in place. You own the research idea and the contribution. We review your experiment design, help set up proper train, validation and test splits, choose baselines and metrics, guide hyperparameter tuning, design ablation studies, review your Python code for leakage and errors, and help you present results in tables and plots that reviewers trust.
An ML research study needs more than a model with high accuracy. It needs a well-defined task, a described dataset with known class balance, a split that prevents information from the test set leaking into training, and comparison against established baselines trained under the same conditions. Metrics must suit the task: accuracy can mislead on imbalanced data, where precision, recall, F1, AUC or balanced accuracy tell a truer story.
Strong papers also show why their method works. Ablation studies remove components one at a time. Repeated runs with different seeds or k-fold cross-validation give mean and spread rather than a single lucky number. Statistical tests, such as paired tests across folds, support claims of improvement.
We look for common leakage sources such as scaling before splitting, duplicate images across sets and patient-level overlap in medical datasets.
We help you choose and run baselines with the same data, preprocessing and tuning effort, so improvements reflect your method rather than uneven comparison.
For imbalanced classes, regression, segmentation or detection tasks, we help select metrics reviewers expect, such as F1, AUC, RMSE, Dice or mAP.
We work with Python, scikit-learn, TensorFlow, Keras, PyTorch, XGBoost and MATLAB, and with Google Colab or local GPU setups.
Fixed seeds, saved configurations, environment files and clear folder structure make your results repeatable for examiners and journal reviewers.
We help you organise experiments so the same results support both your thesis chapters and the papers you plan to submit.
Ten stages, each with a clear output. Nothing moves to the next stage until you have reviewed the last one.
— You review every stage first, then decide —
The problem statement, input, output and evaluation target are checked to be precise and measurable.
Every preprocessing step is reviewed to ensure it is fitted only on training data within each split.
Baselines are checked for equal data, tuning budget and preprocessing compared with the proposed method.
Chosen metrics are compared with class balance and task type so reported performance is not misleading.
Variation across seeds or folds is reviewed so claimed improvements are larger than random fluctuation.
Code, configurations and environment details are reviewed so results can be regenerated from scratch.
Every stage is shared with notes explaining what was changed and why. You and your supervisor check it, and the next stage starts only after your go-ahead.
| Common problem | How Shodhyantri helps |
|---|---|
| Scaling or oversampling the full dataset before splitting into training and test sets. | Fit scalers, encoders and SMOTE only on training data inside each fold, then apply the fitted transform to validation and test data. |
| Reporting accuracy alone on a heavily imbalanced dataset where most samples share one class. | Report precision, recall, F1, AUC or balanced accuracy, along with a confusion matrix showing performance on minority classes. |
| Comparing a tuned proposed model against untuned baselines taken from older papers. | Retrain baselines on the same split with comparable tuning effort, or clearly state when published numbers are used. |
| Tuning hyperparameters repeatedly on the test set until results look strong enough. | Tune only on validation data or inner folds, and evaluate on the untouched test set once for final reporting. |
| Claiming improvement from one run that beat a baseline by a tiny margin. | Repeat runs with several seeds or folds, report mean and spread, and use a statistical test before claiming improvement. |
| Using patient images or user records that appear in both training and test sets. | Split at the patient, user or subject level so no individual contributes data to more than one partition. |
Decide your test set before experimenting and do not look at its results until final evaluation. This protects the honesty of your claims.
A logistic regression or random forest baseline shows whether complex deep models actually add value for your problem.
Record settings, seeds, metrics and dates for each run, in a spreadsheet or a tracking tool, so results are never lost or confused.
scikit-learn pipelines keep preprocessing inside cross-validation and greatly reduce the risk of accidental data leakage.
State hardware, training time and framework versions. Reviewers increasingly ask for these to judge practicality and reproducibility.
Explainability methods like SHAP or Grad-CAM help reviewers trust your model, especially in medical and financial applications.
If possible, evaluate on a second dataset. Generalisation to new data is far more convincing than performance on one benchmark.
A clean, documented repository supports your paper and helps examiners, and many journals now encourage code availability statements.
Your work is handled by people who read your field's literature, not by a single generalist.
A PhD-qualified specialist from your field who checks the research logic, gap and contribution.
Handles statistics, models and qualitative coding, and documents every analytical decision.
Improves structure, flow, clarity and academic tone without changing your meaning.
Builds and validates MATLAB, Python, ANSYS or COMSOL work where your study needs it.
Checks grammar, consistency, numbering, citations and the small errors examiners notice.
Applies your university or journal template, reference style and front matter exactly.
We support your coding rather than replacing it. We review your code, suggest corrections, explain library functions, help set up pipelines and run checks. You should be able to explain every part of your code at viva, so we keep you involved throughout.
We mainly work in Python with scikit-learn, XGBoost, TensorFlow, Keras and PyTorch, and also in MATLAB. We can work in Google Colab, Jupyter notebooks or local environments, and we help you document versions so results can be reproduced.
Look at recent papers on the same task and dataset, and include both a classical method and recent strong models. Baselines should be trained on the same split with similar effort. We help you select a defensible set based on your literature review.
Not always, but unusually high accuracy should be checked. Common causes are data leakage, duplicates across splits, an easy or imbalanced dataset, or a test set used during tuning. We audit the pipeline so you can trust and defend your numbers.
No. Performance depends on your data, idea and compute, and no honest adviser can promise improvement. We help ensure experiments are fair and correctly reported. A well-run study with modest gains, or useful negative results, can still make a solid contribution.
Yes. We support work on X-ray, CT, MRI, histopathology and similar data, with attention to patient-level splits, class imbalance, augmentation, segmentation metrics such as Dice and explainability methods like Grad-CAM that clinicians and reviewers expect.
Classical ML runs on a normal laptop. Deep learning usually needs a GPU, which Google Colab or a university cluster can provide. We help you plan experiments within your compute limits, for example using transfer learning instead of training from scratch.
We provide research guidance, statistical analysis, editing, formatting and training support. All research content remains the scholar's own work, in line with UGC and university academic integrity norms. We do not guarantee publication, acceptance, indexing or academic outcomes: journals decide through peer review and universities award degrees.
Share your topic, draft or data. We will tell you honestly what it needs, and what it does not, before you pay anything.