Overview
Researchers use machine learning for three broad tasks: classification (predicting a category, such as dropout or diagnosis), regression-type prediction (predicting a quantity, such as length of stay or yield), and unsupervised learning such as clustering (finding groups, such as learner profiles or customer segments). Each has its own pitfalls, and many published studies overstate performance because of data leakage, weak validation or cherry-picked metrics.
We start with the research question: is the goal prediction, explanation, or discovery? That choice drives everything else — the baseline to beat, how data are split, which metrics matter and how results are interpreted. We then build a reproducible pipeline in Python (scikit-learn, XGBoost, LightGBM) or R (tidymodels, caret, ranger) with preprocessing inside cross-validation, sensible hyperparameter tuning and a held-out test set that is touched once.
You receive annotated code, performance tables with uncertainty, interpretation outputs and a plain-language explanation of what the model can and cannot tell you, plus guidance on reporting against checklists your field may expect.
Who it’s for and the problems it solves
This is for you if you are…
- Master's and doctoral students adding a machine learning component to a thesis in health, education, business, engineering or social science
- Researchers comparing ML models with a conventional regression and needing a fair comparison
- Authors responding to reviewers who questioned validation, leakage or interpretability
- Applied researchers who want to learn a sound ML workflow rather than copy a tutorial
Common problems we help with
- Your model reports 98% accuracy and you suspect something is wrong
- You scaled, imputed or oversampled before splitting the data
- Your classes are imbalanced and accuracy looks good while the minority class is missed
- You need to explain which variables drive predictions, not only how well the model predicts
- You ran k-means but cannot justify the number of clusters or describe them meaningfully
What’s included — and what isn’t
Included
- Problem framing: prediction versus explanation, outcome definition, unit of analysis and baseline model
- Data audit for leakage risks: target leakage, time leakage, duplicate or grouped records across splits
- Train/validation/test design, k-fold, stratified, grouped or time-series cross-validation, and nested cross-validation for tuning
- Preprocessing pipelines (imputation, encoding, scaling, resampling) fitted within training folds only
- Model fitting and tuning: regularized regression, decision trees, random forests, gradient boosting, support vector machines, neural networks where justified
- Evaluation with metrics suited to the task: ROC-AUC, PR-AUC, F1, balanced accuracy, calibration, RMSE, MAE
- Interpretability: permutation importance, SHAP values, partial dependence and accumulated local effects plots
- Clustering: k-means, hierarchical, DBSCAN, Gaussian mixture or latent profile alternatives, with validation (silhouette, stability) and profile description
- Reproducible code with fixed seeds, environment files and a reporting checklist
Not included
- Tuning on the test set or reporting the best of many runs without disclosure
- Presenting predictive importance (such as SHAP values) as causal effects
- Writing your thesis chapter or paper; we explain the outputs so you can write about them
- Building production software or deploying models in live systems
What you receive
- Modeling plan
- Research framing, outcome and features, split strategy, candidate models, metrics and a pre-stated primary comparison.
- Reproducible pipeline
- Jupyter notebook or R Markdown/Quarto document with annotated code, fixed seeds and a requirements or renv file.
- Results tables and figures
- Cross-validated and test-set performance with confidence intervals, confusion matrices, ROC/PR curves and calibration plots.
- Interpretation outputs
- SHAP summary and dependence plots or permutation importance, with notes on correlated features and limits of interpretation.
- Walkthrough
- A session explaining each step and result so you can describe and defend the analysis.
How it works
Frame the question
We agree whether the aim is prediction, explanation or discovery, and define outcome, features and a baseline.
Audit and split
We check the data for leakage and grouping, then fix the validation design before any modeling.
Model and tune
We fit candidate models within cross-validation and select using pre-agreed criteria.
Test, interpret and report
We evaluate once on the held-out test set, produce interpretation outputs and walk you through the results.
Methods and tools
- Supervised learning
- Logistic and penalized regression (lasso, ridge, elastic net), random forests, gradient boosting (XGBoost, LightGBM), SVMs and neural networks, compared against a simple baseline.
- Unsupervised learning
- k-means, hierarchical clustering, DBSCAN and Gaussian mixture models, with dimensionality reduction (PCA, UMAP) for exploration.
- Validation
- Stratified, grouped and time-series cross-validation; nested cross-validation for tuning; bootstrapped confidence intervals for metrics.
- Imbalanced data
- Class weights, threshold tuning and resampling such as SMOTE applied only within training folds, with PR-AUC and balanced metrics.
- Interpretability
- SHAP, permutation importance, partial dependence and ALE plots, with caution about correlated predictors.
- Software
- Python (pandas, scikit-learn, XGBoost, SHAP) and R (tidymodels, caret, ranger, DALEX).
Data leakage: the most common reason ML results fail
Leakage happens when information from outside the training data slips into model building, making performance look far better than it will be on new cases. It is easy to cause and hard to see in the output.
- Scaling, imputing or selecting features on the full dataset before splitting
- Oversampling (for example, SMOTE) before cross-validation, so synthetic copies land in test folds
- Repeated measurements from the same person or school appearing in both training and test sets
- Predictors recorded after the outcome occurred, such as a discharge code used to predict admission
- Random splits of time-ordered data where the model effectively sees the future
Interpreting models honestly
SHAP values and permutation importance show how a fitted model uses its inputs. They are useful for transparency, but they describe the model, not the world: a feature can be important because it is correlated with a true cause, and importance can shift between correlated features. We help you word interpretation accordingly and, where your research question is causal, point you towards methods designed for causal inference.
Clustering that means something
Unsupervised results depend heavily on scaling, distance measures and the number of clusters. We compare solutions using internal indices and stability checks, then profile each cluster on the original variables so the groups can be described in substantive terms, and we are clear when the data do not support distinct clusters.
What the work can look like
Illustrative example — fictional data
Illustrative results summary for a dropout prediction model
A fictional education researcher wanted to predict first-year university dropout from enrollment and first-semester data for 8,000 students. All figures are invented to show reporting style.
Leakage fix
An early model reached an AUC of 0.97 because it included 'final credits earned', recorded after dropout. With that variable removed and only first-semester predictors kept, performance fell to a realistic level.
Performance
Using five-fold stratified cross-validation for tuning and a 20% held-out test set, gradient boosting achieved ROC-AUC 0.81 (95% CI 0.78 to 0.84) versus 0.78 for logistic regression. PR-AUC was 0.46 against a baseline of 0.14, the dropout rate.
Interpretation
SHAP values showed first-semester credits and attendance as the largest contributors. The write-up describes these as predictive markers, not causes of dropout.
Illustrative example with invented data and results; it does not describe a real client or institution.
What you’ll need to provide
- Research questions and how the model's output will be used or interpreted
- The dataset (de-identified) with a data dictionary, including how and when each variable was collected
- Any existing code, prior results or reviewer comments
- Preferred language (Python or R) and computing constraints
- Journal or program reporting requirements, if any
You don’t need everything ready to ask for a quote — tell us what you have and we’ll say what’s missing.
What affects price and turnaround
Price depends on
- Dataset size, number of features and data cleaning needs
- Number of models and outcomes to compare
- Complexity of validation (grouped, temporal or nested designs)
- Interpretation and reporting depth required
- Delivered analysis versus guided sessions where you write the code
Turnaround depends on
- Clarity of the research question and outcome definition
- Computation time for tuning larger models
- Data access and de-identification steps
Every project is quoted individually. How we price our work.
Questions people ask before starting
Should I use machine learning or regression?
If your aim is to estimate and explain the effect of specific variables, regression is often clearer. If your aim is to predict accurately, especially with many predictors and nonlinear relationships, ML may help. We often run both and compare them fairly.
My dataset is small. Can I still use ML?
Sometimes, with simple models, strong regularization and careful cross-validation. With very small samples, complex models overfit, and we will tell you if a simpler approach is more defensible.
Python or R?
Either. We follow your preference, your supervisor's expectations or the language you want to learn. Both have mature ML ecosystems.
Will you explain the code so I can defend it?
Yes. Code is annotated, and a walkthrough is part of the service. You should be able to explain every step in a viva or to a reviewer.
Is my data safe?
Send de-identified data only. We use it solely for your project and can sign a confidentiality agreement. For sensitive health or personal data, check your data-sharing permissions first.