ReferencePython
snake-water-gun
A command-line variant of rock-paper-scissors, with input validation and three unit tests covering the ties, each winning matchup and a rejected input.
A beginner exercise. It sat under the name pde-problems for a year, which is where the repository started before that content was deleted and replaced; the name was corrected rather than left to misdescribe the contents, and GitHub still redirects the old one.
- Python
- Unit Testing
- Learning Artifact
ReferenceHTML
iftaar-invitation-2026
A single-file personalised invitation for a private gathering. Guest names, animation and countdown all run in the browser; nothing is submitted, collected or measured.
An event-specific design artifact, not a reusable event-management system. The host and venue details are deliberately particular to the original invitation.
- Static Site
- Client-Side Only
- Design Artifact
ReferenceHTML
ai-engineering-portfolio
The separate case-study site: architecture diagrams of the employer systems contributed to, an interactive walkthrough, and a written record of what each claim rests on, published under its own redaction review.
Documentation of systems built elsewhere, not an implementation. It states a contribution level for every component and claims no authorship of the platform it describes.
- Architecture
- Documentation
- Evidence Boundaries
ReferenceJavaScript
express
A fork of the upstream Express web framework.
No authored contribution. It appears here only because this index lists every public repository, including the ones that flatter nobody.
ReferencePython
colpali
A fork of the upstream ColPali repository, a vision-language retriever that indexes document pages as images rather than as extracted text.
No authored contribution. Listed for the same reason as the fork above: this index covers every public repository, not only the flattering ones.
- Fork
- Upstream
- Document Retrieval
ReferenceJupyter Notebook
Statistical-Learning-Transportation
Three graded problem sets from a TUM master's course: linear regression and diagnostics; then PCA, support vector machines and tree ensembles; then backpropagation, Bayesian optimisation, sequence forecasting, Markov decision processes and convolution.
University coursework, published for reference rather than as a research contribution. Sixty-six of the numbers its reports quote were recomputed afterwards from the committed artifacts and all sixty-six matched. The problem sheets it includes are the chair's material, not mine.
- Coursework
- Regression
- Ensembles
- Neural Networks
ReferencePython
CNN-Image-Classification-PyTorch
A compact image-classification experiment with a tracked configuration, learning history, per-class diagnostics, and an honestly recorded reference result.
A bounded educational baseline on a 15,000-image training subset. No checkpoint is versioned and no state-of-the-art result is claimed.
- PyTorch
- Computer Vision
- Class Diagnostics
ReferencePython
Insurance-Claims-Prediction-ML
Works through classification, probability calibration with Platt scaling or isotonic regression, cost-sensitive threshold selection, and SHAP-based attribution.
Source and notebooks only, with no versioned data, model, or evaluation report. It is not an underwriting, pricing, or claims-decision system.
- Probability Calibration
- SHAP
- Cost-Sensitive Thresholds
How it runs
src/data_pipeline.pyCleans, encodes and scales, fitting every transformer on training rows onlysrc/model_training.pyLogistic regression and random forest, with optional boostingsrc/calibration.pyPlatt scaling or isotonic regression, cross-validatedsrc/threshold_optimizer.pyChooses an operating threshold against a cost matrixsrc/explainability.pySHAP attributions over the fitted model
- Models and calibration
- scikit-learn
- Boosting
- XGBoost, LightGBM
- Attribution
- SHAP
- Class imbalance
- imbalanced-learn
- Data access
- Kaggle CLI
Publishes no metric. The repository contains source and notebooks only. It versions no data, no split, no trained model, no calibration output and no evaluation report, so there is nothing to substantiate a number with.
ReferencePython
NLP-Text-Classification-Transformers
Runs TF-IDF baselines and a fine-tuned DistilBERT against the same AG News task with the same evaluation, so the two tracks can be compared rather than asserted.
Versions source and notebooks but not data, checkpoints, or metrics, so no accuracy, F1, or model-comparison claim is published.
- DistilBERT
- Hugging Face
- TF-IDF
- Benchmarking
How it runs
src/data_loader.pyFetches AG News, and stops rather than silently using the offline fixturesrc/model.pyThe TF-IDF baselines and the DistilBERT configurationsrc/train.pyFits a baseline, or fine-tunes through the Hugging Face Trainersrc/evaluate.pyClassification metrics, confusion matrices and optional latencysrc/inference.pyRuns either track from a locally saved artifact
- Transformer
- Transformers, Tokenizers
- Training loop
- Accelerate
- Corpus access
- Datasets
- Baselines and metrics
- scikit-learn
Publishes no metric. Data, trained models, checkpoints, metrics, plots and run metadata are all untracked, so the repository declines to state how the two tracks compared.