A portfolio repository of university coursework projects covering data analytics, machine learning, deep learning, and natural language processing. The projects include both individual and group work focused on practical business, healthcare, computer vision, and text-analytics problems.
This repository brings together seven academic projects that demonstrate end-to-end workflows such as data cleaning, exploratory data analysis, feature engineering, regression, classification, clustering, deep learning, NLP, calibration, and business interpretation. The work spans tabular, image, and text data, with implementations mainly in Python using Jupyter Notebook.
| Project | Area | Summary |
|---|---|---|
da-airline-passenger-clustering |
Data Analytics / Clustering | Analyses airline passenger satisfaction data and applies K-means clustering to identify meaningful customer segments and service-improvement opportunities. |
da-bikes-demand-prediction |
Data Analytics / Regression | Uses linear regression to study how weather, seasonality, and calendar effects influence shared-bike rental demand. |
dl-covid-ct-classifier |
Deep Learning / Medical Imaging | Classifies chest CT images into COVID-19 and Non-COVID classes using CNN and HOG-based baselines, with deployment-oriented threshold analysis. |
dl-imdb-sentiment-classifier |
Deep Learning / NLP | Builds a Wide & Deep neural network in PyTorch for binary sentiment classification on IMDB movie reviews. |
ml-course-rating-predictor |
Machine Learning / Classification | Predicts whether SkillsFuture healthcare courses are likely to receive high learner ratings using interpretable ML models. |
nlp-annual-report-brochure |
NLP / Document Comparison | Compares an annual report and marketing brochure using semantic similarity, topic modelling, NER, and sentiment analysis. |
nlp-sroi-reports |
NLP / PDF Text Analytics | Analyses SROI reports to extract and compare impact-adjustment factors such as deadweight, displacement, attribution, and drop-off. |
- Data analytics and statistics: EDA, correlation analysis, regression, and business insight generation.
- Machine learning: Logistic Regression, Decision Trees, K-means clustering, and interpretable feature-driven modelling.
- Deep learning: PyTorch pipelines for image and text classification, including tuning, calibration, and deployment-oriented analysis.
- Natural language processing: Text preprocessing, embeddings, TF-IDF, NER, topic modelling, sentiment analysis, and document comparison.
- Reproducible notebook workflows: Most projects are structured around Jupyter notebooks with supporting datasets, documentation, and project-specific instructions.
Across the repository, the main tools and libraries include:
- Python
- Jupyter Notebook
- pandas, NumPy
- matplotlib, seaborn
- scikit-learn
- statsmodels
- PyTorch, torchvision
- OpenCV, scikit-image, PIL
- NLTK, spaCy
- Hugging Face Transformers
- Git and GitHub
school-assessments/
├── da-airline-passenger-clustering/
├── da-bikes-demand-prediction/
├── dl-covid-ct-classifier/
├── dl-imdb-sentiment-classifier/
├── ml-course-rating-predictor/
├── nlp-annual-report-brochure/
└── nlp-sroi-reports/
The individual project READMEs describe each folder’s dataset, methodology, results, limitations, and usage steps in more detail.
-
Clone the repository.
git clone https://github.com/Gyres/school-assessments.git -
Navigate into any project folder of interest.
cd school-assessments/<project-folder> -
Install the dependencies listed in that project’s README.
-
Open the notebook and run the cells in sequence.
Some projects require datasets that may be too large to store directly on GitHub, so their READMEs include extra setup notes for local execution.
- This repository is intended as an academic portfolio showcasing applied analytics, machine learning, deep learning, and NLP work.
- Projects vary in scope, including individual and group assignments.
Unless otherwise stated in a project folder, the materials in this repository are shared for portfolio showcase purposes only. Please do not copy, reuse, or submit the work as your own academic assignment.
- Name: Ou Yang Yu
- GitHub: https://github.com/Gyres
- Linktree: https://linktr.ee/yuouyang