Portfolio
A selection of machine learning, NLP, data-engineering and visualisation work. Filter by category below.
Customer segmentation using RFM and KMeans clustering (with kernel PCA) to build actionable customer groups and targeted marketing strategies.
MongoDB and PyMongo to store, restructure and analyse GitHub OSS data — querying commits, authors and activity patterns.
Visual exploration of COVID-19 impact on UK businesses across 2019–2021 with interactive and animated charts.
NLP sentiment analysis and network analytics on 10k+ YouTube comments to map public perception of the Fiat 500 EV.
EDA and KMeans guest segmentation plus multiple models to predict booking cancellations and compare performance.
NLP pipeline detecting helpful IMDB reviews with text preprocessing, feature engineering and supervised models.
Bigfoot sightings analysis — EDA, NLP and semantic classification, built end to end.
Scraping pipelines for NBA defensive analytics and Aldi job postings, from collection through to analysis.
Forecasting framework in TensorFlow/Keras with LSTM and CNN architectures and training visualisations.
Supervised and unsupervised ML in R — trees, random forests, SVMs, kNN, LDA and cross-validation.
Network analysis of trader knowledge-sharing and attitudes to AI on a trading floor using graph metrics.
Credit-risk modelling on bank client data to estimate default likelihood and identify the key drivers.
Interactive R Shiny app predicting appropriate medications from patient characteristics.