My most recent Projects
I recently completed the Data Analytics Career Accelerator course from
the London School of Economics and Political Science (LSE). During the
programme, I worked on four hands-on projects across the finance,
healthcare, and retail sectors. These projects involved applying
statistical and exploratory data analysis techniques, predictive
analytics, machine learning frameworks (Scikit-learn), and data
visualisation tools to real-world datasets using Python, R, SQL,
Tableau, and Excel.
In addition, I completed an employer project with the Bank of England,
where I explored the relationship between the sentiments of speeches
made by senior representatives and various economic indicators. Click
on the links below to learn more about each project.
Global Supermarkert Analysis: Data Analysis for Business
Excel | SQL | Tableau
An Exploratory Data Analysis was performed on a global supermarket using Excel, SQL and Tableau. Presented engaging, interactive Tableau dashboards to non-technical stakeholders to explore the best selling products based on customers' demogragraphics, to investigate the most effective advertising platform and to understand customers' purchasing behaviour.
Turtle Games
Python | R | Predictive Analytics | Machine Learning
As part of the LSE Data Analytics Accelerator, I explored customer loyalty patterns, segments, and review sentiment for Turtle Games using Python and R. The project involved regression modelling, decision trees, K-Means clustering, and NLP techniques to provide actionable business insights. Click on the link to view the full report and Python and R code on Github.
Sentiment Analysis of Bank of England Speeches: An Employer Project
Python | NLTK | XGBoost
Used NLP to analyze 20+ years of central bank speeches, classifying sentiment and tone (hawkish, dovish, neutral). Explored trends over time and assessed links to key economic indicators. Delivered insights and strategic recommendations based on exploratory analysis and regression modeling.