Project on Health data using R and ShinyApp

This project examines the impact of the COVID-19 pandemic on trends in mental health services in Scotland. It focuses on immediate and long-term shifts in psychiatric admissions, demographic disparities and regional variation. Using high-frequency, population-level data from Public Health Scotland and NHS Scotland (2018-2023), the study employs reliable quasi-experimental methods, Interrupted Time-Series (ITS) analysis and Mixed-Effects Regression modelling, to measure both sudden and sustained changes in service utilisation and diagnostic patterns. Interactive Shiny dashboard visualizes findings, informs policy and resource allocation. The ShinyApp facilitates stakeholder engagement, real-time monitoring, evidence-based decision-making and targeted interventions.

Predicting Daily Collision Counts.

This end-to-end data analytics project explores the drivers of daily road collisions over eight years, combining exploratory analysis with two predictive modelling approaches: Linear Regression and a Deep Neural Network (DNN). Implemented entirely in Python on Google Colab, the project demonstrates proficiency in data cleaning, feature engineering, statistical analysis and TensorFlow-based model development.

This project illustrates a full analytics lifecycle from data ingestion and exploratory visualisation to advanced predictive modelling. It delivers actionable insights into collision dynamics. The combination of statistical rigour and machine learning sophistication makes it a compelling showcase for both data science and applied analytics roles.

Classic Models

This Power BI dashboard explores sales and profitability for Classic Models Ltd, a retailer of collectible vehicles. It provides an interactive view of global performance from 2003 to 2005, helping managers identify profitable regions, products and customers.

Users can filter by year, month, product line or country to explore trends and compare performance. Designed entirely in Power BI Desktop, this project demonstrates strong skills in data modelling, DAX measures and storytelling with visuals. It turns raw sales data into actionable insights for operational and strategic decision-making. The analysis highlights the USA and France as leading markets and shows that Classic Cars consistently drive the highest profit. The dashboard illustrates how clear data visualisation can guide evidence-based business decisions and improve sales planning.

Electric Vehicle Analysis and Dashboard

This Tableau project presents an analytical overview of electric vehicle (EV) adoption across Washington State. This highlights trends by vehicle make, model, type and geographic distribution. The dashboard integrates registration and ownership data to help policymakers, researchers and businesses understand patterns in EV usage and infrastructure needs.

The analysis focuses on Battery Electric Vehicles (BEV) and Plug-in Hybrid Electric Vehicles (PHEV), identifying Tesla, Nissan and Chevrolet as the leading manufacturers. Users can explore data interactively by county, year and vehicle type, gaining insight into how EV adoption has evolved from 2014 to 2023. Geographic maps reveal concentration hotspots such as King, Snohomish and Pierce counties, which collectively account for the highest EV ownership.

This is built entirely in Tableau Desktop, the dashboard demonstrates strong capability in data visualisation, geospatial analysis and interactive storytelling. It transforms complex datasets into an accessible, evidence-based tool for promoting sustainable transport and supporting data-driven policy decisions. This project showcases how visual analytics can bridge the gap between environmental data and actionable insights for a greener future.