SUPERSTORE DATABASE BUILDING USING SQL
As part of my MSc coursework, I co-developed a Superstore Database system that modelled both online and in-store retail operations. The project involved analysing business rules, designing an Entity–Relationship model, and implementing a fully normalised relational database in Oracle SQL. We created tables for customers, staff, products, vendors, orders, and deliveries, ensuring integrity through constraints and testing. Data was then inserted and optimised with queries, indexing, and performance improvements. This work demonstrated advanced database design, normalisation up to 3NF, and practical SQL implementation for a real-world retail environment and was a collaboration with a guest lecturer at bcu, UK.
Stock Market Analysis of the Walt Disney Company
This project explored two decades of Walt Disney Company stock market data to uncover patterns and trends shaping investor behaviour. By applying time series forecasting (ARIMA), the analysis revealed seasonal patterns and the impact of major global events such as the 2008 financial crisis and the 2020 COVID-19 pandemic. These insights demonstrate how historical stock data can be transformed into meaningful forecasts, supporting investors, businesses, and policymakers in making more informed, data-driven decisions while navigating market uncertainty.
aiding second hand car buyers make the right choice
This project analysed the UK used car market to understand the key factors influencing vehicle pricing and buyer decisions. By applying statistical techniques such as exploratory data analysis, correlation, hypothesis testing, and regression modelling, the study uncovered how variables like mileage, engine size, transmission type, and fuel type affect car values. The analysis revealed important trends – such as the rising cost of automatic and electric vehicles, and the trade-offs between fuel efficiency (mpg), taxes, and engine performance. These insights are highly valuable not only for buyers and sellers in the used car market but also for businesses seeking to optimise pricing strategies and for policymakers monitoring consumer trends. Ultimately, this work demonstrates how applied statistics can guide smarter financial decisions in dynamic markets.