Machine Learning Project with Clustering Analysis (Python)

This project applies unsupervised learning using K-Means clustering to segment customers based on real-world marketing campaign data from Kaggle (2,240 records). The goal was to identify distinct customer groups using demographic and transactional attributes to support targeted marketing and resource optimization. The process included data cleaning, feature engineering (e.g., tenure, total spend, family size), encoding categorical variables, and standardizing numerical features. K-Means was then used to uncover patterns in customer behavior and define meaningful segments.

Brazilian E-Commerce Data Analysis (SQL & PowerBI) 

A comprehensive analysis of 100,000+ orders from a leading Brazilian e-commerce platform (2016–2018) was conducted to uncover insights into customer behavior, delivery efficiency, product performance, and regional demand. The project involved end-to-end execution, from data preparation using MS SQL to developing an interactive Power BI dashboard which enabled data-driven decisions across marketing, operations, and sales teams. 

Dissertation project with NHS (Score: 75/100)

An 8-week collaboration with NHS Lanarkshire focused on enhancing the efficiency and quality of Chronic Disease Monitoring (CDM) services in Community Treatment and Care (CTAC). Using Strategic Options Development and Analysis (SODA) and Cognitive Mapping, key pain points were identified through stakeholder interviews. A data-driven needs assessment highlighted three priority areas: IT systems, skill mix, and treatment room services. To align strategic goals, 48 KPIs were developed using the Balanced Scorecard framework, with an added innovation perspective to support long-term service improvement.