
Bank Customer Churn Analysis
Analyzed 10,000 bank customers to identify demographic and behavioural drivers of churn, validate key patterns statistically, and build a Random Forest model to predict customers at risk of leaving.
Data Analyst
Exploring what’s possible with data.

I'm interested in what happens when curiosity meets data.
I started with data analysis because I wanted to understand how information can be turned into something useful. I'm not just talking about reports and dashboards, but about better questions, clearer decisions, and eventually better systems and solutions.
The past year has been about building projects and exploring what's possible with data, using tools such as SQL, Excel, Power BI, and Python. It's been about learning how to think through problems and turn what I learn into something tangible.
I'm currently building my foundations across data analysis, statistics, machine learning, and computer science. Each gives me a different way of looking at problems: data helps me find patterns, statistics helps me reason about uncertainty, and programming gives me the ability to build beyond the analysis itself.
Ultimately, I want to create things that are useful, and I'm just getting started.
Data analysis, automation, and building reproducible workflows.
Querying, joining, and transforming relational data to extract meaningful insights.
Data cleaning, analysis, modeling, and building practical business solutions.
Building interactive dashboards and reports that communicate data clearly.
Strengthening understanding of probability, inference, and statistical modeling.
Exploring data science techniques to uncover patterns and build models.
Learning ML algorithms and applications to make data-driven predictions.
Continuously learning new technologies and approaches as they emerge.
A selection of problems I’ve explored through data.

Analyzed 10,000 bank customers to identify demographic and behavioural drivers of churn, validate key patterns statistically, and build a Random Forest model to predict customers at risk of leaving.

Analyzed New Zealand vehicle theft data to uncover trends across time, vehicle types, and regions, identify high-risk patterns and regional disparities. The findings were translated into practical recommendations for targeted prevention.
A new project currently in development. More details will be shared when it's ready.
Thoughts on data, technology, learning, and building.
The first note is taking shape.
“Creativity is intelligence having fun.”
Have a question, an opportunity, or something interesting to build? I’d be glad to hear from you.