Projects
A selection of projects demonstrating my experience in data analysis, machine learning, and data-driven problem solving. Each project reflects my ability to work with real-world datasets, build analytical pipelines, and generate actionable insights.
Renery — Smart Energy Planner
Currently in Development
Renery is a web application designed to help Australian households make greener electricity choices. It combines renewable-energy forecasts, electricity plan information and the user's available schedule to recommend better times for flexible household activities such as washing, dishwashing, clothes drying and EV charging.Problem
Renewable-energy availability and electricity tariffs can change throughout the day, but this information comes from different sources and can be difficult for everyday households to understand and compare.
Tools
React, Tailwind CSS, Node.js, Express.js, PostgreSQL, Python, Pandas, Scikit-learn, AEMO Energy Data, AER Electricity Plan Data.
Outcome
Currently in development. Renery aims to turn complex energy and tariff data into simple, practical recommendations that help households decide when to use flexible appliances.
Spotify Data Analytics
Built a comprehensive data analysis pipeline covering data collection, cleaning, database design, statistical analysis, and machine learning. The project explored audio features and user behaviour patterns across multiple countries and time periods, enabling insight generation and visualization.Problem
Understand patterns in music data and identify factors influencing song popularity and user listening behaviour.
Tools
Python, SQL, Pandas, Machine Learning (Regression, Clustering, Classification), Power BI.
Outcome
Developed an end-to-end analytics pipeline and interactive dashboards to generate insights and support data-driven recommendations.
Chronic Disease Prediction & Progression Analysis
Analyzed anonymized patient health data to study chronic disease risk and progression using both exploratory analysis and predictive modeling. The project examined how variables such as smoking, diet quality, gender, biomarkers, and lifestyle factors relate to disease outcomes, while also building interpretable classification models for chronic disease prediction.Problem
Analyze how demographic and lifestyle factors influence chronic disease risk and identify meaningful patterns across patient populations.
Tools
R, tidyverse, ggplot2, caret, recipes, logistic regression, multinomial logistic regression.
Outcome
Combined exploratory healthcare analytics with predictive modeling to identify disease-related patterns and support data-driven interpretation.
Amazon Laptop Data Analysis
Collected real-world product data through web scraping, performed data cleaning and feature engineering, and developed machine learning models to predict laptop prices and analyze feature importance across different product categories.Problem
Analyze laptop pricing and specifications to understand market trends and key pricing factors.
Tools
Python, Selenium, BeautifulSoup, Pandas, Linear Regression, Random Forest.
Outcome
Built predictive models to estimate laptop prices and evaluate how technical specifications affect pricing.
