A set of five interactive charts created in R, showing long‑term trends in rental stress, housing costs, and household types in Australia.

These are the math problems of the day for Friday 6/12/26.

The rapid growth of e-commerce has created many opportunities for online businesses, but it has also brought challenges such as product returns and unpredictable sales demand. Product returns can increase operational costs, affect customer satisfaction, and reduce business efficiency. At the same time, accurate sales quantity prediction is important for inventory planning, stock management, and revenue growth. This project analyzes an e-commerce transaction dataset to study return behavior and sales quantity prediction. The project focuses on two main machine learning tasks: classification and regression. The classification task aims to predict whether an order will be returned, while the regression task aims to predict the quantity of products sold. These analyses can help e-commerce companies identify return drivers, improve decision-making, reduce unnecessary costs, and manage stock more effectively.

WQD7004 Group Project. A complete data science workflow in R analysing NYC property sales: data cleaning, EDA, and predictive modelling for sale price (regression) and high-value transaction classification.

The Shapiro-Wilk test is exquisitely sensitive to sample size, not normality itself. With large n, it flags trivial deviations. With small n, it misses real departures. This leads researchers to make the wrong inference about whether to use parametric tests.

Five interactive data visualisations exploring threatened species trends in Australia using EPBC Act listing data, the Threatened Species Index 2025, and the 2016 State of Environment report. Built in R with plotly for RMIT Data Visualisation and Communication Assignment 3.