This reflection covers my initial post, peer responses, feedback and summary for the second collaborative discussion in the Machine Learning module, which focused on AI writers, human oversight and responsible use.
Google Colab notebook: Open in Google Colab · Raw Notebook: Download .ipynb
My Colab notebook applies the same k-means clustering process to three datasets: Iris, Wine, and WeatherAUS. Here, I explain the methodology behind it.
I watched two animations of the k-means clustering algorithm to see how it repeatedly assigns data points to clusters and updates their centroids.
Google Colab notebook: Open in Google Colab · Raw Notebook: Download .ipynb
This portfolio activity investigates the relationship between a country or entity's mean population and its mean total GDP across the years 2001-2020. The analysis uses two World Bank datasets, global_gdp.csv and global_population.csv, and applies descriptive analysis, Pearson correlation, log-scale visualisation, and simple linear regression.
The first collaborative discussion in the Machine Learning module focused on Industry 4.0, Industry 5.0, digital infrastructure and data-driven operational resilience. Here, I reflect on my initial post, two peer responses and the final summary.
Google Colab notebook: Open in Google Colab · Raw Notebook: Download .ipynb
The Airbnb NYC 2019 dataset is useful for exploring listing-level patterns, but it has one important limitation: it does not contain direct demand measures. There are no actual booking counts, occupancy rates, revenue figures, or guest ratings that can be used as a clean target variable.