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.
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.
Google Colab notebook: Open in Google Colab · Raw Notebook: Download .ipynb
I expanded a basic correlation and regression example into a set of controlled experiments exploring how dataset properties affect statistical results.