Google Colab notebook: Open in Google Colab
This post explains the K-Means clustering methodology used in my Colab notebook on 3 datasets - Iris, Wine, and WeatherAUS. The notebook applies the same clustering process to these datasets.
This post reflects on two animations of the k-means clustering algorithm, which illustrate how the algorithm iteratively assigns data points to clusters and updates cluster centroids.
Google Colab notebook: Open in Google Colab
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, then applies descriptive analysis, Pearson correlation, log-scale visualisation, and simple linear regression.
Google Colab notebook: Open in Google Colab
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
I expanded a basic correlation and regression example into controlled experiments to explore how dataset properties affect statistical results.