In this short exercise, I explored how gradient descent reduces a cost function by repeatedly updating model parameters.

This post briefly reflects on three neural network notebooks: a simple perceptron, a perceptron trained for the AND operator, and a multi-layer perceptron for a more complex non-linear problem.

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.

Introduction​

This portfolio activity calculates the Jaccard coefficient for three pairs of individuals using a small table of pathological test results.

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.

This post is part of the Collaborative Discussion 1 assignment for the Machine Learning module.

This post reflects on my initial post, two peer responses, and the summary post for the first collaborative discussion on Industry 4.0, Industry 5.0, digital infrastructure and data-driven operational resilience.

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.

Notebook 1: Covariance, Pearson Correlation and Regression​

Google Colab notebook: Open in Google Colab

Purpose of the notebook​

I expanded a basic correlation and regression example into controlled experiments to explore how dataset properties affect statistical results.

Google Colab notebook: Open in Google Colab

Introduction​

In this activity, I carried out exploratory data analysis (EDA) on the Auto MPG dataset to judge whether it was suitable for machine learning and to identify key uncertainties before modelling.