I bridge the gap between traditional software development and modern data science. Whether I'm architecting cloud solutions, deploying machine learning models, or guarding the net as a middle blocker, I bring discipline, focus, and teamwork to every challenge I tackle.
Hey, I'm Aidan. I recently graduated from Varsity College Newlands with a BSc in Application Development, but my real education happens when I'm building things from scratch.
I thrive in the space where backend logic meets clean architecture. I'm currently focused on expanding my cloud capabilities with AWS and Azure, building APIs with Flask, C#, Node.js, and Python, and diving deep into the world of Data Science and DevOps.
But I'm not just a guy behind a keyboard. When the laptop closes, I'm out chasing waves on my surfboard, hitting the gym, spiking volleyballs the UCT third team, or jamming with my church band. Discipline offline fuels my focus online.
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Year 1: Foundation in Application Development and core programming concepts.
Year 2: Advanced OOP, database architecture, and initial cloud deployments.
Year 3: Graduation. Data Structures, Work Integrated Learning, and Mobile Development.
Managed logistical operations and streamlined internal processes for improved efficiency.
Lead development of a full stack web application using c# asp.net and node.js, implementing RESTful APIs and responsive design.
Developed applications like a wedding rsvp system for a client as well as an application for the breast cancer foundation to help them manage their donations and events.
The FlexForce API serves as the backend for the FlexForce fitness application. It provides RESTful endpoints for managing user authentication, workouts, and fitness tracking data.
This roject was crazy fun to build but also crazy hard to build at the same time. Had a large learning curve but managed to bring it back with the help of some great team members like GOATIV. Overall taught me a lot about api development and full stack development in general.
A robust data processing and analytics pipeline built with Python and Pandas. This project integrates disparate organizational datasets (Office XMLs and HR records) to provide deep insights into employee performance, satisfaction, and retention metrics.
This project was a great exercise in data cleaning and transformation. I had to deal with a lot of messy data and learn how to use Pandas effectively to extract meaningful insights. It really improved my data manipulation skills.
The goal was to build a system capable of recognizing digits (0-9) from grayscale images. By transforming spatial pixel data into flat feature vectors and applying advanced normalization techniques, the pipeline achieves high accuracy using ensemble learning and nearest-neighbor algorithms.
This was a fantastic project to work on. It really pushed me to understand the intricacies of image data and how to preprocess it for machine learning. The combination of ensemble methods and nearest-neighbor algorithms was particularly rewarding when I saw the accuracy improve.
This project creates a robust data preprocessing pipeline to prepare NBA player data for Machine Learning tasks, specifically Linear Regression to predict player salaries.The script automates the entire flow from data collection to the final transformation, ensuring the dataset is clean, enriched, and mathematically optimized for model training.
This project was a great way to apply data preprocessing techniques to a real-world dataset. I learned a lot about handling missing values, feature engineering, and preparing data for machine learning models. It was rewarding to see the final dataset ready for training.
This repository contains the frontend implementation of the Emora Trade Space project, built using the ASP.NET Web Application 4.8 framework. The project follows the MVC (Model-View-Controller) architecture and connects seamlessly to a backend API developed using TypeScript, Node.js, and Supabase.
This project was a great opportunity to work with the ASP.NET framework and integrate it with a modern backend API. It was a bit of a learning curve, but I enjoyed the challenge and gained valuable experience in full-stack development. Wasnt able to finish the project due to team members leaving but I learned a lot along the way and am proud of what I accomplished.
A data-driven machine learning tool designed to predict professional salaries based on key performance metrics (Rating, Experience, Age, etc.). This project demonstrates a progressive approach to regression analysis, moving from simple linear models to complex feature selection and multicollinearity optimization.
This project was a great way to apply machine learning techniques to a real-world problem. I learned a lot about regression analysis, feature selection, and model optimization. It was rewarding to see the final model perform well on the test data.
Amazon Web Services
Hyperskill
Hyperskill