This is a custom backend for the first iteration of the discussion platform. This was created to learn how to create a custom backend using Python and Flask.

A simple Flask app to learn how to create a RESTful API. This was a foundational project to learn how to create a back-end using Flask. This was helpful when creating the back-end for the discussion platform.

A simple Flask app to learn how to use JWT for authentication. This serves as a foundation to using JWT in other projects using Flask.

A simple Django app to learn how to use Django with tokens for authentication. This serves as a foundation to using Django in other projects.

A simple Next.JS app to experiment with the Clerk Authentication SDK.

A simple Next.JS app to experiment with the Auth0 Authentication SDK. This does not use the new Next.JS 13 app router as it is not supported as of the time of making this demo.

A project leveraging the UCI Adult Income dataset to predict income brackets using a RandomForestClassifier. Emphasis is on feature engineering, data preprocessing with One-Hot Encoding, and model optimization through hyperparameter tuning.

An analytical approach to predicting California housing prices using the RandomForestRegressor and LinearRegressor, with a focus on data preprocessing and feature engineering.

Be able to implement machine-learning algorithms, using the Nearest Neighbours algorithm as an example. Have an understanding of ways to apply the ideas and algorithms of machine learning in science and technology.

Be able to use and implement machine-learning algorithms, with the Lasso and inductive conformal prediction algorithms as examples. Have an understanding of ways to apply the ideas and algorithms of machine learning in industry and medicine.

Be able to use and implement machine-learning algorithms, with the SVM, neural networks, and cross-conformal prediction algorithms as examples. Have an understanding of ways to apply the ideas and algorithms of machine learning in industry.

Implemented various machine learning algorithms and techniques learned during the course, such as Nearest Neighbours, conformal prediction, linear regression, Ridge Regression, Lasso, data preprocessing, parameter selection, kernels, neural networks, support vector machines, scikit-learn pipelines, and cross-conformal predictors.

An assignment exploring valuation of options using methods like Black-Scholes, binomial trees, and Monte Carlo. Also includes theoretical aspects of put-call parity and financial arbitrage opportunities.

Simple calculator app built using Java as a Maven project. This was a second year Java assignment focused on software engineering methodologies. The project involved creating a calculator application, emphasizing the importance of proper version control procedures, test-driven development, documentation, and code quality assurance through linting and styling. The assignment highlighted the significance of following efficient software development processes rather than just focusing on the final implementation.

Simple botanic garden planner app built using Java. This was in first year to learn about Java and object oriented programming.

Simple app to track Covid cases. This was in first year to learn about Java and object oriented programming.

This was in first year to learn about Java and object oriented programming.

Learning to interact with a database using Java.

A collection of Leetcode solutions in Python. This is used to practice algorithms and data structures. They are also used to practice unit testing. CI/CD is also used to run the tests when merging to the main branch.

Jupyter Notebook containing various searching and sorting algorithms. Each algorithms is explained. All the algorithms are also compared to each other.

A shell script which automates the setup of a new Linux machine. This is specifically for my Fedora install.