My Coding Projects

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Rental Management Site

A rental web app that lists propery managers and tenants. You can see prices, homes and various things associated with renting. This was a project I worked on for a 3rd yr course

Languages Used: SQL, Javascript, HTML, CSS, PHP

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Java Express Emailer

This project demonstrates core Java networking and email APIs through three programs:

SendEmail – Sends messages with To, CC, and BCC from a text file.

SendEmail w/ Attachments – Adds support for sending files (.jpg, .png, .zip).

GetMail – Retrieves and displays unread emails, with the option to download specific messages.

Key skills: Java I/O, networking, SMTP/IMAP/POP protocols, and email handling with attachments.

Languages Used: Java

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Shakepearing Insult Generator

A C++ program that generates up to 10,000 unique Shakespearean-style insults using random combinations of words from a source file. The project implements three classes:

Languages Used: C++

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UHYVE (HYVE V2)

uHyve is a web application built to help people easily navigate a new city and connect with local services such as barbers, braiders, and nail techs. Inspired by the challenge of finding these services at a PWI (Predominantly White Institution), the app streamlines the process of discovering trusted community providers.

 The source code is private but can be shared upon request.

Key skills: full-stack development, React, Node.js, SQL, REST APIs, and collaborative software engineering.

Languages Used: SQL, Javascript, HTML, CSS, PHP

Frameworks: React, Node.js

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Poem of the Day Server

A Java socket-based server that demonstrates client-server communication. The server loads poems from a text file, waits for Telnet connections, and guides clients through a simple finite-state machine (FSM) interaction:

  • Sends a welcome message, poem list, and instructions.

  • Accepts user input to select a poem.

  • Responds with the chosen poem or an error message.

  • Ends the session and waits for the next client.

Languages Used: Java

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Machine Learning Material

This project applies machine learning algorithms to Hidden Markov Models (HMMs) and neural networks. It includes:

  • Joint Probability Computation – Calculates sequence probabilities using the chain rule.

  • Viterbi Algorithm – Implements dynamic programming to decode the most likely sequence of hidden states.

  • Feedforward Neural Network – Code provided for basic classification.

Languages Used: MATLAB

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