It is a simple recipe app that comes with data populated with firebase. It is a how-to cook app complete with recipes required to fully cook a specific dish. Its data migration is so simple that with just a few tweaks in its columns/fields you can import any recipe data available on the internet.
Another feature is its ability to list and save the key ingredients needed for a specific dish that you wish to cook in just a single click. This simple and yet handy app is a must-have for all food lovers out there who are in the process of learning how to cook any recipes found on the app.
It’s a simple and straightforward chat application and has the functionality of sending text messages that will be automatically deleted within 24 hours for the initial version.
For the first version app release, we are planning to provide the key features and the main infrastructure of the app. Functionality and Process flow will be its main priority.
For the API we are going to be using Codeigniter and MySQL for the database hosted by Namecheap servers. This will also be using HTTPS protocol for all connection requests and communication to the server.
This is the new design for 2021, first prototyped in Figma and implemented by our volunteers.
This volunteer, with a background in Computer Science, decided to make a web app that can give guidance and direction on what to do if you’re not feeling mentally well. One of the methods he implemented in the web application that utilizes React and HTML, there4u, is the guidance for box breathing.
Deploying an open source full stack serverless application to search, sort, and filter the public records on AWS, with CI/ CD (continuous integration, continuous deployment) workflows.
Developed using a popular JavaScript Framework, React. React Bootstrap library is used for UI components. The frontend is configured to redeploy on every repository push on the main branch. Github repository for the front-end source code:
Developed using Node.js, Express, and Serverless frameworks, and deployed on AWS Lambda. The REST API is developed using AWS API Gateway at this base url (https://q60xzpit4j.execute-api.us-east-1.amazonaws.com/dev). No API Key is required to access the API endpoints at the moment. The backend is also configured to deploy on every repository push on the main branch, using GitHub Actions. Github repository for the back-end source code:
The serverless database is up and running on AWS RDS with PostgreSQL-compatible Aurora engine. The database is publicly accessible with the correct host, username, and password. Only the reader instance is used with the application since the application does not require create, update, delete operations.
This volunteer worked on a lot of projects during his time with the program. He created a number of games such as a snake game, pong, cross road, and many other using Python. I also improved my skills using C language and object oriented programming.
This project focuses on building a game from scratch in C++ without external libraries or game engines. It consists of implementing the game simulation logic, resource management, often hidden functionalities (such as graphics), and integrating on the Windows platform. Currently, it can bring up a Windows graphical application and render primitive shapes, such as rectangles, concerning user inputs (such as mouse clicks). Future work includes supporting more keystrokes, rendering shapes, and interesting game logic.
RND4IMPACT.com 5.0 , the new design for 2023, first prototyped in Figma and implemented by our volunteers.
The Project name was “SafeSecretShare”. We often need to share sensitive credentials such as account passwords, credit card information, and API credentials with our friends, family, and co-workers. While sharing credentials, we often rely on the security of communication methods such as email and messaging apps such as WhatsApp, Signal, etc. These communication methods are not designed with the safety in mind of sensitive credentials and do not allow security features such as limited-time viewing, automatic deletion, IP address filtering, etc. The project’s main idea is to enable users to securely share sensitive credentials.
I have created a back-end repository in Golang containing APIs to store encrypted messages. Github
You can find more about the project’s technical description in the following Google doc:
This Python project implements ideas and problems from the space of quantitative finance. Some simple quantitative problems with theoretical results will be empirically verified using simulation. Some complex problems will also be simulated.
The goal of the project is to simulate ideas from simple problems to more complex problems. Optimization techniques will be implemented to improve the runtime and space complexity.
Machine learning, reinforcement learning, constraint optimization, etc. ideas will be implemented soon. Github
Fullstack project after the onboarding process until the 9th week. Implemented both front-end interface and back-end server with JavaScript and deployed the code to GitHub with a demo link and introduction.
Overview: The front-end interface was coded in JavaScript and deployed to Vercel with a user-friendly interface with the purpose of making easier process for user to book a hotel room. The back-end server was first deployed to AWS (but also local) and write the database to the MongoDB. The database will keep track all the data such as the guest information, room information, etc … Each customer will have their own accounts to make reservation and can check back to past or the current reservation with the total amount of money for each reservation. The user can also leave a review if they want to share their experience to other customers.
Fullstack project from week 10 until week 19. Implement the front-end interface in JavaScript and the back-end server in Java. The project allows users to upload multiple database files then visualize the data in different charts and also make predictions for the users if necessary.
Overview: The front-end interface was coded in JavaScript and deployed to Vercel. Friendly interface allows user to upload multiple database files (currently there are 3 types of data file supported) and they can choose what type of plots they want to display the information. The back-end server was coded in Java and Python to process requests sent from the front-end interface. The Java back-end server used to process and arrange data for displaying purpose meanwhile the Python back-end server used to for predicting purpose.
Last project from week 20 until the end (week 22). Implement front-end in JavaScript and integrate the weather interface online to look for information on the real-time data on the weather.
Overview: The project mostly focus on creating a user-friendly interface for a weather application and integrate some onlines API to help looking for information about real time information. The website was deployed to Vercel with the link: 
This project is a set of APIs for the library management system. It has endpoints for different – books, author, borrowers. It also has support for data validation using middleware.
Project Descriptions 
Voting management application is an API based service. There are endpoints exposed for both Polls and Votes. Security with JWT authentication is also implemented. Middleware is used for error handling and also prevention of unauthorized manipulation including uniqueness of votes. The real-time updates for voting counts was done using Socket.IO/SignalR software for .NET.
Project Descriptions
It takes location as the input and displays information about its weather. It also has CSS to support styling. OpenWeatherMapp API is used to get more accurate and granular weather information. It also has weather forecast option using the same API.
Project Descriptions
Javascript based questionnaire paired with HTML. It also has CSS and styling components, which are easily configurable. There is also a component for random question generation. The score is displayed at the end.
Project Descriptions
The first Computer Vision project I conducted in 2024 was Iris Classification, a classic machine learning project in Kaggle or Dacon. As a Volunteer Software Engineer, I have implemented image classification models that classify different types of iris flowers. This project explores classifying Iris flower species (Iris setosa, Iris versicolor, and Iris virginica) using machine learning algorithms. For this project, I employ the classic Iris dataset and investigate the efficacy of different classification models, including supervised and unsupervised learning. The analysis includes data exploration, model training, evaluation, and final selection of the best-performing model.
Project Descriptions
Online Voting System: Application built using the MERN stack where users can create polls, share polls and vote on polls.
Stock Dashboard: Application built using the MERN stack where users can look at stock prices, graphs and add favorite stocks to their account for easy viewing
Weather App: Application built using the MERN stack where users can check the weather forecast and a map that will tell you the temperature of each city
Hotel Booking System: Application built using the MERN stack where users can book hotelsOBS Dashboard: Application built using Svelte (Frontend Javascript) where users can control their livestream from a single page
In this project, I converted Google Sheets data from a sales company into a relational database. My primary focus was to master SQL and table creation. The transition to a relational database was crucial for eliminating redundancy and anomalies inherent in list-based data. This is particularly important for companies in the e-commerce industry that need to efficiently track order details, customer information, and products. I also created an Entity-Relationship (ER) Diagram to illustrate functional dependencies among the variables. Through this project, I developed key skills in SQL, MySQL Workbench, data cleaning, data mining, and Google Sheets.
In this project, I focused on enhancing my Python skills through an off-platform project provided by Codecademy. The goal was to write Python code to decipher received messages and encode my own. I successfully implemented the Caesar Cipher and Vigenère Cipher, allowing for encoding and decoding of messages using these methods. This project helped me develop key skills in Python and cryptography.
In this project, I aimed to enhance my data visualization skills using Tableau. Through Codecademy, I worked with multiple CSV files containing data from the 2015 Tree Census in NYC and median household incomes across all NYC zip codes. My goal was to explore the relationship between tree density and income, specifically whether higher-income zip codes have a higher density of healthy trees. With Codecademy’s guidance, I created seven different charts, including bar charts, scatter plots, bubble charts, and map charts, to answer various questions about the relationship between greenery and income. I combined these charts into a comprehensive dashboard that effectively tells the story of this relationship. The outcome revealed a clear correlation between higher-income zip codes and a higher percentage of healthy trees.
In this ongoing project, I am manipulating data from a list of Category 5 hurricanes to enhance my proficiency with Python dictionaries, a crucial tool for data analysts. The tasks involve reorganizing and sorting data based on specified dictionaries, which has helped me refine my skills in Python dictionary usage and statistical analysis. This project aims to highlight the impact of each hurricane through comprehensive data analysis.
The files include 3D models, assembly model & drawings for the Vertical Stirling Engine design. Also uploaded Weekly report, manufacturing plan, & Bill of Materials for the Stirling Engine.
This repository contains the frontend code for the Hotel Booking System, a fully-featured web application that allows users to book hotel rooms with ease. The frontend is built using React and incorporates a range of modern web development practices and tools.
A comprehensive comparative study of health expenditure data for 192 countries over the past two decades. This project covered the entire data pipeline—from data collection and cleaning to analysis and visualization—with the goal of generating actionable insights. Visualizations included bar charts and filled maps for summary dashboards, and point maps for detailed views.
This project is a Jupyter Notebook that implements a machine learning model to classify dog images by breed. The classifier leverages deep learning techniques to identify different dog breeds based on input images.
This project implements a Document Summarizer and Question-Answering (QA) System using Retrieval Augmented Generation (RAG) principles. It combines document retrieval, summarization, and question-answering capabilities using FAISS for efficient vector search and OpenAI GPT models for summarization and Q&A tasks.
This project involves analyzing customer data from an e-commerce platform to identify distinct customer segments based on their purchasing behavior, demographics, and engagement. By using clustering techniques such as K-Means or hierarchical clustering, the project helps in classifying customers into meaningful groups (e.g., loyal customers, high spenders, deal-seekers). These insights enable targeted marketing strategies, personalized communication, and improved customer retention.
The Life Expectancy Project aims to analyze global health and socio-economic data to understand the key factors influencing life expectancy across different countries. By exploring variables such as GDP, healthcare expenditure, education, immunization rates, and disease prevalence, the project uses data visualization and machine learning models to identify patterns and correlations. The ultimate goal is to provide insights that can help policymakers and health organizations make informed decisions to improve public health and increase life expectancy worldwide.
The Product Sales Analytics Project is designed to analyze historical sales data to uncover trends, patterns, and insights that drive business performance. By examining key metrics such as sales volume, revenue, product performance, seasonal trends, and regional distribution, the project helps identify top-selling products, underperforming items, and sales opportunities. Using data visualization and predictive analytics, it supports strategic decision-making in inventory management, pricing, marketing, and forecasting future sales for improved profitability and growth.
The House Price Prediction Project aims to develop a data-driven model that accurately estimates property prices based on various features such as location, size, number of rooms, age of the house, and nearby amenities. By leveraging machine learning algorithms and exploratory data analysis, the project identifies the key factors that influence housing prices. This helps buyers, sellers, and real estate professionals make informed decisions, optimize investments, and understand market dynamics in the real estate sector.
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