Niv Neuvirth

MealMemo - Android Recipe Application

The Recipe App is a feature-packed Android application that enables users to discover recipes based on the ingredients they have available. The app retrieves recipe data from the Spoonacular API, offering detailed instructions, ingredients, and nutritional information. Users can also save their favorite recipes for quick future access. To enhance user experience, the app integrates Azure Translator API, offering multi-language support by translating recipe details based on the user’s preferred language.

Built using the MVVM (Model-View-ViewModel) architecture for clean separation of concerns, the app features a modern UI designed with XML layouts. The app utilizes Kotlin Coroutines for asynchronous background tasks, ensuring smooth performance. Dagger Hilt is employed for dependency injection, providing an efficient and modular architecture. Retrofit handles network requests to interact with the Spoonacular API, ensuring seamless communication between the app and the backend.

The app also integrates Room Database for local data storage, allowing users to store and retrieve their favorite recipes. LiveData is used to manage and observe UI-related data in a lifecycle-conscious manner, ensuring that UI updates are triggered automatically when data changes.

DBT - Tourist Attractions Full-Stack Web Application

DBT is a full-stack web application that allows users to explore and discover popular tourist attractions. Built using React for the frontend and Node.js/Express for the backend, the application connects to a PostgreSQL database hosted on Azure Cloud. The platform features a responsive design with Material-UI, secure JWT-based authentication, and a seamless user experience. It was deployed with Netlify for the frontend and Render for the backend, ensuring fast and reliable access to data for travelers.

Cost Manager - Smart Expense Tracking & Monthly Reports

The Cost Manager is a backend project designed to help users track and manage their expenses efficiently. Built with Node.js and Express.js for the backend and MongoDB Atlas for Cloud data storage, the project features a structured database with two collections—'users' for authentication and 'costs' for storing expense records. I developed a set of RESTful API endpoints using GET and POST requests to handle expense tracking, user authentication, and data retrieval. To test and document the API, I used Postman, ensuring smooth and efficient communication between the client and the server. Additionally, I implemented unit testing in Python to verify the core functionalities, improving code reliability and accuracy

Windows Checkers Game in C# and .NET Framework

This project is a Windows-based American Checkers game developed in C# with Windows Forms. The main goal was to create a structured and modular application that separates the game logic from the user interface, allowing future enhancements. The project follows an event-driven programming approach, using event handlers for game interactions. Technologies used include C#, .NET Framework, Windows Forms (WinForms), and Object-Oriented Programming (OOP) principles. This structured approach ensures that the interface can be upgraded easily, such as transitioning to a graphical or web-based version in the future.

Visual Programming of a Self-Checkout Machine

This project focuses on representing the functionalities and interactions of a self-checkout system through visual diagrams and models. Additionally, a graphical user interface (GUI) has been developed using Python, utilizing the generated code from the Statecharts to create an interactive interface for users to simulate the self-checkout process.

What makes a beer great? - Data Science project

As part of my data science course, I developed a project to explore factors that contribute to a beer's rating and predict its score. Using thousands of beer data entries, I conducted data scraping, handling, exploratory data analysis (EDA), and machine learning techniques to analyze and visualize key trends. The project culminated in building a machine learning model that predicts beer ratings based on specific features, with insights on what makes a beer highly rated.