Mihai Miron

Sentiment Analysis Web Application 

This project is a Sentiment Analysis Web Application that predicts the sentiment of a given text (positive, neutral, or negative). It consists of two main parts: 

1. Backend: A Flask-based API that handles text processing, sentiment prediction using a deep learning model (Keras/TensorFlow), and TF-IDF vectorization for text features.

 2. Frontend: A React-based web interface where users can input text and receive sentiment analysis results from the backend. 

Link: https://github.com/Dackohn/ExcaliburAiProject

Dating App 

Developed an AI-powered dating application designed to match users based on personal traits and preferences. The project includes a user profile system, compatibility matching algorithms (including collaborative filtering and sentiment analysis), and a simple interface for interaction. Implemented using Python (pandas, scikit-learn), with a focus on data processing, recommendation systems, and user experience. 

Link: https://github.com/mihaimiron1/Dating_app

Florist Shop 

Designed and implemented a console-based application for managing a florist shop using Object-Oriented Programming in C++. The system handles flower inventory, promotional strategies, customer interactions, and EDUCATION AND TRAINING PROJECTS discount mechanisms. Focused on applying OOP principles such as inheritance, polymorphism, operator overloading, and encapsulation to simulate a real-world store environment. 

Link https://github.com/mihaimiron1/OOP_Project_Florist_Shop

Android App 

Developed an Android application that integrates with the Metropolitan Museum of Art public API to display a collection of artworks. The app features a search bar for finding objects by keyword and a local database for managing a personalized list of favorite items. Built using Java and Android Studio, the project demonstrates skills in RESTful API integration, RecyclerView usage, local data persistence, and clean UI design. 

Link https://github.com/mihaimiron1/android_app