I'm a machine learning and AI enthusiast with a strong passion for solving problems as well as how machine learning works and testing the core fundamentals to enhance my understanding of it!
I've developed a fully functioning Klondike card game in Python, emphasizing my adept use of Object-Oriented Programming (OOP) principles. The game adheres to classic Klondike rules, with an intuitive graphical interface. Leveraging OOP, the code is modular and well-organized, showcasing my commitment to clean, maintainable design.
This movie recommender project harnesses the power of machine learning through libraries like scikit-learn for cosine similarity computations and TfidfVectorizer for feature vectorization of genres, keywords, and tags. Leveraging these tools, the system provides personalized movie recommendations based on users' selected preferences. The project is built upon Python and deployed seamlessly using Streamlit, offering an intuitive and interactive user interface for exploring and discovering a curated selection of movies tailored to individual tastes and interests. This project also selects movies with an relatively good accuracy selecting movies with a good fit to what the user input was.
This Parking Lot project is a Python-based system leveraging OpenCV to efficiently discern available parking spaces. Using computer vision techniques, the program detects vacant and occupied spots within a parking lot. Open spaces are highlighted with green lines, while occupied spaces are marked with red lines, providing a visual indication of their status. Additionally, the system counts the number of free spaces available, displaying this information to users. By combining image processing capabilities with color-based recognition, the project optimizes parking lot management, aiding drivers in quickly identifying available parking spots.