🌟 Data whisperer
📊 Data Scientist with an M.Sc. in Data Science from BGU
💬 Drop me a line about any of my repos – I'm all ears and love chatting about my projects!

In this project, I conducted a comprehensive analysis, both quantitative and qualitative, of ChatGPT's performance in hate speech and target identification. This study employed three distinct prompting techniques: zero-shot, one-shot, and few-shot.

In this project, I aim to conduct exploratory data analysis on the Age dataset by preprocessing, augmenting with additional sources, visualizing data, performing sentiment analysis, and exploring correlations with events.

Ben Gurion University "Deep Reinforcement Learning (372.2.5910)" course assignments & solutions

Ben Gurion University "Deep Learning (372.2.6101)" course assignments & solutions

Ben Gurion University "The Art of Analyzing Big Data - The Data Scientist’s Toolbox (372.2.5401)" course assignments & solutions

Ben Gurion University "Machine Learning (372.2.5214)" course assignments & solutions

In this project, I developed a full-stack web application tailored for watching and managing Super League games. I used three tier pattern with REST API architecture and Azure SQL DB.

• Backend: Creating an API to interact with the sportmonks platform, where all communication with the platform occurs server-side via the API. This involves implementing all server-side API requests, including routing, middleware, and proper organization into classes. The backend handles all server-side logic for validation and communication.
• Frontend: Developing the client-side using the Vue.js platform, focusing on enhancing user experience in terms of visibility and ensuring correct retrieval of information during user login. Additionally, structuring and styling the application's appearance using packages from the Bootstrap site to improve overall usability.

This project integrates HTML for structure and design, JavaScript for functionality, and jQuery for user management.

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In this project, I bulid a Search engine, implementing Parser, Indexer, Searcher, and Ranker components, and maintain a large set of data. Additionally, I implemented five differenet retrieval methods using: Word2Vec, Global Method, Spelling Correction, Thesaurus and Word Net - and evaluated their effectiveness using MAP, Precision, and Recall metrics.

The game was developed in multiple phases, incorporating various design patterns such as Strategy and Observer.

• In the initial phase, I utilized Prim's algorithm to construct the maze and implemented three distinct search methods—BEST-FIRST SEARCH, BFS, DFS—to provide users with a solution.
• During the second phase, I implemented bit compression to optimize storage and employed a client-server architecture with thread management for efficient time handling.
• In the final step, emphasis was placed on enhancing the frontend, refining game design, and improving user experience through intuitive GUI enhancements.