Hello World! 👋
I am Eyad Kobatte. Full Stack Web Developer based in Reading, UK working in Meltwater. I build progressive web apps using the latest frameworks like Angular, React on the front end and Node.js on the backend
Check out this website to see some of my work. You can also check out my GitHub too. Feel free to drop me a message any time.
I have a blog called DeveloPanda where I write about web development
Projects
I build stuff for the web with anything JavaScript and I love doing it. Take a look at what I have done so far
Sudoku
A single page application that generates sudoku boards on the fly with three possible difficulty levels. All data is saved on the client side using storeon for state management so the application works perfectly offline.
Users can have rooms to compartmentalize easily. Other users can be added into rooms to share with only those that need to see it.
Sudoku is built using React, NextJS, Storeon, CSS Modules
Track Mate
A collaborative tool to build ideas and thoughts and share those ideas with the people that matter. Sign up using a social media platform and start collaborating with ease.
Users can have rooms to compartmentalize easily. Other users can be added into rooms to share with only those that need to see it.
Track Mate is built using amazing tools like Angular, Node.js, Express, MongoDB, Firebase Auth, Angular Material
Music Recommender
A Data Science Project which involved using the million-song dataset available on Kaggle and building a recommender engine based on the previous listening history of users. The recommender engine was built on an item-item based collaborative filtering to suggest new songs to the User.
A front-end was built so the user could log in and new music would be displayed alongside the listening history.
This whole project was built on Python, pandas, Flask and Celery
Color Decider
A Machine Learning Project which generated a random background color. The algorithm would predict a better foreground color for the randomly generated background color. This algorithm could be used in many scenarios where the background color would be unknown and this algorithm would predict a foreground color which is readable to the eyes of the user. This algorithm was based on K-Nearest Neighbors Algorithm.
Completely built using Python with scikit-learn to use the machine learning algorithm. The front end was built on Flask.