Name: Pratik Fandade
Type: User
Company: Vishwakarma Institute of Technology, Pune
Bio: Full Stack Developer
Twitter: pratikfandade
Location: Pune, Maharastra
Pratik Fandade's Projects
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Curated list of resources for college students
This is a MERN Stack Web App equipped with end-to-end encryption using CryptoJS and AES Algorithm.
A Static website developed using no-Stack JavaScript, mainly focused towards creating an online identity and help business develop a presence on web.
Roadmap to becoming a web developer in 2021
This is project a draggable list in react made using no libraries only eventHandlers in VanillaJS.
A markdown version emoji cheat sheet
The main task is to keep a track of the attendees of our events. Make a platform to ease the task of managing and keeping track of the participants attending the events. The database will have several events and for each event keep a track of contact details of the participants like name, email, etc. and their attendance for each slot. Events can have varying slots based on the duration of the event. On the first webpage, the admin should be able to mark the attendance of the participants according to their time slots. The second webpage will display the final table with the functionality to sort by the number of slots attended.
:books: Freely available programming books
Resources for Coding Interviews for Undergrads for Internships and FTE
This repository consists of my multiprogramming operating system project and assignment, shell scripting codes which I have done in S.Y. of Engineering
Using React.js, Node.js, Express.js, MongoDB
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
Vanilla JS TodoMVC with Cypress Tests
A system for the Doctors that enables them to segment multi class tumors like edemas, non-enhancing tumor and enhancing tumor in Specific Patients. It will be fused with ML possibilities to ease out the work of Doctors that inputs Brain MRI images. It will help to ease the work in absence of neurologist to segment what type of tumor the patients may have and start the treatment earliest as possible. It will help doctors in increasing productivity and ultimately saving lives. The model will allow the targeted individuals to Automate Medical Procedures. We will train a model from scratch collecting the datasets with the brain MRIโs and Classify as well as segment the type of brain tumor. Considering the situation of need, this system will enable doctors to classify the patients based on tumors they have using brain MRIโs. It will help doctors to increase their productivity considering easing the diagnosis procedure.