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I'm a 21-year-old boy pursuing computer science engineering. I love learning and exploring new skills :) Github Image

  • 🔭 I’m a Full Stack developer
  • 🌱 I’m currently exploring Machine Learning
  • 🤔 I’m looking to collaborate on good web development and ML projects
  • 😗 Fun fact about me: My average typing speed is 80 WPM :/
  • 💬 Not sure if I am good at programming or good at Googling :')
  • 😄 Pronouns: He/Him
  • My leetcode profile: Divyam6969
garbinmarcelo

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Divyam 's Projects

actearly icon actearly

ActEarly is a child development milestone application created during the Mitacs Globalink Research Internship for Canadian government hospitals. It helps monitor and track children's developmental milestones, providing valuable insights and support to parents and healthcare professionals.

flutter-awesome-calculator icon flutter-awesome-calculator

It is a responsive calculator that can work in any angle. I saw the design on a youtube channel called Mitch Koko, I tried to edit it as clean code.

getting-and-cleaning-data-course-project icon getting-and-cleaning-data-course-project

This repository contains the code and data for the Getting and Cleaning Data Course Project, a Coursera course offered by Johns Hopkins University. The purpose of the project is to demonstrate your ability to collect, work with, and clean a data set. The goal is to prepare tidy data that can be used for later analysis. :)

heart-disease-prediction icon heart-disease-prediction

Application made using Flask that runs on a ML Model trained using random forest classification model that helps in prediction of heart disease

ignite icon ignite

React based Campus Placement Web Site for ignite @ Delhi University

mashup-project icon mashup-project

Mashup Creator is a cutting-edge web application that seamlessly blends tracks from YouTube videos to produce personalized, high-quality music mashups tailored to each user's preferences.

multithreading-102103142 icon multithreading-102103142

The 'Matrix Multiplication Performance Benchmark' project evaluates the impact of multi-threading on matrix multiplication efficiency. Using Python libraries like NumPy and threading, it measures computation time and CPU utilization, providing insights for optimizing computational tasks.

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