Discover @geekygirl8 Open Source projects

Nikita (@geekygirl8)

geekygirl8

Stars
76
Global Rank 252,515 (Top 9 %)
Followers 20
Following 7
Registered about 5 years ago
Most used languages

HTML
45.5 %

JavaScript
27.3 %

CSS
9.1 %

C++
9.1 %

Jupyter Notebook
4.5 %

Python
4.5 %
Location 🇮🇳 India
Country Total Rank 7,904
Country Ranking

C++
782

HTML
2,407

CSS
5,184

Hacktoberfest_2022_DSA

Hacktoberfest2022 🥳- Contribute Any Project or Algorithm In Any Language😎 Every Valid PR will be accepted. Happy Contribution

OIBGRIP

Here all 3 tasks of level-2 of Web Developement and Desigining internship of Oasis Infobyte are available.

Facetime

A multi-user real time video calling and chatting application.

hackerrank

In this repository, one can contribute solutions to HackerRank problems in whichever language is comfortable such as c,c++, java, python, javascript...

RESTfulAPI

google-cloud

myportfolio

My personal portfolio

Spaceliner

Aabhaar

This a static single page website build using HTML,CSS and JavaScript

LGMVIP-WebDev

LGMVIP Web Development Internship tasks repository

geeky_girl.github.io

A simple portfolio website for beginners which can be edited specifically for each one.

CC-JAN-WEB_DEVELOPMENT

netflix-clone

myportal.github.io

MovieStar

geekygirl8

FutureReadyTalentProject

This repository contains the details of future ready talent program organized by Microsoft, E&Y, Future Skills Prime, Nasscom, Quess and Github collaboration.

bmicalculator.github.io

Cakesmith

BlockRacer

It's a simple Javascript game developed by me on the Mimo platform. It is compatible with all devices.

worldpeacegifts.github.io

github-pages-with-jekyll

hello-world

Introduction repository on Github

portfolio

My personal portfolio

js-snake-game

A simple and fun javascript-based snake game.

github-slideshow

A robot powered training repository 🤖

Movie-Review-Sentiment-Analysis

To classify the sentiment of sentences from the Rotten Tomatoes dataset. Labeled phrases on a scale of five values: negative, somewhat negative, neutral, somewhat positive, positive and then predict the overall sentiment of the sentence using Naiive Bayes and Decision Tree Algorithms

Jupyter Notebook