gilbishkosma Goto Github PK

hello-github-actions

hello-hacktoberfest2k19

It is a repo in which anyone can contribute for a Hacktober Fest 2k19. It is starter repository in which you can start your open-source journey.

hover-preview

A vanilla js library to show preview images on hover

htmlprank

A chrome extension to easily change content of a website or to prank someone :smile:

illustratorhunt

One place to look for sites offering free illustrations

jstea

koa2-introduction

Introductory tutorial to Koajs web framework.

koatodolist

linkeep

live

Prism Live: Lightweight, extensible editable code editors. A work in progress, try it out at your own risk (and report bugs!) :)

login_page_assignment

material-ui

React components for faster and easier web development. Build your own design system, or start with Material Design.

nativebase-hackathon

nativebasefork

Mobile-first, accessible components for React Native & Web to build consistent UI across Android, iOS and Web.

open-pixel-art

A collaborative pixel art project to teach people how to contribute to open-source

personalblog

I write stuffs related to django, react, javascript and slice of life

pokemoncart

A single page pokemon shopping cart. Deployed at:

portfolio

react-hooks

Learn React Hooks! 🎣 ⚛

react-otp-input

:heavy_check_mark: OTP Input Component for React

react-tailwind-boilerplate

Boilerplate of tailwind + create-react-app

recroassignment

scrapingmodules

These is a respository where i will save the scrapingmodules.I am learning how to scrape and all the codes i learn will be displayed in these repository.The codes are from the book "Web scraping with Python" by Ryan Mitchell"

sentiment-analysis-on-imdb-film-reviews

Sentiment Analysis is a popular Natural Language Processing (NLP) task which allows us to extract the overall opinion in a text. In this project, we will be performing Sentiment Analysis on some IMDB movie reviews, to classify the overall review as positive or negative. When dealing with text data, a prevalent issue is how to encode the words as a numeric feature that can be used to compute the output of a classification algorithm. Especially because words don’t naturally lend themselves to a numeric ordering, there have been many approaches on how to featurize a text. In this project, we will use the bag of words model, which uses the count of words in a text as a feature. We will begin by using logistic regression to perform this task, followed by a decision tree approach, and random forests models. We will tune the regularize and tune the parameters of each model and use AdaBoost Classifiers with our Decision Tree and Random Forest models as our base estimator. Finally, we will compare the performance of each model on our training and validations data sets.

sidepanda

SidePanda assignment

sih_back_new

simple-react-snippets

Simple React Snippets for VS Code that you will actually use

some-code-stuff

Contains code sample, which I used in my career to solve problems.

static-react-example

styled-jsx

Full CSS support for JSX without compromises