Discover @tanishq9 Open Source projects

Tanishq Saluja (@tanishq9)

tanishq9

Stars
30
Global Rank 491,990 (Top 17 %)
Followers 38
Following 47
Registered almost 7 years ago
Most used languages

Java
33.3 %

Jupyter Notebook
26.7 %

JavaScript
13.3 %

HTML
6.7 %

Kotlin
6.7 %

Dockerfile
6.7 %

Python
6.7 %
Location 🇮🇳 India
Country Total Rank 21,220
Country Ranking

Dockerfile
272

Kotlin
2,029

Jupyter Notebook
3,511

Java
4,294

Text-Classification-20-Newsgroups

The dataset is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups. • Builded vocabulary from the dataset which was used as a feature set. • Implemented Multinomial Naive Bayes classifier from scratch for classifying news into appropriate group.

Jupyter Notebook

Data-Structures-and-Algorithms

Covers majority of Data Structures and Algorithms questions from various online judges.

IPyNotebooks

Implementation of various Machine Learning Algorithms & Techniques.

Jupyter Notebook

Kotlin-ToDo-Application

ToDo Notes Application in Kotlin.

URL-Shortener

URL Shortener with Node.js and MongoDB. Deployed on : https://g-z.herokuapp.com/

MNIST-NodeJS

p5js canvas used for sketching and keras used for prediction. Deployed on : https://live-mnist.herokuapp.com/

QnA-System-Android-Application

BERT model is used to build a Question-Answering system which answers user’s question using the content and questions file uploaded by them to the server for processing.

Safest-Route

Android Application and Backend Code for Hackathon Project developed at MS Hack 2019.

RestAPI-GitHubUsers

Using JSON parsing in Java for showing data related to GitHub Users using GitHub's REST API.

QnA-System

Jupyter Notebook

GraphQL-Playground

flask-deploy

Sample Flask App used as a microservice for classifying digits.

k8s-Notes

Movie-Reviews-NLP

The movie_reviews corpus contains 2K movie reviews with sentiment polarity classification.Here, we have two categories for classification. They are: positive and negative. The movie_reviews corpus already has the reviews categorized as positive and negative.Using NLTK , we can predict whether a review is positive and negative.

Jupyter Notebook

WebD-Node.js

Basics of HTML & CSS, Inheritence rules, usage of selectors, media-queries, keyframes, bootstrap, creating and using npm packages, using ExpressJS for creating servers, handlebars for rendering in case of JS disabled pages and jQuery for making AJAX calls in case of JS enabled pages, MySQL for data persistence, Sequelize for ORM (Object Relational Mapping), REST APIs, Socket.IO for real-time messaging, MongoDB and mongoose for using NoSQL database, Authentication using PassportJS, Using Python scripts on NodeJS and running custom sklearn, tensorflow or keras pre-trained model in our backend.