Utkarsh Sharma (@skhiearth)
github

skhiearth

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  • Stars
    star
    49
  • Global Rank 344,157 (Top 12 %)
  • Followers 49
  • Following 7
  • Registered over 6 years ago
  • Most used languages
    Python
         47.6 %
    Solidity
         23.8 %
    JavaScript
         14.3 %
    Swift
         9.5 %
    R
         4.8 %
  • Location 🇮🇳 India
  • Country Total Rank 12,942
  • Country Ranking
    Solidity
         77
    Swift
         789
    R
         860
    Python
         2,989

Top repositories

1

Coursera-IBM-Machine-Learning-with-Python-Final-Project

The following algorithms are used to build models for the different datasets: k-Nearest Neighbour, Decision Tree, Support Vector Machine, Logistic Regression The results is reported as the accuracy of each classifier, using the following metrics when these are applicable: Jaccard index, F1-score, Log Loss. This project counts towards the final grade of the course.
Python
17
star
2

Pink-iOS

Source Code and linked assets for the Pink+ project by The Misfits
Swift
3
star
3

VacSeen

Code repository and documents for VacSeen by The Misfits
Solidity
3
star
4

Vehicle-Detection-using-Intel-OpenVINO

An implementation of a cost-effective, real-time vehicle detection application on the edge using the OpenVINO toolkit.
Python
2
star
5

Traffic-Sign-Classification-using-Keras

Implementation of CNNs in Keras to recognize traffic signs from the German Traffic Sign Dataset
Python
2
star
6

International-Debt-Statistics

Analysing international debt of various countries using data from International Monetary Fund.
Python
2
star
7

NYC-Motor-Vehicle-Collisions

Analysing and visualising Motor Vehicle Collisions in New York City with an objective to make the city roads safer using Data Science techniques.
Python
2
star
8

Bullz-and-Bearz-PeddieHacks

Code repository and supporting documents for Bullz and Bearz
Swift
2
star
9

VirtualTrack

Code snippets and supporting documentation for the VirtualTracker project built on Matic and Portis by The Misfits.
Solidity
2
star
10

BlockCred

Code Snippets and Supporting Documentation for the BlockCred project by The Misfits for Code with GlobalShala 2.0
JavaScript
1
star
11

Blockchain_Dev_SocialNet

Notes and the Social Network project from 'Blockchain Tutorial For Developers' by Dapp University
JavaScript
1
star
12

Go-Tutorial

Notes and code snippets from the freeCodeCamp.org tutorial on Golang.
1
star
13

VirtualTracker

Code snippets and supporting documentation for the VirtualTracker project built on Matic and Portis by The Misfits.
Solidity
1
star
14

GreenFunds

Code repository and supporting documents for GreenFunds project by the Misfits for E-Hack '21
Solidity
1
star
15

Word-Frequency

Analysing the word frequency in documents and books off Project Gutenberg and verifying the Pareto Principle in language and literature.
Python
1
star
16

Web3.js-Ethereum-Developer

Code snippets from the YouTube playlist, 'Web3.js - Ethereum Blockchain Developer Crash Course' by Dapp University
JavaScript
1
star
17

skhiearth

1
star
18

cifar-cnn-pytorch

Training a Convolutional Neural Network to classify images from the CIFAR-10 database using PyTorch
Python
1
star
19

BlockStore

Code repository, UI elements and supporting documents for the BlockStore project by The Misfits at BhilaiHacks
Solidity
1
star
20

Level-Difficulty-in-Candy-Crush-Saga

Candy Crush Saga is a hit mobile game developed by King (part of Activision|Blizzard) that is played by millions of people all around the world. In this DataCamp Project, I worked with a real Candy Crush dataset and used this data to estimate level difficulty.
R
1
star
21

Generating-Keywords-for-Google-Ads

Automatically generate keywords for a search engine marketing campaign using Python. Inspired from a DataCamp project of the same name.
Python
1
star
22

Multiple-Linear-Regression-Backward-Elimination-Python

Implementing a Multiple Linear Regression in Python using Backward Elimination
Python
1
star
23

Upper-Confidence-Bound-Thompson-Sampling

Optimizing click-through-rate of different users on ads in a Social Network using Upper Confidence Bound and Thompson Sampling in Python and R
Python
1
star