Hari Vamsi

@harivamsi9

Clean Code - Python, Java, C++, GoLang, Rust; Converts Coffee to Code, and bug to features 😉;

University of California San Diego United States

Followers

Following

Public Repos

Private Repos

Language Breakdown

Lines of code distribution across 50 owned repositories

14.0M Total LOC

Jupyter Notebook

11,735,716 lines

84.1%

N/A

HTML

1,167,364 lines

8.4%

N/A

C++

510,290 lines

3.7%

N/A

Python

177,573 lines

1.3%

N/A

158,555 lines

1.1%

N/A

Other

208,935 lines

1.5%

N/A

I-Shaped Developer

I-shaped

Specialist — deep expertise in Jupyter Notebook

Jupyter Notebook

HTML

C++

Python

Collaboration Network

Global Impact visualization

LIVE

0 active collaborators

Repos

PRs

Growth

+18%

Top Collaborators

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Coding Streak

Contribution activity over the past year

367 days

751

Contributions

751

Commits

Pull Requests

Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun

Based on GitHub activity

Less

Followers 19

Anjum Rashid

@bijoy26

S4IL

@S4IL21

Trick Munyi

@MR-MUNYI

Ivy🗝️🧬!!

@1vyRoses

Cogito Ergo Sum

@standardgalactic

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Following

71 total

Oleksandr Redko

@alexandear

Marcelo Trylesinski

@Kludex

Dariusz Jędrzejczyk

@chemicL

Ashwin Bhat

@ashwin-ant

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@Soypete

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Synced via GitHub

Top Repositories

git-greener

4 1

Shell

Advanced-Lane-Finding-using-OpenCV-for-Self-Driving-Cars

2 0

Jupyter Notebook

PrairieLearn

Online problem-driving learning system

1 0

TypeScript

Fault-Tolerant-Scalable-SurfStore-Drop-box

• Designed a distributed cloud-based networked file storage service (SurfStore)based on Dropbox which syncs files toand from the “cloud” where client and server interactions are done with gRPC and implemented TCP/IP connections to listen and serve multiple clients with go routine

1 0

TCSion_MODEL_01

The amount of data that is produced in the past decade is much greater than the total of the data produced before that from the time of invention of the computer or the internet. Each day, trillions of terabytes of data is being produced and consumed. And in the online retail reviews, or even our particular case of interest i.e. online movie reviews sentiment analysis. There are so many ways that a reviewers can give a feedback about a movie. And each reviewer based on his interests give different kinds of feedbacks which can be positive, negative, neural, and sometimes extreme. It’s very hard for someone to monitor such comments, and also it is of importance to know the talk of the each movie for better analytics. And manually performing this task is extremely tedious, and time consuming. Our project focuses on automating this aspect, by performing the sentiment analysis on the movie reviews data so we classify each review as positive, negative, neutral. We can use this project in various aspects, either as just how it is to know the nature of the review, or this could be a sub module for building the a recommendation system, or personality analyzer.

1 0

Jupyter Notebook

Finding-Lane-Lines-using-OpenCV-for-Self-Driving-Cars

DSA in CPP Practice

golang-python-performance

0 0

Python