smithashivakumar Goto Github PK
Name: ShivSmit
Type: User
Bio: Committing to git
Name: ShivSmit
Type: User
Bio: Committing to git
In the spirit of the conference, this meetup will feature a series of short talks on practical applications with R!
Repository for Functional Programming
A JavaScript / Python / PHP cryptocurrency trading library with support for more than 100 bitcoin/altcoin exchanges
This is where programming becomes awesome.
Apps hosted in the Dash Gallery
Submit and share your resources and ideas in the wiki for Baseball Hack Day!
Tutorials walking new programmers through the process of building their first website in Python and Django
Team CPTDS
Securities and Exchange Commission (SEC) EDGAR database which contains regulatory filings from publicly-traded US corporations.
Roman Emperors from 26 BC to 395 AD
Find XBRL filings on the SEC's Edgar and extract accounting metrics.
Shiny App
My new repository
A few samples from Henry Carsten's book '101 Trading Ideas'
101
Leetcode and hackerrank
Machine learning library written in readable python code
Neural Net exercises
Python code for common Machine Learning Algorithms
Machine learning for beginner(Data Science enthusiast)
This is a comprehensive Exploratory Data Analysis for the [Personalized Medicine: Redefining Cancer Treatment](https://www.kaggle.com/c/msk-redefining-cancer-treatment) challenge. Using *ggplot2* and the *tidyverse* tools to study and visualise the structures in the data. Challenge to automatically classify genetic mutations that contribute to cancer tumor growth (so-called "drivers") in the presence of mutations that don't affect the tumors ("passengers"). The [data](https://www.kaggle.com/c/msk-redefining-cancer-treatment/data) comes in 4 different files. Two csv files and two text files: - *training/test variants:* These are csv catalogues of the gene mutations together with the target value *Class*, which is the (manually) classified assessment of the mutation. The feature variables are *Gene*, the specific gene where the mutation took place, and *Variation*, the nature of the mutation. The test data of course doesn't have the *Class* values. This is what we have to predict. These two files each are linked through an *ID* variable to another file each, namely: - *training/test text:* Those contain an extensive description of the evidence that was used (by experts) to manually label the mutation classes. The text information holds the key to the classification problem and will have to be understood/modelled well to achieve a useful accuracy.
Maximum Likelihood Estimation using Multivariate Diffusion
Maximum likelihood estimators for multi-variate diffusions
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.