smithashivakumar Goto Github PK

100daysofcommits.github.io

2018-n5--pre-rfinance-meetup

In the spirit of the conference, this meetup will feature a series of short talks on practical applications with R!

anly560smitha

Repository for Functional Programming

ccxt

A JavaScript / Python / PHP cryptocurrency trading library with support for more than 100 bitcoin/altcoin exchanges

chipy-flaskr-joke

chipymentorship

codingworkshops

This is where programming becomes awesome.

credit-risk-modelling-using-ml

dash-sample-apps

Apps hosted in the Dash Gallery

data-and-resources

Submit and share your resources and ideas in the wiki for Baseball Hack Day!

django-carrots

Tutorials walking new programmers through the process of building their first website in Python and Django

dmc-hackathon

Team CPTDS

edgar

Securities and Exchange Commission (SEC) EDGAR database which contains regulatory filings from publicly-traded US corporations.

emperors

Roman Emperors from 26 BC to 395 AD

financial_fundamentals

Find XBRL filings on the SEC's Edgar and extract accounting metrics.

happinessquotient

Shiny App

hello-world

My new repository

henrycarstens-101-trading-ideas

A few samples from Henry Carsten's book '101 Trading Ideas'

https-github.com-yhilpisch-py4fi

java

101

kaggle-example

leadconversion_logreg

leetcode-datastructures-and-algorithms

Leetcode and hackerrank

machine-learning

Machine learning library written in readable python code

machine-learning-ii

Neural Net exercises

machine-learning-with-python

Python code for common Machine Learning Algorithms

machinelearning

Machine learning for beginner(Data Science enthusiast)

medicaldata

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.

mlemv

Maximum Likelihood Estimation using Multivariate Diffusion

mlemvd

Maximum likelihood estimators for multi-variate diffusions