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estimatePi <- function(numDraws){
r <- .5 #radius... in case the unit circle is too boring
x <- runif(numDraws, min=-r, max=r)
y <- runif(numDraws, min=-r, max=r)
inCircle <- ifelse( (x^2 + y^2)^.5 < r , 1, 0)
return(sum(inCircle) / length(inCircle) * 4)
}
  1. General Background and Overview
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"About Challenge - Data science challenge is a regression problem where \"target\" prediction is to be done on test data set of 1000 records. Training set is consist of 5000 records with 254 features + 1 target.\n",
"\n",
"Data Files - \n",
"Training File (codetest_train.txt) - 5000 records with 254 features + 1 target\n",
@hanifmahboobi
hanifmahboobi / README.md
Created November 4, 2017 02:41 — forked from leonardofed/README.md
A curated list of AWS resources to prepare for the AWS Certifications


A curated list of AWS resources to prepare for the AWS Certifications

A curated list of awesome AWS resources you need to prepare for the all 5 AWS Certifications. This gist will include: open source repos, blogs & blogposts, ebooks, PDF, whitepapers, video courses, free lecture, slides, sample test and many other resources.

For more about AWS and AWS Certifications and updates to this Gist you should follow me @leonardofed


@hanifmahboobi
hanifmahboobi / reinvent-2017-youtube.md
Created December 7, 2017 00:36 — forked from stevenringo/reinvent-2017-youtube.md
Links to YouTube recordings of AWS re:Invent 2017 sessions

| Title | Description

@hanifmahboobi
hanifmahboobi / role_arn_to_session.py
Created January 20, 2018 01:21 — forked from gene1wood/role_arn_to_session.py
Simple python function to assume an AWS IAM Role from a role ARN and return a boto3 session object
import boto3
def role_arn_to_session(**args):
"""
Usage :
session = role_arn_to_session(
RoleArn='arn:aws:iam::012345678901:role/example-role',
RoleSessionName='ExampleSessionName')
client = session.client('sqs')
"""