https://github.com/navdeep-g/jsm-2018

Joint Statistical Meeting 2018
https://github.com/navdeep-g/jsm-2018

cloud-computing h2o machine-learning r statistics

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Joint Statistical Meeting 2018

• Host: GitHub
• URL: https://github.com/navdeep-g/jsm-2018
• Owner: navdeep-G
• Created: 2018-07-19T21:15:08.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-05-25T15:41:24.000Z (over 5 years ago)
• Last Synced: 2024-11-06T03:54:59.054Z (3 months ago)
• Topics: cloud-computing, h2o, machine-learning, r, statistics
• Language: R
• Size: 16.2 MB
• Stars: 3
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Joint Statistical Meeting 2018

## Distributed Machine Learning with H2O

### Prerequisites for H2O

[H2O-3 Requirements](http://h2o-release.s3.amazonaws.com/h2o/rel-wolpert/9/docs-website/h2o-docs/welcome.html#requirements)

### Install H2O in R

```

# The following two commands remove any previously installed H2O packages for R.

if ("package:h2o" %in% search()) { detach("package:h2o", unload=TRUE) }

if ("h2o" %in% rownames(installed.packages())) { remove.packages("h2o") }

# Next, we download packages that H2O depends on.

pkgs <- c("RCurl","jsonlite")

for (pkg in pkgs) {

if (! (pkg %in% rownames(installed.packages()))) { install.packages(pkg) }

}

# Now we download, install and initialize the H2O package for R.

install.packages("h2o", type="source", repos="http://h2o-release.s3.amazonaws.com/h2o/rel-wright/3/R")

# Finally, let's load H2O and start up an H2O cluster

library(h2o)

h2o.init()

```

### Running R script

* The [R](https://github.com/navdeep-G/jsm-2018/blob/master/h2o_airlines.R) script is meant to be run in a multi-node (EC2 or cluster of CPU's) setup as the data used is quite large (airlines dataset)

* [Here](https://github.com/navdeep-G/h2o-ec2) is a guide to setup your own EC2 instance with the latest [H2O-3](http://h2o-release.s3.amazonaws.com/h2o/rel-wright/3/index.html)