Ian ionox0

#Spark Assignment 3

This assignment is based on the 3rd chapter of the book - 'Advanced Big Data Analytics with Spark'. You can find a copy of the book here.

In this assignment, you will be required to build a recommendation system using Spark and MLib using a dataset published by AudioScrobbler. This data is 500MB uncompressed and can be downloaded here.

You can glance through the chapter and understand how to build the recommendation system. The chapter will walk you through Scala code;

In case anyone wants, here’s some links I’ve used for getting started with React:

React Notes:


http://mern.io/ 
http://redux.js.org/
https://webpack.github.io/

	Starting from:
	Ubuntu 14.014 Server, amd64
	30 GB SSD EBS storage
	ubuntu/images/hvm-ssd/ubuntu-trusty-14.04-amd64-server-20170405 (ami-772aa961)




	sudo apt-get update
	sudo apt-get -y upgrade

	var emails = ['[email protected]', '[email protected]', '[email protected]', '[email protected]', '[email protected]']

	function checkEmail(i){
	email = emails[i];
	a = document.getElementsByClassName('vO')[0];
	console.log(a);
	a.focus();
	a.value = email;
	console.log("Found divs: ", a, email);
	document.getElementsByClassName('nM')[0].focus();

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	const defaults = {
	tags: [{ id: 1, text: "Apple" }],
	suggestions: ["Banana", "Apple", "Apricot", "Pear", "Peach"],
	handleAddition: noop,
	handleDelete: noop,
	handleDrag: noop
	}

	const DOWN_ARROW_KEY_CODE = 40;

	https://github.com/papers-we-love/papers-we-love
	https://github.com/darcyclarke/Front-end-Developer-Interview-Questions
	https://github.com/sorrycc/awesome-javascript
	https://github.com/cjbarber/ToolsOfTheTrade
	https://github.com/Puppets
	http://calv.info/node-and-express-tips/
	http://calv.info/an-introduction-to-nodes-new-streams/
	http://scottksmith.com/blog/2014/07/02/beer-locker-building-a-restful-api-with-node-oauth2-server/
	https://en.wikipedia.org/wiki/Stream_processing
	https://www.thc.org/root/phun/unmaintain.html

	from pandas.tools.plotting import scatter_matrix
	scatter_matrix(df[continuous_cols], alpha=.2, figsize=(12, 12), diagonal='kde')
	plt.show()