Regression Analysis & Prediction

Devon JonesLead Systems Engineer, Knewton

Gabe HamiltonSoftware Engineering Mgr, Revionics

For those who work downtown, check out our DOSUG inspired group.

meetup.com/TechConfluence

3rd Wednesday of the month

At lunch: 12:30 - 1:30pm

Tech Confluence

The Plan

1. Regression Analysis - Devon

2. Google Prediction API - Gabe

3. Applying Regression - Devon

Google Prediction API

{ "label": "awesome", "score": 0.98 },

{ "label": "lame", "score": 0.08 }

Gabe Hamilton

What kind of Prediction?

Predict an output value based on some input values.

Things like:

Sentiment Analysis, Spam Detection, Today's temperature, GDP Growth

How does Google predict things?

Through an intensive breeding program Google has managed to distribute Punxsutawney Phils throughout its datacenters across the world. Each Phil is kept in a climate controlled enclosure that mimics the conditions of a perfectly average February 2nd. A full scale digital sundial maps your problem domain onto the shadow matrix of the enclosure allowing each Phil to fully interact with your model. The early spring / long winter emergence probability of each Phil is then sorted and reduced to determine the final result returned by the prediction API.

Well, it's Google

No Really, How do they do it?

Short Answer:I have no idea

Long answer:It's a service, they can do whatever works, swap implementations run multiple algorithms

Possible Implementations

Regression AnalysisNeural NetworksSupport Vector MachineMonte Carlo SimDecision TreesEvolutionary Algorithms

Basically it is

STATISTICS

Types of Prediction you can do

Regression

How do inputs cause an output to vary?

Output is a numeric value: Shopping Cart Size Stock Price

Classification

Deciding which bucket some input belongs in

Buckets are text values: French, Spanish, English

What is Classification good for?

Classification

● Sentiment analysis● Spam detection● Language categorization● Tagging● Assign priority to bugs● Predict movie ratings● Message routing decisions● <Your brilliant idea here>

Hello World page is great

https://developers.google.com/prediction/docs/hello_world

Getting Started

So you have a big pile of data

Time for some cleanup

90% of the development time is data cleanup

Good talk on data driven projectshttp://www.slideshare.net/ryanweald/building-data-driven-products-with-ruby-rubyconf-2012

CSV Input file aka Training Set

First column is expected values.

2nd through N columns are input values

"French", "Je pense donc j'essuie", "Paris"

Output an input more input

No header columns 250MB max file size

1. Create a CSV file of your training data2. Create a new Project in the Prediction API

a. requires entering billing info3. Upload your csv file to Google Storage4. In Prediction API Browser:

a. insert a new training set (the csv file)b. view your trained setc. use trainedmodel.predict to make

predictionsSee the hello world for details of the method calls

4 Steps to Prediction

Let's make some predictions...

Live demo screenshots: List Models

Live demo screens: Analyze Model

Live demo: Predict Model Category

Live demo: Predict Model Numeric

Storage for datasets

https://storage.cloud.google.com

API Explorerhttps://developers.google.com/apis-explorer/#s/prediction/v1.6/