An Overview of Predictive Analytics for Practitioners. Dean Abbott, Abbott Analytics

An Overview of Predictive Analytics for

Practitioners

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Dean Abbott

•

Co-founder and Chief Data Scientist at

SmarterHQ, based in Indianapolis, Indiana

•

President of Abbott Analytics in San Diego,

California

•

Internationally recognized data mining and

predictive analytics expert with over two

decades’ experience

•

Author of

Applied Predictive Analytics (Wiley,

2014), co-author of IBM SPSS Modeler

Cookbook (Packt Publishing, 2013).

•

Advisory board and instructor for UC Irvine

Speaker Social Media

The Analyst’s Journey

Gain critical business and data

analytics skills

Uncover insights and provide valueto

your organization

Put your

knowledge to use immediately

An Overview of Predictive Analytics for

Practitioners

What do Predictive Modelers do?

The CRISP-DM Process Model

•

CR

oss-

I

ndustry

S

tandard

P

rocess

M

odel for

D

ata

M

ining

•

Describes Components of

Complete Data Mining Cycle

from the Project Manager’s

Perspective

•

Shows Iterative Nature of Data

Mining

Business

Understanding Data Understanding

Data Preparation Modeling Evaluation Deployment Data Data Data

CRISP-DM:

Business Understanding Steps

•Ask Relevant Business Questions

•Determine Data

Requirements to Answer Business Question

•Translate Business Question into Appropriate Data Mining Approach

•Determine Project Plan for Data Mining Approach

Define Business Objectives Background Business Objectives Business Success Criteria Assess Situation Inventory of Resources Requirements, Assumptions, Constraints Risks and Contingencies Terminology

Costs and Benefits Determine

Data Mining Objectives

Data Mining Goals Data Mining Success Criteria

Produce

Project Plan Project Plan

Initial Assess-ment of Tools & Techniques

Objective’s

•

Business objective:

•

Random test mailing to NRA’s house file achieved a 11% response rate

•

Need a model that finds population with a minimum response rate of

13.5% to be profitable

•

Modeling Objectives:

•

Develop a binary outcome model that will rank-order current database

based on propensity to respond to traditional mailing, optimizing at a

cumulative average response rate of >= 13.5%.

CRISP-DM Step 2:

Data Understanding Steps

•

Collect initial data

• Internal data: historical customer behavior, results from previous

experiments

• External data: demographics & census, other studies and

government research

• Extract superset of data (rows and columns) to be used in

modeling

• Identify form of data repository: multiple vs. single table, flat file

vs. database, local copy vs. data mart

•

Perform Preliminary Analysis

• Characterize Data (describe, explore, verify) • Condition Data Collect Initial Data Initial Data Collection Report Describe Data Data Description Report

Explore Data Data Exploration Report

Verify Data Quality

Data Quality Report

Source Data

•

Business partner provided data that summarizes transactional

data for every active NRA member - 49

independent

variables.

•

TN Marketing enhanced the database with demographic

data-18

appended

variables.

•

I-Miner was used to derive new variable features and

transformations of pre existing data points - 79

derived

CRISP-DM Step 3:

Data Preparation (Conditioning) Steps

Select Data Rationale for Inclusion/Exclusion

Clean Data Data Cleaning Report

Construct

Data Derived Attributes

Generated Records

Integrate

Data Merged Data

Format Data Reformatted Data

Fix Data Problems

Data Preparation

•

Key transformations

•

Date Features

•

Filling missing data

• Use “Distribution” when possible for numeric fields • Use Constant for categoricals

• For numeric data with both “in-house” and third-party versions, use in-house when available,

Data Size

•

Original Data

•

Data after data cleanup

and feature creation

•

Data after further cleanup,

and adding interaction

terms

CRISP-DM Step 4:

Modeling Steps

Select Modeling

Techniques _TechniquesModeling _AssumptionsModeling

Generate Test

Design Test Design

Build Model _{Parameter Settings Models}

Revised Parameter Model Description

Algorithm Selection

Sampling

Algorithms

Model Ranking

Sampling

•

Randomly split the 21,557 records into two data sets, training and validation

•

Build response model on training data set: 10,778 records

•

•

Validate model by scoring test data set: 10,779 records

•

Ideally, have a third held out data set to provide final assessment of

Classifiers Find Different Decision Boundaries

11-Nearest Neighbor Neural Network

Naïve Bayes Logistic Regression Decision Tree Actual Data

How to deploy model?

Software, source code, in database

How often, when to update

model

Report results

Lessons learned

Plan Deployment _{Deployment Plan}

Plan Moni-toring and Maintenance

Monitoring & Maintenance Plan

Produce Final

Report Final Report Final Presentation

Review Project Experience

CRISP-DM Step 6:

Deployment Steps

Model Results after Deployment

•

Scored over 2,100,000 prospects

•

Actual results from the rollout

•

Average response rate = 13.67%

What is Predictive Analytics?

Simple Definitions

•

Data driven

analysis for [large] data sets

•

Data-driven to discover input combinations

•

Data-driven to validate models

•

OR

•

Discovering interesting patterns in data

automatically

from the

data

•

Input variables are selected automatically

Customer Analytics: BI vs. PA

Customer Analytics: Business Intelligence

■ What were the e-mail open, click-through, and response rates?

■ Which regions/states/ZIPs had the highest response rates?

■ Which products had the highest/lowest click-through rates?

■ How many repeat purchasers were there last month?

■ How many new subscriptions to the loyalty program were there?

■ What is the average spend of those who belong to the loyalty program? Those who aren’t a part of the

Customer Analytics for Predictive Analytics

■ What is the likelihood an e-mail will be opened?

■ What is the likelihood a customer will click-through a link in an e-mail?

■ Which product is a customer most likely to purchase if given the choice?

■ How many e-mails should the customer receive to maximize the likelihood of a purchase?

■ What is the best product to up-sell to the customer after they purchase a product?

■ What is the visit volume expected on the website next week?

Predictive Analytics vs. Data Science

•

Predictive Analytics and

Data Mining have

always

covered

the same ground except for…

•

Big data-centricity

•

Advanced database technology (to

handle big data)

• Hadoop

• Other NoSQL (MongoDB, Cassandra…)

•

Programming language-centricity (not

listed)

What Degree Does it Take

to Be a Predictive Modeler?

• You don’t need an advanced degree to be a great practitioner!

Max. Degree Count

Math 2

Computer Science 2 Social Science 2 Statistics 1

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Chapters for

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Analytics

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Like What You Heard?

Dean will be presenting at BAC 2015!

Pre-Conference (full day):

•

An Overview of Predictive Analytics for

Practitioners

Breakout Sessions (60 mins):

•

Starting Your First Predictive Analytics

Project

passbaconference.com

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