Business Problems and Data Science Solutions

CSCI E-

84

A Practical Approach to Data

Science

Ramon A. Mata-Toledo, Ph.D.

Professor of Computer Science

Harvard Extension School

Unit 1 - Lecture 2

Business Problems and Data Science

Solutions

This lecture is based primarily on Chapters 2 and 3 of the book “Data Science for Business” by Foster Provost and Tom Fawcett, 2013.

Thanks also to Professor P. Adamopoulos (Stern School of Business of New York University) and Professor Tomer Geva (The Tel Aviv University School of Management)

Lecture Objectives

At the end of this lecture, the student should be able to identify and define concepts such

as:

•

Data Mining Process

•

Data Mining Models

•

Data Mining Tasks

•

Supervised Segmentation (purity, information gain, entropy)

In addition, this lecture will introduce you to:

•

Supervised versus Unsupervised Methods

•

Data Warehousing

•

Database Querying

•

On-line Analytical Data Processing

•

You just landed a great analytical job with MegaTelCo, one of the largest

telecommunication firms in the US.

•

They are having a major problem with customer retention in their wireless

business.

•

In the mid-Atlantic region, 20% of cell phone customers leave when their

contracts expire. Communications companies are now engaged in battles to

attract each other’s customers while retaining their own.

•

Marketing has already designed a special retention offer.

•

Your task is to devise a precise, step-by-step plan for how the data science team

should use MegaTelCo’s vast data resources to solve the problem.

MegaTelCo: Predicting Customer Churn

•

What data you might use?

•

How would they be used?

•

How should MegaTelCo choose a set of customers to receive their offer in order to

best reduce churn for a particular incentive budget?

Terminology

Model

:

•

A simplified representation of reality created to serve a purpose

Predictive Model

:

•

A formula for estimating the unknown value of interest: the target

•

The formula can be mathematical, logical statement (e.g., rule), etc.

Prediction

:

•

Estimate an unknown value (i.e. the target)

Instance / example

:

•

Represents a fact or a data point

Terminology

(Continuation)

Model induction

:

•

The creation of models from data. The procedure that creates the model is

called the induction algorithm or learner

Training data

:

•

The input data for the induction algorithm

Individual

:

Terminology

(Continuation)

Informative variables (attributes):

provide information about the

characteristics of the entity.

It is desirable to choose those

variables that correlate well with the

target variable.

What is a model?

A

simplified

_{representation}

_{of reality created for a}

_{specific purpose}

• Examples: map (abstraction of physical world), engineering prototypes, the Black-Scholes option pricing

model, etc.

•

Data Mining example:

“formula” for predicting probability of customer attrition at contract expiration

!

“classification model” or “class-probability estimation model”

Data Mining Process

science + craft + creativity + common sense

Data Mining is

a

process

Data mining is a process that breaks up the overall task of finding patterns from data into a set of well-defined set of subtasks. The solutions to these subtasks can often be put together to solve the overall problem.

Cross-Industry Standard Process for Data Mining

CRISP-DM

Business

Understanding Understanding Data Preparation Data Modeling Evaluation Deployment

Format Data Integrate Data Construct Data Clean Data Select Data Determine Business Objectives Review Project Produce Final Report Plan Monitoring & Maintenance Plan Deployment Determine Next Steps Review Process Evaluate Results Assess Model Build Model Generate Test Design Select Modeling Technique Assess Situation Explore Data Describe Data Collect Initial Data Determine Data Mining Goals Verify Data Quality Produce Project Plan

Data Mining versus Data Warehousing/Storage

Data Warehousing / Storage

Data Mining versus Database Querying and Reporting

Database Querying / Reporting (SQL, Excel, QBE, other GUI-based querying)

• Queries are requests posed to a database for particular subsets of the data

• Very flexible interface to produce reports summaries of the data

• No modeling or sophisticated pattern finding

Data Mining versus On-line Analytical Data Processing

OLAP – On-line Analytical Processing

• OLAP provides easy-to-use GUI to explore large data collections

• Exploration is manual; no modeling

• Dimensions of analysis

Data Mining versus…

•

Traditional statistical analysis

• Mainly based on hypothesis testing or estimation / quantification of uncertainty • Should be used to follow-up on data mining’s hypothesis generation

•

Automated statistical modeling

(e.g., advanced regression)

• This is data mining, one type – usually based on linear models • Massive databases allow non-linear alternatives

Answering business questions with these

techniques..

•

Who are the most profitable customers?

•

Is there really a difference between profitable customers and the average customer?

•

But who really are these customers? Can I characterize them?

•

Will some particular new customer be profitable? How much revenue should I expect this

customer to generate?

Common Data Mining Tasks

•

Classification estimation

Given a sample population predict for each individual the class to which it belongs. The set of classes are generally small and disjoint (non overlapping).

•

Class probability estimation (scoring)

A model produces, for each individual, a value or score which is the probability that the individual belongs to the class.

• How likely is this consumer to respond to our campaign?

•

Regression

Given a sample population, regression, attempts to estimate the relationship between a selected dependent variable and one or more independent variable.

Common Data Mining Tasks

(continuation)

•

Similarity Matching

Process that attempts to identify similar individual based on particular data about them. • Can we find consumers similar to my best customers?

•

Clustering

Process that attempts to group individual in a population based on similarities w/o a specific target. • Do my customers form natural groups?

•

Co-Occurrence Grouping

Process attempts to find associations based on their transactions. • Customers that bought this item also bought

Common Data Mining Tasks

(continuation)

•

Profiling

Process that attempts to characterize the typical behavior of an individual, group, or population. Often used to establish a baseline. Used also to observe ‘out of the normal behavior’ for fraud detection.

• What items do my best customer buy more frequently?

•

Link Prediction

Process that attempts to predict connections between nodes of a graph based on the information of the individual nodes. Often used in social networks such as Facebook or LinkedIn.

• People that you may know

•

Data Reduction

• Process that transforms a large set of data to replace it with a smaller set that contains most of the relevant information.

Common Data Mining Tasks

(continuation)

•

Casual Modeling

Process that attempts to help us understand the actions or events that influence other events. • Did the advertising campaign increased the volume of purchases?

This type of technique may include randomized controlled experiments with two variants (A/B tests). One of the variants serves as the control; the other is the variation of the experiment.

• Which of the two promotional codes increased volume of sales? (assuming an email advertisement sent two identical populations with variations in the promotional code)

Supervised versus Unsupervised Data Mining

•

Unsupervised (no specific target)

•

“Do our customers naturally fall into different groups?”

• No guarantee that the results are meaningful or will be useful for any particular purpose

•

Supervised (specific target)

•

“Can we find groups of customers who have particularly high likelihoods of canceling their

service soon after contracts expire?”

• Results are generally more useful than unsupervised. Supervised data mining require different techniques than unsupervised.

• Supervised Data Mining requires that the target information be in the data.

Supervised Data Mining & Predictive Modeling

•

Is there a specific, quantifiable target that we are interested in or trying to predict?

• Think about the decision

•

Do we have data on this target?

• Do we have enough data on this target?

• Need a min ~500 of each type of classification

• Do we have relevant data prior to decision?

• Think timing of decision and action

•

The result of supervised data mining is a model that predicts some quantity

Subclasses of Supervised Data Mining

Data Mining Model in Use

Probability

estimation model

New

data

item

prediction

of prob. of

class

Model in use:

Data Mining versus Use of the Model

Supervised Segmentation

•

How can we divide (segment) the population into groups that differ from each other with

respect to some quantity that we would like to predict?

• Which customers will churn after their expiration date? • Which customers are likely to default?

•

Informative attributes

• Find knowable attributes that correlate with the target of interest

• Increase accuracy

• Alleviate computational problems

Supervised Segmentation

(Continuation)

•

How can we judge whether a variable contains important information about the target

variable?

• How much?

• What single variable give the most information about the target?

• Based on this attribute, how can we partition the customers into subgroups that are as pure as

possible – with respect to the target (i.e., such that in each group as many instances as possible belong to the same class).

• A subgroup is pure if every member of the group (instance) has the same value for the target otherwise is impure.

Selecting Informative Attributes

•

Example: A binary (Yes, No) classification problem with the following attributes:

•

Head shape (square, circular)

•

Body shape (rectangular, oval)

•

Body color (red, blue, purple)

•

Target variable: Yes, No

Purity Measure – Information Gain – Entropy

•

Difficulties encountered in segmenting the population due to the impurity of the subgroups can

be addressed by creating a formula that evaluates how well each attribute separates the entities

into segments. The formula is called a

purity measure.

•

The most common criterion for segmenting the population is called information gain (IG).

•

Information gain is based on a purity measure called

entropy

.

•

Entropy

is a measure of the degree of disorder that can be applied to a set.

•

The entropy is defined as follows:

P_i: the probability (the relative percentage) of property i within the set

P_i = 0 when no member of the set satisfy the property

P_i = 1 when all the members of the set satisfy the property

lg(p_i) stands for the logarithm of base 2: log₂p_i

IG (parent, children)

=

Note: Higher IG indicates a more informative split by the variable.

Parent

Child 1

(c

)

Child 2

(c

)

Child…

Selecting Informative Attributes

• Example: Set of 10 people divided into two classes: will default (write-off) and will not default (non-write-off). 7 people will not default; p(n-w-o)=7/10 = 0.7 and p(w-o)=3/10

• entropy (S) = -[0.7 x lg(0.7) + 0.3 x lg(0.3)] = 0.88

lg(x) stands for logarithm of base 2. Log (x) is the logarithm of base 10.

The logarithm of number x in base 2 equals the logarithm of x in base 10, divided by the logarithm of 2 in base 10.

Remember: logarithms of numbers between 0 and 1 are negative.

Information Gain

•

Information gain measures the

change

in entropy due to any amount of new information being

added

Information Gain

(continuation)

13/30 approx. 0.43 _{17/30 approx. 0.57}

Relative percentages of the subset with respect to the parent. This percentage is calculated dividing the cardinality of the child-set by the cardinality of the parent-child-set.

Attribute Selection

Reasons for selecting only a subset of attributes

:

•

Better insights and business understanding

•

Better explanations and more tractable models

•

Reduced cost

•

Faster predictions

Example: Attribution Selection with Information Gain

•

This dataset includes descriptions of hypothetical samples corresponding to 23 species of

gilled mushrooms in the Agaricus and Lepiota Family

•

Each species is identified as definitely edible, definitely poisonous, or of unknown edibility and

not recommended

• This latter class was combined with the poisonous one

•

The Guide clearly states that there is no simple rule for determining the edibility of a

mushroom; no rule like “leaflets three, let it be” for Poisonous Oak and Ivy.

Example: Attribution Selection with Information Gain

Multivariate Supervised Segmentation

•

If we select the

single

variable that gives the most information gain, we create a very

simple

segmentation

•

If we select multiple attributes each giving some information gain, how do we put them

together?

Categorical and Quantitative Variables

A categorical variable places an observation in one (and only one) category chosen from two or more possible categories. • If there is no ordering that can be done between the categories, the variable is nominal.

• If there is some intrinsic order that can be assigned to the categories, the variables are ordinal.

Examples of categorical data

• Your gender (male, female)

• Your class in school (Freshman, Sophomore, Junior, Senior, Graduate) • Your performance status (Probation, Regular, Honors)

• Your political party (Democrat, Republican, Independent) • Your hair color (blonde, brown, red, balck, white, other) • Your type of pet (cat, dog, ferret, rabbit, other)

• Your race (Hispanic, Asian, African American, Caucasian) • Machine settings (Low, Medium, High)

• Method of payment (Cash, Credit)

Variables and the Case format

Before analyzing the data, prepare charts or graphs, or do any statistical test, it’s important to gain a sense

of what data is all about.

Why is this necessary?

The type of data available dictates what we can do with it.

Do you want to prepare a scatterplot or perform a linear regression to generate a predictive model?

Fine as long as you two quantitative variables preferable both continuous

Do you want to do a one-way Analysis of Variance (ANOVA)?

No problem, as long as you have multiple collections of quantitative variables, and you can

split them up into groups using one of the categorical variables you have collected.

Do you want to construct a bar plot?