Career in Analytics- Introduction to decision trees

Career in Business Analytics
An Introduction to Decision Tree Modelling
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Agenda
1. What is Analytics?
2. Why build a career in Business Analytics?
3. Analytics: When it is a right career choice for me?
4. Introduction to Decision Trees- CART
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4. Introduction to Decision Trees- CART
5. Building a CART model in R
6. Next Steps
7. QA Session

What is Analytics?
Business Analytics(BA) refers to the tools, techniques and processes for continuous exploration and
investigation of past data to gain insights and help in decision making.
BA is an integration between science, technology and business context that assist
data driven decision making.
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Data from Industry DecisionsANALYTICS Insights Impact
Descriptive Analytics Predictive Analytics Prescriptive
Analytics

Why build a career in Business Analytics?
Job of a data scientist is the sexiest job of
21st century.
Harvard Business Review (October 2012 edition)
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“By 2018, the United States alone could
face a shortage of 140,000 to 190,000
people with deep analytical skills as well
as 1.5 million managers and analysts with
the know-how to use the analysis of big
data to make effective decisions.”.
McKinsey Global Institute (May 2011 edition)

Analytics: If it is a right career choice for me?
1. Do you inherently like Mathematics, Puzzles, Sudoku, Brain Teasers?
Passion for Data:
The left and right Brain:
IntangibleAttributes
2. Are you structured in your thinking and applications?
3. Do you like to solve problems and how efficiently and how deeply?
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IntangibleAttributes
4. Are you creative in your thinking?
5. B.Tech, Graduation/Post Graduation in Statistics, Economics, Econometrics, MBA
Academic Qualifications:
Knowledge of ML techniques:
6. Regression, Decision Trees, Text Analytics, Forecasting, Optimization, etc.
7. R, SAS, Python, etc.
Knowledge of ML tools:
TangibleAttributes

Introduction to Decision Trees - CART
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Decision trees
What is a Decision Tree?
Decision tree is a type of supervised learning technique (having a pre-defined target /dependent
variable) that is mostly used in classification problems. It works for both categorical and continuous
input and output variable
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Decision tree- Example
Let’s say we have a sample of 30 students with three variables:
• Gender (Boy/ Girl),
• Course (Statistics/Economics)
• Height (5 to 6 ft)
15 out of these 30 play Football in leisure time.
Now, I want to create a model to predict who will play football during leisure period?
Split on Gender Split on Course Split on Height
N=30
N=30 N=30
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MaleFemale
EconomicsStatistics >5.5 ft<5.5 ft
N=30
N=30 N=30
N=10 N=20
Play Football=2
(10%)
Play Cricket=13
(65%)
N=12 N=18
Play Football=7
(58%)
Play Football =8
(44%)
N=16 N=14
Play Football=8
(50%)
Play
Football=7
(50%)

Decision tree- Example (Continued)
Decision Tree identifies the most significant variable and its value, that gives homogenous set of
population.
Split on Gender Split on
Course
Split on
Height
N=30 N=30 N=30
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MaleFemale EconomicsStatistics
>5.5 ft<5.5
ft
N=10 N=20
Play Football=2
(10%)
Play
Football=13
(65%)
N=12
N=18
Play Football=7
(58%)
Play
Football=8
(44%)
N=16
N=14
Play
Football=8
(50%)
Play
Football=7
(50%)

Decision tree- CART
CART-Classification and Regression Tree
Decision Tree, which has continuous target/dependent variable then it is called
as Classification and Regression Tree (CART).
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Decision tree- CART
CART-Classification and Regression Tree
Decision Tree, which has continuous target/dependent variable then it is called
as Classification and Regression Tree (CART).
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Decision tree- CART in R
Problem Statement: “Suppose we want to predict the income category of a person based on
census/demographic data.
Here,
Let y= income which can be divide two categories:
Y<=50,000
Y> 50,000
For Independent variables we have:
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For Independent variables we have:

Decision tree- CART in R (Results)
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Decision tree- CART in R (Interpretation)
The significant variables which explain for a
person falling in two income categories:
Y<=50,000
For example for a person with
Y> 50,000, will have following significant
attributes
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1. Relationship={Not in family, Other relative,
Own Child, Unmarried}>capital gain < 7074
2. Relationship={Husband,
Wife}>education*>capital gain<5696

Next Steps-Online resources in CART
1. http://www.datasciencecentral.com/profiles/blogs/introduction-to-classification-regression-
trees-cart/
2. http://machinelearningmastery.com/classification-and-regression-trees-for-machine-learning/
3. https://statweb.stanford.edu/~lpekelis/talks/13_datafest_cart_talk.pdf/
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Question & Answers
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Thank you
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Career in Analytics- Introduction to decision trees

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