The document covers the fundamentals of predictive analytics, focusing on how data and statistical algorithms are used to forecast future outcomes based on historical data. It describes various modeling techniques, including logic-driven and data-driven models, as well as methods such as data mining and time series analysis. Additionally, it outlines the advantages and limitations of predictive models and emphasizes the importance of effective communication in conveying insights from predictive analytics.

1. PREDICTIVE
ANALYTICS

2. SYLLABUS
Introduction to Predictive analytics - Logic and
Data Driven Models - Predictive Analysis
Modeling and procedure - Data Mining for
Predictive analytics - Analysis of Predictive
analytics
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3. INTRODUCTION TO
PREDICTIVE
ANALYTICS
Predictive analytics is the use of data, statistical
algorithms and machine learning techniques to
identify the likelihood of future outcomes based
on historical data. The goal is to go beyond
knowing what has happened to providing a best
assessment of what will happen in the future.
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4. Predictive modeling
means developing models
that can be used to
forecast or predict future
events. In business
analytics, models can be
developed based on logic
or data

6. LOGIC AND DATA
DRIVEN MODELS

7. LOGIC-DRIVEN
MODELS
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A logic-driven model is one based on experience, knowledge,
and logical relationships of variables and constants connected
to the desired business performance outcome situation. The
question here is how to put variables and constants together to
create a model that can predict the future.

8. CAUSE-AND-EFFECT DIAGRAM
(FISHBONE DIAGRAM)
• The cause-and-effect diagram is a
visual aid diagram that permits a user
to hypothesize relationships between
potential causes of an outcome.
• This diagram lists potential causes in
terms of human, technology, policy,
and process resources in an effort to
establish some basic relationships
that impact business performance.
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10. INFLUENCE DIAGRAM
• Another useful diagram to
conceptualize potential relationships
with business performance variables
is called the influence diagram.
• Influence diagrams can be useful to
conceptualize the relationships of
variables in the development of
models.
• It maps the relationship of variables
and a constant to the desired
business performance outcome of
profit.
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11. DATA-DRIVEN PREDICTIVE MODELS
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12. RETAIL PRICING
MARKDOWNS MODEL
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When retailers deliberately reduce the selling price of retail
merchandise, it is called price markdown or markdown pricing.

13. MODELLING
RELATIONSHIPS AND
TRENDS IN DATA
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• Linear function: y=a+bx
• Logarithmic function: y=ln(x)
• Polynomial function: v=ax2+bx+c
• Power function: y=axb
• Exponential function: y=aex

14. CLUSTERING MODEL
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• Partitions data set into clusters, and models it by one representative from each cluster
• Can be very effective if data is clustered but not if data is “smeared”

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16. TIME SERIES MODEL
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17. NEURAL NETWORKS
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18. REGRESSION ANALYSIS
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20. CLASSIFICATION MODEL
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21. CLASSIFICATION EXAMPLE
Tid Home
Owner
Marital
Status
Taxable
Income Default
1 Yes Single 125K No
2 No Married 100K No
3 No Single 70K No
4 Yes Married 120K No
5 No Divorced 95K Yes
6 No Married 60K No
7 Yes Divorced 220K No
8 No Single 85K Yes
9 No Married 75K No
10 No Single 90K Yes
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Home
Owner
Marital
Status
Taxable
Income Default
No Single 75K ?
Yes Married 50K ?
No Married 150K ?
Yes Divorced 90K ?
No Single 40K ?
No Married 80K ?
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Test
Set
Training
Set
Model
Learn
Classifier

22. ADVANTAGES OF USING PREDICTIVE MODELS
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 Higher fault tolerance and system reliability
 Better load balancing
 Faster error diagnosis, recovery and error aversion
 Deeper understanding of business objectives and relationships
 Ability to address and answer business strategy decisions
 Better and more reliable strategic planning
LIMITATIONS OF PREDICTIVE ANALYTICS MODELS
 The need for massive training datasets
 Properly categorising data
 Applying learning to different cases

23. DATA MINING FOR
PREDICTIVE
ANALYTICS
Data mining is a discovery-driven software application process
that provides insights into business data by finding hidden
patterns and relationships in big or small data and inferring rules
from them to predict future behavior. These observed patterns
and rules guide decision-making. This is not just numbers, but
text and social media information from the web.
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24. • As of 2018, it is believed that the world's largest
single database is the World Data Center for
Climate , clocking in at 6PB. That's larger than any
telco, larger than Google, larger than any single
bank, and larger than the CIA.
• With any size data file, the normal procedure in
data mining would be to divide the file into two
parts. One is referred to as a training data set,
and the other as a validation data set. The training
data set develops the association rules, and the
validation data set tests and proves that the rules
work.
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26. 26
DATA MINING PROCESS

27. DATA MINING METHODOLOGIES
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• Summarization: find a compact description of the dataset or a subset of the dataset
• Classification: learning a function that maps an item into one of a set of predefined classes
• Association: identify significant dependencies between data attributes
• Clustering: identify a set of groups of similar items
• Trend analysis (Time series data)

28. DATA MINING TECHNIQUES
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 Cluster analysis
 Neural networks
 Online Analytical processing
 Data visualisation
 The method of concluding the information which is a logical outcome of the information stored in
the database is known as deduction.
 The method of deducting the information which is generalised from the database is known as
induction.
• Decision trees
• Rule induction

29. EXAMPLE OF A DECISION TREE
Tid Home
Owner
Marital
Status
Taxable
Income Default
1 Yes Single 125K No
2 No Married 100K No
3 No Single 70K No
4 Yes Married 120K No
5 No Divorced 95K Yes
6 No Married 60K No
7 Yes Divorced 220K No
8 No Single 85K Yes
9 No Married 75K No
10 No Single 90K Yes
10
HO
MarSt
TaxInc
YES
NO
NO
NO
Yes No
Married
Single, Divorced
< 80K > 80K
Splitting Attributes
Training Data Model: Decision Tree

30. PROS AND CONS
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33. ANALYSIS OF PREDICTIVE
ANALYTICS ​
• Your ability to communicate effectively will leave a lasting impact on
your audience​
• Effectively communicating involves not only delivering a message but
also resonating with the experiences, values, and emotions of those
listening
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34. THANK YOU
Brita Tamm​
502-555-0152​
brita@firstupconsultants.com​
www.firstupconsultants.com