Predictive analytics BA4206 Anna University Business 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.

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 2

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. 3

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

5. Logic and Data Driven Models

6. Logic-Driven Models 7 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.

7. 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. 8

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9. 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. 10

10. Data-Driven predictive Models 11

11. Retail pricing markdowns model 12 When retailers deliberately reduce the selling price of retail merchandise, it is called price markdown or markdown pricing.

12. Modelling relationships and trends in data 13 Linear function: y= a+bx Logarithmic function: y=ln(x) Polynomial function: v=ax 2 +bx+c Power function: y= ax b Exponential function: y= ae x

13. Clustering model 14 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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15. Time series model 16

16. Neural networks 17

17. Regression analysis 18

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19. Classification model 20

20. Classification Example categorical categorical continuous class Test Set Training Set Model Learn Classifier

21. Advantages of using predictive models 22 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

22. 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. 23

23. 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. 24

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25. 26 DATA MINING PROCESS

26. DATA MINING METHODOLOGIES 27 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)

27. Data mining techniques 28 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

28. Example of a Decision Tree categorical categorical continuous class HO MarSt TaxInc YES NO NO NO Yes No Married Single, Divorced < 80K > 80K Splitting Attributes Training Data Model: Decision Tree

29. Pros and cons 30

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32. 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 33

33. THANK YOU Brita Tamm​ 502-555-0152​ [email protected]​ www.firstupconsultants.com