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Predictive Analytics with Decision Trees

Predictive Analytics with Decision Trees

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Predictive Analytics with Decision Trees

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  1. Predictive Analytics with Decision Trees Professors Leo Pipino & Luvai Motiwalla OIS Department, MSB 16.711 (203) Special Topics: Computational Data Modeling (Prof Chandra)

  2. Visualization skills to interpret data and present in meaningful ways Executive and management skills to know when and how to use data for making decisions Domain strategy skills to develop the right questions, determine which data is important Tool developers to mask the complexity of data and analytics to lower skill boundaries Skills Categories are in Demand for “Big Data” Data experts to manipulate and integrate big data Mathematical and operations research to develop analytics algorithms

  3. Four key skills in Analytics Analytics and Big Data The Business Analyst Applies business intelligence, predictive analytics, and other techniques to turn information into business insight The Data Scientist Combines the skills needed to collect, store, manage, and understand patterns and trends in data 60% of enterprises face a shortage of business analytics skills today 40% of enterprises report a skills shortage in ability to manage information Information Security Software Engineering & Mobile Dev The Cyber Security Professional Requires a broad portfolio of security skills and systems thinking applied to business priorities The Next Generation Software Engineer Employs the skills and methodologies needed to keep pace with the rapidly evolving software engineering discipline 39% of organizations adding IT staff plan to hire information security professionals 65% of enterprises face a shortage of mobile development skills today Source: IBM Tech Trends report 2012

  4. What is Predictive Analytics? • Discover relevant, new patterns with speed and flexibility. • Analyze data to find useful insights. • Make better decisions and act quickly. • Monitor models to verify continued relevance and accuracy. • Manage a growing portfolio of predictive assets effectively

  5. Components of Predictive Analytics and Data Mining • Exploratory Data Analysis – Visually explore data sets of any size to spot trends, patterns and hidden insights that you can use to design a strategy, confirm a hypothesis or identify a new idea. • Model Development and Deployment – Streamline the data mining process to create highly accurate descriptive and predictive analytic models. • High-Performance Data Mining – Generate accurate, timely insights and solve complex problems using big data. • Credit Scoring – Build, validate and deploy credit risk models using in-house expertise. • Analytics Acceleration – Produce faster results and improve data governance with in-database analytics. • Scoring Acceleration – Maximize the performance and accuracy of your analytic models. • Model Management and Monitoring – Create, manage, deploy, monitor and operationalize analytical models. • Text mining applies analytical techniques to text-based documents. The knowledge gleaned from data and text mining can be used to fuel strategic decision making.

  6. Decision Trees • Oldest machine learning model • Recursively divides training data sets into homogeneous buckets thru most discriminative dividing criteria • "homogeneity" is measured thru output variable • If its’ a numeric value, the measurement will be the variance of the bucket • If its’ a categorical value, the measurement will be the entropy or gini index of the bucket

  7. Decision Tree Algorithms - Quinlan • Based on Shannon’s definition of information (entropy) • Compute information needed (bits) to classify the set • Compute decrease in entropy for each attribute • Choose attribute that gives greatest decrease in entropy (gain of information) as first node • Repeat procedure for resulting subsets to select next node(s) of the tree • Repeat until leaves reached

  8. Decision Tree Example

  9. IBM Analytics tools like SPSS Modeler turns information into insight and insight into business outcomes. Transform Align Anticipate Act your organization around information with confidence at the point of impact to optimize outcomes see, predict and shape business outcomes Deploy an information and big data strategy that flows from your business strategy. Leveraging business analytics to deliver actionable insights Embed analytics into your processes and empower a culture of data-driven decision making • Business Intelligence • Performance Management • Predictive and Advanced Analytics • Risk Analytics • Sentiment Analytics • Big Data Analytics • Content Analytics • Web and Digital Analytics • Online Benchmark • Spend Analytics • Decision Management • Advanced Case Management • Digital Marketing Optimization • Cross-channel Selling and Marketing • Pricing, Promotion, and Assortment Optimization • Marketing Performance Optimization • Supply Chain Optimization • Organization and Workforce Transformation • Big Data Platform • Data Warehousing • Information Integration and Governance • Data Management • Enterprise Content Management • Defensible Disposal Learn

  10. IBM’s Business Analytics Software

  11. Questions? Lets’ get started with IBM SPSS Modeler Tutorial