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Predictive Analytics-session 1- Data Prep and Investigation.pptx

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Predictive Analytics-session 1- Data Prep and Investigation.pptx

  1. 1. © 2011 Alteryx, Inc. Confidential. Alteryx Predictive Analytics Data Prep and Investigation Welcome! Cailin Swingle, Solutions Engineer
  2. 2. © 2012 Alteryx, Inc. Confidential. 2 Participation • We will be asking for interaction and feedback during the class. • At the top of the screen click on Participants • Interaction icons: • Hand • Green check mark • Red ‘X’ • Go slower/faster • Emoticons
  3. 3. © 2012 Alteryx, Inc. Confidential. 3 Chat and Q&A • Feel free to ask questions! • Ask verbally or • Q&A tab • Type your question here
  4. 4. © 2012 Alteryx, Inc. Confidential. 4 Session 1– Data Prep and Investigation • Understanding your data • Determining predictor variables • Transforming variables Session 2– Model Build and Assessment Session 3- Cluster Analysis Session 4- Time Series Course Outline
  5. 5. © 2012 Alteryx, Inc. Confidential. 5 • It is expected that you are familiar with building workflows in Alteryx. We will not be going over how to use the standard Alteryx tools. • It is helpful to have some background with Predictive Analysis. • The focus is on how to do Predictive Modeling in Alteryx, not for example explaining what a regression is. Assumptions
  6. 6. © 2012 Alteryx, Inc. Confidential. 6 A quick service restaurant chain is interested in mailing coupons to local neighborhoods to grow their customer base. They want to know which households are most likely to become customers and which households would be the most valuable in terms of revenue. Data available: • Customer and non-customer households with additional fields appended from the Experian ConsumerView file • Household level restaurant (food away from home) expenditure and other household characteristics from the Bureau of Labor Statistics Objectives: • To determine which households to mail to, construct a model that predicts the likelihood that a household will be a customer • To find out which households will spend the most, build a predictive model that estimates restaurant expenditure Business Case
  7. 7. © 2011 Alteryx, Inc. Confidential. Alteryx Engine
  8. 8. © 2012 Alteryx, Inc. Confidential. 8 Things to look for: • Constants (Unique Values = 1) • Zero’s in your numeric data (Min = 0) • Numeric fields that have few unique values • “This field has a small number of unique values, and appears to be a categorical field. Consider changing the field data type to "string".” • Rare characteristics (Min Value Count) • “Some values of this field have a small number of value counts. If Appropriate, consider combining some value levels together.” Field Summary Report
  9. 9. © 2012 Alteryx, Inc. Confidential. 9 Summary When to use Scatterplot vs. Plot of Means vs. Association Analysis • Scatterplot – Shows how two variables relate to one another graphically. Can reveal non- linear relationships between variables and show if one variable must be transformed to model the true relationship between the two. Best when both variables are interval or ratio. • Plot of Means – Takes a numeric or binary categorical target variable along with a categorical predictor variable and plots the mean of the response field for each of the categories (levels) of the predictor variable. • Association Analysis – This tool should be used to see which variables are correlated with one another. Makes the most sense when all variables are interval or ratio.
  10. 10. © 2012 Alteryx, Inc. Confidential. 10 Support Resources • Technical support 8am – 8pm Eastern • Email: clientservices@alteryx.com • Phone: (888) 836-4274 • Choose option 2 • WebEx training – live and recorded • Community
  11. 11. © 2011 Alteryx, Inc. Confidential. Thank You!

Editor's Notes

  • New predictive tool set. They are macros (not regular tools). Open one to show the macro – explain R tool. Able to turn on macro messages to dig deeper.
  • New predictive tool set. They are macros (not regular tools). Open one to show the macro – explain R tool. Able to turn on macro messages to dig deeper.
  • BAMA 512: Database Marketing and Data Mining

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