Predictive Analytics Essential Training: Data Mining

Get useful, real-world insights into using predictive analysis and data mining to solve problems.

Introduction

• Data mining and predictive analytics

1. What Is Data Mining and Predictive Analytics?

• Introducing the essential elements
• Defining data mining
• Introducing CRISP-DM

2. Problem Definition

• Beginning with a solid first step: Problem definition
• Framing the problem in terms of a micro-decision
• Why every model needs an effective intervention strategy
• Evaluate a project's potential with business metrics and ROI
• Translating business problems into data mining problems

3. Data Requirements

• Understanding data requirements
• Gathering historical data
• Meeting the flat file requirement
• Determining your target variable
• Selecting relevant data
• Hints on effective data integration
• Understanding feature engineering
• Developing your craft

4. Resources You Will Need

• Skill sets and resources that you'll need
• Compare machine learning and statistics
• Assessing team requirements
• Budgeting sufficient time
• Working with subject matter experts

5. Problems You Will Face

• Anticipating project challenges
• Addressing missing data
• Addressing organizational resistance
• Addressing models that degrade

6. Finding the Solution

• Preparing for the modeling phase tasks
• Searching for optimal solutions
• Seeking surprise results
• Establishing proof that the model works
• Embracing a trial and error approach

7. Putting the Solution to Work

• Preparing for the deployment phase
• Using probabilities and propensities
• Understanding meta modeling
• Understanding reproducibility
• Preparing for model deployment
• How to approach project documentation

8. The Nine Laws of Data Mining

• CRISP-DM and the laws of data mining
• Understanding CRISP-DM
• Advice for using CRISP-DM
• Understanding the nine laws of data mining
• Understanding the first and second laws
• Understanding the data preparation law
• Understanding the laws about patterns
• Understanding the insight and prediction laws
• Understanding the value law
• Understanding why models change

Conclusion

• Next steps