Share Description
In this course, you will :
- introducing you to key definitions and processes that you will need to successfully complete the course
- steps you through the process of defining the problem that your predictive analysis must address, then focuses on how to ensure you meet the data requirements and how good data preparation improves your data mining projects.
- dives into the skill sets and resources you'll need, as well as the problems you'll face
- goes over the steps for determining the solution and putting it to use with probabilities, propensities, missing data, meta modelling, and much more.
Syllabus :
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
