Association Rule Mining: Basic Theory & Practice

Association Rule Mining: Basic Theory & Practice

  Welcome to the association rule mining course. This course is an introductory course. You will learn basic knowledge of association rule mining in this course.

  Association rule mining is a useful technique to explore associations between variables. It contributes to effective cross-selling and has been applied to construct recommender system in EC sites. We can use it not only in marketing analytics but also other fields in business analytics.

  This course intends to provide you with theoretical knowledge as well as python coding. Theoretical knowledge is important to understand the algorithm of data mining, and it can be a useful foundation for more advanced learning.

  This course consists of 4 sections. In the first section, you will learn what an association rule is. In Session 2, you will learn the basic metrics of association rule mining. Session 3 covers apriori algorithm that is a useful method to identify important associations between variables. Session 4 is a Hands-On chapter, where you will learn how to implement association rule mining in Python.

  I’m looking forward to seeing you in this course!

Source of Pictures:

Course Image: Gerd Altmann from Pixabay
PV:
- Beer: Hans Braxmeie from Pixabay
- Pretzel: Couleur from Pixabay
- Potatoes: RitaE from Pixabay
- Diaper: PublicDomainPictures from Pixabay

Theoretical Understanding and Market Basket Analysis with Python

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What you will learn

• Basic theory of association rule mining
• Basic metrics of association rule mining
• Apriori algorithm

Rating: 4.5

Level: Beginner Level

Duration: 1.5 hours

Instructor: Takuma Kimura