Association Rule Mining: Basic Theory & Practice

Market basket Analysis with Python

Created by : Rated :4.2 64185 views

Last Updated: 2022-12-30 07:12:18 English Course Type: Self Learning

Basic theory of association rule mining
Basic metrics of association rule mining

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

Knowledge of Python. Novice level is OK.

Beginners in association rule mining

Section 1 : What is Association Rule Mining? 6 Lectures 00:17:25
Lecture 1 :
Introduction Preview
Lecture 2 :
What is Association Rule Mining?
Lecture 3 :
Comparison with Similar Methods
Lecture 4 :
Basic Terms
Lecture 5 :
Basic Math: Set Theory
Lecture 6 :
What is Association Rule Mining?
Basic understanding of association rule mining
Section 2 : Basic Metrics 6 Lectures 00:32:23
Lecture 1 :
Support
Lecture 2 :
Basic Math: Conditional Probability
Lecture 3 :
Confidence
Lecture 4 :
Lift
Lecture 5 :
Comprehension
Lecture 6 :
Summary
Section 3 : Apriori Algorithm 6 Lectures 00:25:53
Lecture 1 :
The Role of Apriori Algorithm
Lecture 2 :
Apriori Principle
Lecture 3 :
Apriori Algorithm--Phase 1
Lecture 4 :
Apriori Algorithm--Phase 2
Lecture 5 :
Summary
Lecture 6 :
Apriori Algorithm
Check your understanding of Apriori Algorithm
Section 4 : Market Basket Analysis with Python 4 Lectures 00:26:08
Lecture 1 :
Dataset
Lecture 2 :
Data Preparation
Lecture 3 :
Basic Metrics
Lecture 4 :
Apriori Algorithm
Section 5 : Thank You! 1 Lectures 00:00:37
Lecture 1 :
Thank You!

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Takuma Kimura,

64185 Course Views

1 Courses

Profile Summary: Dr. Takuma Kimura is an internationally recognized scholar in business and management fields. His expertise includes research in organizational behavior, and practical business analytics in human resource management and marketing. He teaches these subjects in universities and industrial companies. Professional Details: He published more than 10 academic papers in internationally prominent journals such as Journal of Business Ethics, International Journal of Management Reviews, Industrial Marketing Management. He is awarded as one of the World Top Reviewers from Publons, and as a Recognized Reviewer from European Management Journal. He is technically skilled for Statistical Analysis, Machine Learning, Data Science, Qualitative Analysis. And he has abundant knowledge in management theory, especially in organizational behavior and psychology.