Customer Market Basket Analysis using Apriori and Fpgrowth algorithms

Customer Market Basket Analysis using Apriori and Fpgrowth algorithms

In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.

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Nathan Elbert

Senior Data Scientist at Tiger Analytics

This was great. The use of Jupyter was great. Prior to learning Python I was a self taught SQL user with advanced skills. I hold a Bachelors in Finance and have 5 years of business experience.. I... Read More

Swati Patra

Systems Advisor , IBM

I have 11 years of experience and work with IBM. My domain is Travel, Hospitality and Banking - both sectors process lots of data. The way the projects were set up and the mentors' explanation was... Read More

What will you learn

Introduction to Market Basket Analysis
Association rules
Parameters of association rules
Apriori algorithm
Fpgrowth algorithm
Exploratory Data Analysis
Univariate analysis
Bivariate analysis
Identifying top selling products & departments
Creating baskets for analysis
Feature engineering
One hot encoding
Difference between apriori and fpgrowth algorithm
Support, lift, confidence in relation to association rules
Comparing time taken to run apriori and fpgrowth algorithms

Project Description

Analysis of historical customer data can highlight if a certain combination of products purchased makes an additional purchase more likely. This is called market basket analysis (also called as MBA). It is a widely used technique to identify the best possible mix of frequently bought products or services. This is also called product association analysis. The set of items a customer buys is referred to as an itemset, and market basket analysis seeks to find relationships between purchases. Market Basket Analysis creates If-Then scenario rules, for example, if item A is purchased then item B is likely to be purchased. The rules are probabilistic in nature or, in other words, they are derived from the frequencies of co-occurrence in the observations. Market Basket analysis is particularly useful for physical retail stores as it can help in planning floor space and product placement amongst many other benefits.

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Curriculum For This Mini Project

Introduction to Market Basket Analysis
05m
Introduction To Association Rules
03m
Loading And Understanding the Dataset
09m
Exploratory Data Analysis
06m
Univariate Analysis
12m
Bivariate Analysis
03m
Creating Order Products
07m
Department wise featured products
06m
Theory About Apriori And Fpgrowth
06m
Filtering And One Hot Encoding
08m
Apriori Algorithm
07m
Deeper Understanding Of Association Rules
05m
General Function Using Apriori Association Rules
05m
Fpgrowth Algorithm
03m
General Function Using Fpgrowth
04m
Comparison Of Apriori And Fpgrowth Conclusion
05m