# Customer Market Basket Analysis using Apriori and Fpgrowth algorithms

#### Videos

Each project comes with 2-5 hours of micro-videos explaining the solution.

## What will you learn

Association rules
Parameters of association rules
Apriori algorithm
Fpgrowth algorithm
Exploratory Data Analysis
Univariate analysis
Bivariate analysis
Identifying top selling products & departments
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.

## Curriculum For This Mini Project

05m
Introduction To Association Rules
03m
09m
Exploratory Data Analysis
06m
Univariate Analysis
12m
Bivariate Analysis
03m
Creating Order Products
07m
Department wise featured products
06m
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