Market Basket Analysis is a data mining technique used by retailers to analyze customer purchase data and identify relationships between products that are frequently bought together. This analysis helps retailers to improve product placement, cross-selling, and promotions to increase sales and profitability.
The Apriori Algorithm is a popular algorithm used for Market Basket Analysis. It generates association rules by identifying frequent itemsets in the transaction data.
In this project, you will use Python to implement Market Basket Analysis using the Apriori Algorithm. You will start by importing the required libraries, loading the transaction data into a Pandas DataFrame, and then using the Apriori Algorithm to generate frequent itemsets. You will then use the association_rules function to generate association rules based on the frequent itemsets.
The main steps of the project are:
Import the required libraries
Load the transaction data into a Pandas DataFrame
Convert the transaction data into a one-hot encoded format
Use the Apriori Algorithm to generate frequent itemsets
Use the association_rules function to generate association rules
Analyze and interpret the results
By completing this project, you will gain hands-on experience with Market Basket Analysis and the Apriori Algorithm, which are essential skills for anyone working in the retail industry or data analysis.