This repository contains the implementation of a Market Basket Analysis conducted on a grocery dataset using the Apriori algorithm. Market Basket Analysis is a technique used to uncover associations between different items purchased together, providing valuable insights for marketing strategies, inventory management, and sales promotion efforts.
The dataset used in this project is a collection of grocery transactions, where each transaction includes items purchased by a customer. The dataset is structured with the following key columns:
Member_number: Unique identifier for the customer. Date: Date of the transaction. itemDescription: Description of the item purchased.
The analysis involves the following steps:
The dataset is transformed into a format suitable for the Apriori algorithm. Each transaction is represented as a binary vector, indicating the presence or absence of each item.
We employ the Apriori algorithm to identify frequent itemsets in the dataset. These itemsets are combinations of items that frequently occur together in transactions.
The resulting frequent itemsets, along with their support values, are analyzed to draw insights about purchasing patterns.
Python: The primary programming language used. Pandas: For data manipulation and analysis. mlxtend: Specifically for its implementation of the Apriori algorithm and associated tools for market basket analysis.
To run the analysis, ensure you have Python installed, along with the Pandas and mlxtend libraries. You can install these libraries using pip:
pip install pandas
pip install mlxtend
The main script can be run to perform the market basket analysis. It reads the data, preprocesses it, applies the Apriori algorithm, and outputs the frequent itemsets.