This project performs market basket analysis on transaction data using the Apriori algorithm and association rule mining. It processes the data from a CSV file, generates frequent itemsets, and then extracts association rules with various metrics to help identify important relationships between products.

• Overview
• Features
• Prerequisites
• Installation

This repository contains a Python script designed to analyze transaction data from a CSV file. It performs the following tasks:

• Reads and preprocesses transaction data.
• Converts transactions into a one-hot encoded format suitable for analysis.
• Applies the Apriori algorithm to identify frequent itemsets.
• Generates and displays association rules along with important metrics such as support, confidence, lift, leverage, and conviction.

• Data Import: Reads transactions from a CSV file.
• Data Preprocessing: Cleans and transforms transaction data into a list format and then into a one-hot encoded DataFrame.
• Frequent Itemset Mining: Uses the Apriori algorithm to find item combinations that occur frequently in the dataset.
• Association Rule Mining: Extracts rules that show how the purchase of certain items is associated with others.
• Interpretation Metrics: Outputs support, confidence, lift, and several additional metrics to help in the analysis.

• Python 3.x
• Pandas
• mlxtend

1. Clone the repository:

   git clone https://github.com/viznuv/market-basket-analysis.git
   cd market-basket-analysis