FliT-DA-DS-Project1

Market Basket Analysis For E-Commerce Store

Overview

This project is a market basket analysis of a dataset of customer transactions. The goal of the project is to identify the relationships between different items in the dataset. This information can be used to improve product placement, promotions, and recommendations.

Data

The data for this project is a dataset of customer transactions from an e-commerce store. The dataset includes the following information for each transaction:

• Member_number renamed to CustId
• Date
• ItemDescription renamed to item

Methodology

The methodology used in this project is market basket analysis. Market basket analysis is a technique that is used to identify the relationships between different items in a dataset. The market basket analysis was performed using the Python programming language and the apriori algorithm. The analysis identified the following:

• The most frequently purchased items
• The strongest relationships between items
• The support, confidence, and lift for each association rule

Results

The market basket analysis revealed several interesting patterns and relationships between products. Some key findings include:

• Frequently purchased products: Whole milk, rolls/buns, and other vegetables were among the most frequently purchased items.

• Strong product associations: Strong association rules emerged, indicating that customers who purchase certain items are highly likely to purchase others. For instance, yogurt and sausage exhibited a strong association where customers who bought one often bought the other.

Implications for e-commerce businesses**

E-commerce businesses can use the results of the market basket analysis to improve their product placement, promotions, and recommendations. For example, e-commerce businesses could:

• Place frequently purchased items together in their stores or websites.
• Recommend items that are strongly associated with each other to customers who purchase a particular item.
• Offer discounts on items that are likely to be purchased together.
• Improve sales of less frequently bought products.

By following these recommendations, e-commerce businesses can increase their sales and improve customer satisfaction.

Future work

Future work on this project could include:

• Analyzing the data by customer segments
• Implementing the recommendations in an e-commerce store