Mall Customers Segmentation Analysis

Project Domain: Retail Analytics

This repository contains a simple analysis of mall customer data using k-means clustering. The goal is to segment customers based on their spending patterns and demographic information to better understand their behavior and identify potential marketing strategies.

Overview

In this project, we use k-means clustering to segment customers based on different feature combinations:

• Age vs. Spending Score
• Age vs. Annual Income
• Annual Income vs. Spending Score

The k-means clustering analysis provides insights into customer segmentation based on different feature combinations. The resulting clusters can be used to tailor marketing strategies and improve customer targeting.

Dataset

The dataset used for this analysis is the Mall Customer Segmentation Data, which includes the following attributes:

• CustomerID: Unique ID assigned to each customer
• Gender: Gender of the customer
• Age: Age of the customer
• Annual Income (k$): Annual income of the customer in thousand dollars
• Spending Score (1-100): Score assigned by the mall based on customer behavior and spending nature