K-Means Clustering Project for Mall Customers Dataset

This repository showcases a k-means clustering analysis on customer data from a shopping mall. The goal is to segment customers based on their annual income and spending score. The project uses Python with scikit-learn, pandas, matplotlib, and seaborn for data processing, analysis, and visualization.

Project Overview

In this project, we explore and analyze customer data to segment customers into clusters based on their annual income and spending score. We employ k-means clustering, a popular unsupervised learning technique, to identify distinct customer segments.

Project Structure

• data/
  • Mall_Customers.xlsx : Dataset containing customer information.
• notebooks/
  • K-Means_Clustering_Mall_Customers.ipynb : Jupyter Notebook with full project code and analysis.
• README.md : Project overview and instructions.
• requirements.txt : List of required Python packages.

Data Description

• CustomerID : Unique identifier for each customer.
• Gender : Gender of the customer.
• Age : Age of the customer.
• Annual Income (k$) : Annual income of the customer in thousands of dollars.
• Spending Score (1-100) : Spending score of the customer, ranging from 1 to 100.

Usage

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/K-Means-Clustering-Project-for-Mall-Customers-dataset.git
   cd K-Means-Clustering-Project-for-Mall-Customers-dataset