Customer Segmentation

This project involves customer segmentation analysis using a dataset of mall customers. The goal is to categorize customers into different segments based on their characteristics and purchasing behaviors.

Table of Contents

• Introduction
• Dataset
• Installation
• Usage
• Features
• Cluster Insights

Introduction

Customer segmentation is a crucial part of marketing strategies, allowing businesses to target specific groups of customers effectively. This project utilizes data analysis and machine learning techniques to segment customers into meaningful categories.

Dataset

The dataset used in this project is Mall_Customers.csv, which includes the following features:

• 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): Score assigned by the mall based on customer behavior and spending nature

Installation

To run this project, you need to have Python installed along with several libraries. You can install the required libraries using the following command:

pip install -r requirements.txt

Usage

1. Clone the repository:

git clone https://github.com/KumarranMahesh/Customer-Segmentation.git

2. Navigate to the project directory:

cd Customer-Segmentation

3. Ensure you have the dataset Mall_Customers.csv in the data directory.

4. Run the Jupyter notebook or Python script to execute the project.

Features

• Data Cleaning & Preprocessing: Handling missing values, encoding categorical variables.
• Data Visualization: Visualizing distributions of features.
• Clustering: Implementing K-Means clustering to segment customers.

Cluster Insights

This project also includes detailed insights for each customer cluster, providing specific characteristics and targeted marketing strategies for different customer segments.