Mall Customer Segmentation Project

Overview

Customer segmentation is a crucial aspect of marketing strategy, allowing businesses to identify distinct groups of customers with similar characteristics and behaviors. This project aims to perform customer segmentation using the K-means clustering algorithm on a dataset obtained from Kaggle. By analyzing customer data such as age, gender, annual income, and spending score, we will uncover meaningful segments that can inform targeted marketing efforts.

Table of Contents

• Introduction
• Dataset
• Methodology
• Results
• Usage
• Contributing
• License

Introduction

Customer segmentation involves dividing a customer base into groups or segments based on shared characteristics. This segmentation enables businesses to personalize their marketing strategies, improve customer satisfaction, and drive sales. In this project, we leverage the K-means clustering algorithm to identify distinct customer segments using data collected from a mall.

Dataset

The dataset used in this project is sourced from Kaggle and contains information on 200 customers. The dataset 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. Spending Score (1-100): Score assigned by the mall based on customer behavior and spending habits. You can access the dataset on Kaggle here.

You can access the dataset on Kaggle.

Methodology

We employ the K-means clustering algorithm to perform customer segmentation. This algorithm partitions the dataset into a predetermined number of clusters, with each cluster represented by its centroid. By iteratively assigning data points to the nearest centroid and updating centroids, K-means identifies distinct customer segments based on their characteristics.

Results

The results of the customer segmentation analysis will be presented, showcasing the identified segments and their respective characteristics. Visualization techniques such as scatter plots or cluster profiles may be used to illustrate the findings.

Usage

To replicate or further explore this analysis, follow these steps: Download or clone the repository. Install the necessary dependencies.

Necessary Libraries

To run this project, you'll need the following Python libraries:

• NumPy - For numerical computing.
• Pandas - For data manipulation and analysis.
• Scikit-learn - For machine learning algorithms, including K-means clustering.
• Matplotlib - For data visualization.
• Seaborn - For statistical data visualization.

You can install these libraries using pip:

pip install numpy pandas scikit-learn matplotlib seaborn

Keras API Documentation

For information on using the Keras API, refer to the official documentation:

• Keras API Documentation

Tutorials on K-Means Clustering

To learn more about K-means clustering, you can refer to the following tutorials:

• Scikit-learn Documentation on K-means Clustering
• K-means Clustering in Python with Example

These Markdown sections will provide users with links to install necessary libraries, access Keras API documentation, and find tutorials on K-means clustering using both Scikit-learn and other resources. Adjust the links and descriptions as needed for our project.

Run the provided code scripts or notebooks to perform customer segmentation. Analyze the results and adapt the methodology as needed for specific business objectives.

Contributing

Contributions to improve this project are welcome! Feel free to open issues or pull requests with suggestions, bug fixes, or additional features.

License

This project is licensed under the MIT License.