Customer Segmentation Using K-Means

Table of Contents

1. Project Overview
2. Dataset
3. Project Structure
4. Requirements
5. Installation
6. Usage
7. Analysis and Results
8. Contributing
9. License
10. Author

Project Overview

CustomerSegmentationUsingKMeans is a project that demonstrates customer segmentation using the K-means clustering algorithm. The goal is to group customers of a retail store based on their purchase history and behavior.

Dataset

The dataset used for this project can be found on Kaggle. It contains the following columns:

• 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 assigned by the store based on customer behavior

Project Structure

The repository contains the following files:

• CustomerSegmentationUsingKMeans.ipynb: Jupyter Notebook with the complete analysis and K-means clustering implementation
• Mall_Customers.csv: Dataset used for the analysis

Requirements

To run the project, you need the following libraries:

• NumPy
• Pandas
• Matplotlib
• Seaborn
• Plotly
• Scikit-learn

Installation

1. Clone the repo:

   git clone https://github.com/your-username/CustomerSegmentationUsingKMeans.git
   cd CustomerSegmentationUsingKMeans

2. Install the required Python packages:

   pip install numpy pandas matplotlib seaborn plotly scikit-learn

Usage

jupyter notebook CustomerSegmentationUsingKMeans.ipynb

Analysis and Results

The notebook contains the following steps:

1. Importing Libraries: Importing necessary libraries for analysis and visualization.
2. Data Exploration: Exploring the dataset to understand the distribution and relationships between different variables.
3. Data Preprocessing: Preparing the data for clustering by scaling the features.
4. K-means Clustering: Implementing K-means clustering to group customers into segments.
5. Visualization: Visualizing the clusters to interpret the results.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

For any questions or suggestions, please contact:

• Harsh Singh: [email protected]
• GitHub: harshjuly12