https://github.com/chandkund/customer-segmentation
Customer segmentation divides customers into distinct groups based on characteristics and behaviors. This project uses K-Means clustering, an unsupervised machine learning algorithm, to segment customers and provide insights for targeted marketing strategies
https://github.com/chandkund/customer-segmentation
kmeans-clustering matplotlib numpy pandas python seaborn
Last synced: 3 months ago
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Customer segmentation divides customers into distinct groups based on characteristics and behaviors. This project uses K-Means clustering, an unsupervised machine learning algorithm, to segment customers and provide insights for targeted marketing strategies
- Host: GitHub
- URL: https://github.com/chandkund/customer-segmentation
- Owner: chandkund
- Created: 2024-08-26T21:23:44.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-08-26T21:31:14.000Z (10 months ago)
- Last Synced: 2025-01-18T14:53:44.270Z (5 months ago)
- Topics: kmeans-clustering, matplotlib, numpy, pandas, python, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 1.65 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Customer Segmentation using K-Means Clustering
## Overview
Customer segmentation is a powerful technique that allows businesses to divide their customer base into distinct groups based on various characteristics and behaviors. This project leverages K-Means clustering, a popular unsupervised machine learning algorithm, to segment customers and uncover actionable insights that can help in crafting targeted marketing strategies.
## Table of Contents
- [Project Overview](#overview)
- [Dataset](#dataset)
- [Installation](#installation)
- [Usage](#usage)
- [Results](#results)
- [License](#license)
## Dataset
The dataset contains various features that describe customer preferences, behaviors, and demographics, including:
- **yummy**: Indicates if the product is perceived as yummy (Yes/No).
- **convenient**: Indicates if the product is perceived as convenient (Yes/No).
- **spicy**: Indicates if the product is perceived as spicy (Yes/No).
- **fattening**: Indicates if the product is perceived as fattening (Yes/No).
- **greasy**: Indicates if the product is perceived as greasy (Yes/No).
- **fast**: Indicates if the product is perceived as fast (Yes/No).
- **cheap**: Indicates if the product is perceived as cheap (Yes/No).
- **tasty**: Indicates if the product is perceived as tasty (Yes/No).
- **expensive**: Indicates if the product is perceived as expensive (Yes/No).
- **healthy**: Indicates if the product is perceived as healthy (Yes/No).
- **disgusting**: Indicates if the product is perceived as disgusting (Yes/No).
- **Like**: A numeric rating of the product on a scale from negative to positive.
- **Age**: The age of the customer.
- **VisitFrequency**: The frequency with which the customer visits or uses the product/service.
- **Gender**: The gender of the customer (Male/Female).
## Installation
To get started with this project, clone the repository and install the necessary dependencies:
```bash
git clone https://github.com/chandkund/customer-segmentation.git
cd customer-segmentation
pip install -r requirements.txt
```
Ensure you have Python 3.x and all the required libraries installed.
## Usage
You can run the segmentation analysis by executing the main Python script:
```bash
python customer_segmentation.py
```
This will load the dataset, perform data preprocessing, apply K-Means clustering, and output the results.
### Key Features
- **Data Preprocessing**: Cleans and prepares the data for analysis.
- **K-Means Clustering**: Segments the customer base into distinct groups.
- **Visualization**: Provides visual representations of the clusters.
## Results
The project outputs various insights, including:
- Identification of key customer segments.
- Visualization of clusters based on different customer characteristics.
- Recommendations for targeted marketing strategies based on segment analysis.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.