https://github.com/oscartma/customer-segmentation

Le clustering consiste à regrouper les données en clusters, où les objets d'un même groupe sont plus similaires entre eux qu'avec ceux des autres groupes.
https://github.com/oscartma/customer-segmentation

clustering-methods dbscan k-means pca

Last synced: 3 months ago
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Le clustering consiste à regrouper les données en clusters, où les objets d'un même groupe sont plus similaires entre eux qu'avec ceux des autres groupes.

• Host: GitHub
• URL: https://github.com/oscartma/customer-segmentation
• Owner: OscarTMa
• License: mit
• Created: 2024-11-14T10:41:05.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-11-14T12:25:11.000Z (7 months ago)
• Last Synced: 2025-02-07T17:37:23.894Z (5 months ago)
• Topics: clustering-methods, dbscan, k-means, pca
• Language: Jupyter Notebook
• Homepage:
• Size: 412 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Customer Segmentation Project

## Table of Contents

1. [Description](#description)

2. [Installation](#installation)

3. [Usage](#usage)

4. [Project Structure](#project-structure)

5. [Contributing](#contributing)

6. [License](#license)

7. [Authors](#authors)

## Description

This project explores customer segmentation through clustering and dimensionality reduction, leveraging machine learning techniques to identify unique customer groups. Customer segmentation is critical for businesses to understand their customer base and tailor their marketing and product strategies accordingly.

- Clustering

Clustering is an unsupervised learning technique used to group data points based on similarity. In this project, we apply clustering algorithms to group customers with similar purchasing behaviors, preferences, or demographics. Each cluster represents a distinct customer profile, which can then be used to drive targeted actions. Common clustering algorithms include K-means, which seeks to minimize within-cluster variance, and DBSCAN, which focuses on density-based clusters to identify groups and outliers.

- Dimensionality Reduction

Dimensionality reduction simplifies high-dimensional datasets by reducing the number of features while preserving as much meaningful information as possible. This process is essential for enhancing data interpretability, reducing computation time, and improving model performance in some cases. We use techniques like Principal Component Analysis (PCA) to condense data into principal components, capturing the variance of the original features. Dimensionality reduction also aids in visualizing clusters, allowing us to see the natural groupings of customers more clearly.

Through this project, we aim to demonstrate the application of these methods to create insightful customer segments, ultimately supporting data-driven business decisions.

## Installation

To set up the environment, you can use the requirements file:

``bash

pip install -r requirements.txt

## Project Structure

Customer-Segmentation/                                                             

├── README.md                                                             

├── notebook/                                                             

│   └── customer_segmentation.ipynb                                                             

├── data/                                                             

│   ├── train.csv                                                             

│   └── test.csv                                                             

└── requirements.txt

## Contributing

Contributions are welcome! Please open an issue or submit a pull request.

## License

This project is licensed under the MIT License.

## Authors

Oscar Tibaduiza ([email protected])