https://github.com/naveen88112/clustering_customer_invoice_data

Customer Invoice Data Clustering This project uses clustering methods on customer invoice data for segmentation analysis. It preprocesses data, normalizes features, and uses K-Means and DBSCAN to cluster customers according to spending habits and shared locations.
https://github.com/naveen88112/clustering_customer_invoice_data

clustering data-preprocessing data-visualization numpy pandas python silhouette-score standardization

Last synced: 3 months ago
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Customer Invoice Data Clustering This project uses clustering methods on customer invoice data for segmentation analysis. It preprocesses data, normalizes features, and uses K-Means and DBSCAN to cluster customers according to spending habits and shared locations.

• Host: GitHub
• URL: https://github.com/naveen88112/clustering_customer_invoice_data
• Owner: Naveen88112
• Created: 2025-03-11T08:13:09.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2025-03-11T09:12:09.000Z (3 months ago)
• Last Synced: 2025-03-11T10:27:29.057Z (3 months ago)
• Topics: clustering, data-preprocessing, data-visualization, numpy, pandas, python, silhouette-score, standardization
• Language: Jupyter Notebook
• Homepage:
• Size: 41 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Customer Invoice Data Clustering

Overview

This project focuses on clustering customer invoice data using machine learning techniques. It aims to segment customers based on their spending behavior and transaction patterns to derive meaningful business insights.

Features

- Data Preprocessing: Standardization of invoice-related numerical features.

- Clustering Algorithms: K-Means and DBSCAN applied for segmentation.

- Performance Metrics: Silhouette Score used to evaluate clustering effectiveness.

- Visualization: Cluster insights represented using Matplotlib.

Technologies Used

- Python

- Pandas & NumPy

- Scikit-learn

- Matplotlib

How to Run

1. Clone the repository:

   

   "git clone https://github.com/yourusername/customer-invoice-clustering.git"

   

2. Open the Jupyter Notebook or Google Colab.

3. Upload the dataset (if required) and execute the cells step by step.

Results & Insights

- Customers were segmented based on invoice amounts and transaction patterns.

- K-Means and DBSCAN clustering methods were compared for effectiveness.

- Visualization helped understand customer behavior in different segments.