/customer_segmentation_analysis

This data science project performs customer segmentaion analysis using RFM and K-means Clustering on real data from an e-commerce platform.

Primary LanguageJupyter Notebook

Customer Segmentation with RFM Analysis and K-Means Clustering

Description

This project demonstrates how to build a customer segmentation model in Python. It includes data preparation for customer segmentation, constructing a K-Means algorithm from scratch, and utilizing RFM (Recency, Frequency, Monetary) analysis in marketing to evaluate customer value. The project also explores various metrics for evaluating the performance of clustering algorithms and methods for visualizing and interpreting clusters for actionable insights. The customer data is from an online e-commerce platform and can be found on kaggle on the link below: Link

Key Features:

  • Data Preparation: Clean and prepare data for segmentation.
  • RFM Analysis: Apply RFM analysis to assess customer value.
  • K-Means Clustering: Implement a K-Means clustering algorithm from scratch.
  • Performance Metrics: Evaluate clustering performance using different metrics.
  • Visualization: Visualize and interpret clusters to derive insights.

Tools and Libraries:

  • Python
  • Pandas
  • NumPy
  • Matplotlib/Seaborn (visualization)
  • Scikit-learn (clustering metrics)

Getting Started:

  1. Clone the repository: git clone https://github.com/yourusername/customer-segmentation
  2. Navigate to the project directory: cd customer-segmentation
  3. Install required libraries: pip install -r requirements.txt