metadata

title: Customer Segmentation
emoji: 👀
colorFrom: green
colorTo: yellow
sdk: streamlit
sdk_version: 1.41.1
app_file: app.py
pinned: false
short_description: 'customer segmentation using KMeans '

Customer Segmentation Project

Overview

This project focuses on customer segmentation using KMeans clustering and RFM (Recency, Frequency, Monetary) analysis. By dividing customers into distinct segments, businesses can better understand their customer base and target specific groups with personalized strategies.

Objectives

Segment customers into meaningful groups using KMeans clustering.
Perform RFM analysis to understand customer behavior.
Identify key customer segments such as Loyal Customers, Champions, Risk Customers, and New Customers.

Project Workflow

RFM Analysis:
- Computed Recency, Frequency, and Monetary values for each customer.
- Created 5 bins for each RFM metric based on quantiles.
KMeans Clustering:
- Used the processed RFM data to create 4 clusters.
- Determined the optimal number of clusters using methods like the elbow method or silhouette score.
Data Analysis:
- Analyzed each cluster to identify patterns and insights.
- Focused on understanding key customer groups:
  - Loyal Customers
  - Champions
  - At Risk Customers
  - New Customers

Key Results

Clear segmentation of customers into 4 clusters.
Detailed insights into customer groups:
- Loyal Customers: High frequency and monetary value, recent purchases.
- Champions: Very high monetary value and frequency.
- At Risk Customers: Low recency and frequency, indicating potential churn.
- New Customers: Recently acquired, low frequency and monetary value.

Technologies Used

Python: Data analysis and clustering.
Libraries:
- pandas, numpy for data manipulation.
- matplotlib, seaborn plotlyfor data visualization.
- sklearn for KMeans clustering.

Create a Virtual Environment

python -m venv venv

Activate the Virtual Environment in Windows

venv\Scripts\activate

Install Dependencies

pip install -r requirements.txt

Run the Project

streamlit run app.py