Samanwoy Saha

Customer Segmentation

2 min read

Website | Github

Python Jupyter Scikit-learn

📋 Project Overview

This project implements customer segmentation analysis using K-Means Clustering. The segmentation helps businesses understand their customer base better and develop targeted marketing strategies.

customer-segmentation.png

Dataset

1. Data Exploration & Pre-processing

  • Data quality assessment and cleaning
  • Missing value treatment
  • Feature engineering and selection

2. Exploratory Data Analysis (EDA)

  • Statistical summary of customer data
  • Distribution analysis of key features
  • Correlation analysis between variables
  • Visualization of customer patterns

3. Feature Engineering

  • Customer lifetime value calculation
  • Standardization and scaling

4. Clustering Analysis

  • K-Means Clustering: Primary segmentation method
  • Elbow Method: Optimal cluster determination

📈 Visualizations

The notebook includes comprehensive visualizations:

  • Customer distribution across segments
  • Age Distribution
  • Income Distribution
  • Total Spending Distribution
  • Income by Education Level
  • Spending by Marital Status
  • Correlation Matrix (Income, Age, Recency, Total Spending, Number of Web Purchases, Number of Store Purchases)
  • Heatmap (Average Income by Education and Marital Status)
  • Average Spending by Education
  • Campaign Acceptance Rate by Marital Status
  • Average Income by Age Group
  • ELbow Method for Optimal Cluster Size
  • Customer Segmentation (PCA)

Customer Segments: 0, 1, 2, 3, 4, 5

🎯 Objectives

  • Identify Customer Segments: Group customers with similar characteristics and behaviors
  • Behavioral Analysis: Understand purchasing patterns and customer preferences
  • Business Intelligence: Provide actionable insights for marketing and sales teams
  • Marketing Strategy: Enable targeted campaigns and personalized customer experiences

🛠️ Technologies Used

  • Python 3.8+
  • Pandas - Data manipulation and analysis
  • NumPy - Numerical computations
  • Matplotlib/Seaborn - Data visualization
  • Scikit-learn - Machine learning algorithms
  • Jupyter Notebook - Interactive development environment
  • Streamlit - Web app framework

📁 Project Structure

Customer-Segmentation/
├── notebook/
│ └── customer_segmentation.ipynb # notebook
├── data/
│ └── customer_segmentation.csv   # Dataset
├── model/
│ └── customer_segmentation.pkl   # trained model
├── src/
│ └── app.py                      # web app
├── requirements.txt              # Python dependencies
└── README.md                     # Project documentation

🔧 Installation & Setup

  1. Clone the repository
git clone https://github.com/SamanwoySaha/Customer-Segmentation.git
cd Customer-Segmentation
  1. Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
  1. Install dependencies
pip install -r requirements.txt
  1. “Run app”
streamlit run src/app.py

Graph View

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