Customer Clustering using K-Means

📌 Project Overview

This project focuses on customer segmentation using K-Means Clustering, an unsupervised machine learning technique. The dataset used in this project contains information about mall customers, including their age, gender, annual income, and spending score. The goal is to segment customers into different groups based on their purchasing behavior.

🚀 Features

• Exploratory Data Analysis (EDA)
  • Gender distribution analysis
  • Age distribution visualization
  • Annual income distribution
• K-Means Clustering
  • Elbow Method to determine the optimal number of clusters
  • Cluster visualization with centroids
  • Cluster characteristics summary
• Interactive Dashboard with Streamlit
  • Provides user-friendly visualization and insights
  • Enables users to explore customer segments dynamically

📂 Dataset

The dataset used in this project is the Mall Customers Dataset. It includes the following columns:

• CustomerID: Unique identifier for each customer
• Gender: Male or Female
• Age: Customer's age
• Annual Income (k$): Annual income in thousands of dollars
• Spending Score (1-100): A score assigned based on customer spending behavior

Dataset Source: Mall Customers Dataset

🛠️ Technologies Used

• Python
• Pandas (for data manipulation)
• Matplotlib & Seaborn (for data visualization)
• Scikit-learn (for K-Means clustering)
• Plotly (for interactive visualizations)
• Yellowbrick (for Elbow Method visualization)
• Streamlit (for building an interactive dashboard)

🔧 Installation & Setup

Follow these steps to set up the project:

1. Clone the repository

   git clone https://github.com/ai-azz/customer-segmentation.git
   cd customer-segmentation

2. Create a virtual environment (optional but recommended)

   python -m venv venv
   source venv/bin/activate  # On MacOS/Linux
   venv\Scripts\activate    # On Windows

3. Install dependencies

   pip install -r requirements.txt

4. Run the Streamlit app

   streamlit run dashboard.py

📊 Data Analysis & Insights

1️⃣ Exploratory Data Analysis (EDA)

The dataset is explored through various visualizations:

• Gender distribution: Pie chart representing male vs female customers.
• Age distribution: Bar chart showing the number of customers per age group.
• Annual income distribution: Histogram illustrating income ranges.

2️⃣ Elbow Method for Optimal Clusters

The Elbow Method is used to determine the optimal number of clusters by plotting the Within-Cluster Sum of Squares (WCSS) against different values of K.

3️⃣ K-Means Clustering

Customers are grouped based on their Annual Income and Spending Score. The model assigns each customer to a cluster, and the centroids of these clusters are plotted for better interpretation.

📌 Results & Findings

• The optimal number of clusters is 4, as determined by the Elbow Method.
• Customers are segmented into four groups based on their income and spending behavior.
• High-income customers with high spending scores form a distinct cluster, while low-income customers with lower spending scores form another.