📊 Customer Segmentation Analysis

📌 Project Overview

This project performs customer segmentation using data analysis and machine learning techniques, followed by an interactive business dashboard for insight generation. The goal is to group customers based on their income and spending behavior to understand different purchasing patterns and support data-driven business decisions.

The project combines:

• 📓 Clustering analysis using K-Means (Jupyter Notebook)
• 📊 Interactive visualization using Power BI Dashboard

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🎯 Objectives

• Identify distinct customer groups
• Understand spending behavior across segments
• Highlight high-value and potential customers
• Provide actionable insights for marketing strategies
• Demonstrate an end-to-end analytics workflow

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📂 Repository Structure

Customer-Segmentation-Project/
│
├── Notebook/
│   ├── Customer_Segmentation.ipynb
│   └── Mall_Customers.csv
│
├── Dashboard/
│   ├── Customer_Segmentation_Dashboard.pbix
│   ├── customer_segmentation_dashboard_data.xlsx
│   └── Mall Customer Segmentation Dashboard img.jpeg
│
└── README.md

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📊 Dataset Description

The dataset contains customer information such as:

• Customer ID
• Gender
• Age
• Annual Income (k$)
• Spending Score (1–100)

These features help analyze purchasing power and customer behavior.

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Machine Learning Analysis ( Notebook)

Customer segmentation is performed using the K-Means clustering algorithm, an unsupervised learning method.

Key steps:

• Data preprocessing
• Exploratory Data Analysis (EDA)
• Determining optimal clusters using the Elbow Method
• Applying K-Means clustering
• Visualizing customer groups

This analysis reveals natural groupings of customers based on income and spending habits.

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📊 Business Dashboard (Power BI)

The Power BI dashboard transforms analytical results into clear and interactive visual insights.

Key Highlights

• Customer demographics overview
• Income and spending distribution
• Segment comparison
• Identification of high-value customers
• Interactive filtering and exploration