Project-2-Customer-Segmentation

Customer Segmentation Analysis using Python and Power BI

Customer Segmentation Analysis using K-Means

Project Overview

This project focuses on customer segmentation using data analysis and machine learning techniques. The goal is to identify different groups of customers based on their income and spending behavior to help businesses make better marketing decisions.

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Dataset Information

The dataset contains the following features:

• CustomerID
• Gender
• Age
• Annual Income
• Spending Score
• Spending Level

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Project Steps

1. Data Cleaning

• Checked for missing values
• Verified data types
• Removed inconsistencies
• Prepared data for analysis

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2. Exploratory Data Analysis (EDA)

• Gender distribution analysis
• Age distribution visualization
• Income vs Spending analysis
• Spending level distribution
• Average spending by gender

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3. Customer Segmentation

K-Means clustering was applied using:

• Annual Income
• Spending Score

The Elbow Method was used to determine the optimal number of clusters (5).

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Results & Insights

The customers were divided into 5 segments:

• High Income – High Spending (Premium Customers)
• High Income – Low Spending (Target Customers)
• Low Income – High Spending (Potential Risk / Impulsive Buyers)
• Low Income – Low Spending (Low Value Customers)
• Medium Income – Medium Spending (Regular Customers)

These insights can help businesses:

• Target the right customers
• Improve marketing strategies
• Increase revenue

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Tools & Technologies Used

• Python
• Pandas
• Matplotlib
• Scikit-learn
• Google Colab / Jupyter Notebook

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Files in this Repository

• Customer_Segmentation.ipynb
• Customer_Segmentation_Final.csv
• Cluster_Summary.csv
• dataset.csv
• README.md

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Author

Shawana Hakim
Aspiring Data Analyst