Machine LearningData Science
Customer Segmentation
A machine-learning project using K-Means clustering to segment customers based on annual income and spending behavior.
2026Live
01Project Overview
Overview
A customer analytics project that applies K-Means clustering to identify meaningful customer groups.
02Problem Space
The Problem
Businesses can use customer behavior patterns to create more targeted marketing strategies.
03Project Objectives
Goals
Analyze customer data
Preprocess the dataset
Identify customer clusters
Determine optimal cluster count
Evaluate clustering quality
Generate business insights
04System Design
Architecture
01Dataset ingestion
02Pandas preprocessing
03Feature scaling
04K-Means clustering
05Silhouette evaluation
06PCA visualization
05Engineering Challenges
Challenges
Selecting meaningful features
Determining the optimal number of clusters
Interpreting cluster behavior
06Implementation
Solutions
Feature scaling
Elbow method
Silhouette score
PCA visualization
07Tech Stack
Technology
Python
Pandas
NumPy
Matplotlib
Seaborn
Scikit-learn
K-Means
PCA
08What I Learned
Lessons Learned
Feature selection strongly influences clustering results
Visualization helps interpret unsupervised learning
09What's Next
Future Improvements
Larger customer datasets
Additional customer features
Algorithm comparison
Interactive Streamlit dashboard