Customer Segmentation System

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Project Title:

Customer Segmentation System: An End-to-End Unsupervised Machine Learning Pipeline with Web App Deployment
This project implements an end-to-end unsupervised machine learning system to segment customers based on behavioral and demographic data. It showcases multiple clustering algorithms, dimensionality reduction techniques, and an interactive Streamlit app for dynamic cluster analysis — all containerized and ready for cloud deployment.

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📊 Dataset:

• Source: Kaggle – Mall Customer Segmentation Dataset

• Features:

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

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Key Steps:

1. 🔍 Exploratory Data Analysis (EDA)

• Histograms, pairplots, violin plots (age, income, gender)
• Correlation matrix to identify informative features

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2. 🧼 Preprocessing

• One-hot encoding for Gender
• Standardization of numerical features
• Dimensionality reduction with PCA or t-SNE for visualization

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3. 🧠 Unsupervised Model Comparison

Algorithm	Use Case
K-Means	Baseline hard clustering
Hierarchical Clustering (Agglomerative)	Tree-based segmentation
DBSCAN	Density-based detection (good for outliers)
Gaussian Mixture Models (GMM)	Probabilistic clustering
(Optional) Autoencoders	Deep clustering for nonlinear patterns

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4. 📈 Evaluation Metrics

• Silhouette Score
• Davies–Bouldin Index
• Calinski–Harabasz Score
• Elbow method & Dendrogram for cluster tuning

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5. 📊 Visualization

• 2D PCA/t-SNE cluster plots
• Interactive Streamlit UI with cluster count selector and segment interpretation

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6. Required Modules

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