Customer Segmentation Dashboard

A premium Flask web application for K-Means customer segmentation on the Mall Customers dataset. Built as a Final Year Project for Lovely Professional University.

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🚀 Quick Start

1. Install Dependencies

pip install flask pandas numpy scikit-learn plotly matplotlib seaborn joblib

2. Run the Application

python app.py

3. Open in Browser

http://127.0.0.1:5000

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📁 Project Structure

Customer-Segmentation-Dashboard/
├── app.py                        ← Flask app with all routes
├── requirements.txt              ← Python dependencies
├── data/
│   └── Mall_Customers.csv        ← Dataset
├── static/
│   ├── css/main.css              ← Design system
│   └── js/main.js                ← Interactive JS
├── templates/
│   ├── base.html                 ← Navbar + footer layout
│   ├── home.html                 ← Landing page
│   ├── dataset.html              ← Dataset overview
│   ├── eda.html                  ← Step-by-step EDA
│   ├── cleaning.html             ← Data cleaning
│   ├── scaling.html              ← Feature scaling
│   ├── visualizations.html       ← EDA charts (Plotly)
│   ├── elbow.html                ← Elbow method
│   ├── silhouette.html           ← Silhouette score
│   ├── clustering.html           ← K-Means model
│   ├── cluster_viz.html          ← Cluster plots
│   ├── interpretation.html       ← Cluster profiles
│   ├── recommendations.html      ← Marketing strategies
│   ├── dashboard.html            ← Analytics dashboard
│   └── predict.html              ← New customer prediction
├── utils/
│   ├── preprocessing.py          ← Load, clean, scale
│   ├── eda.py                    ← EDA functions
│   ├── clustering.py             ← Elbow, Silhouette, KMeans
│   ├── visualization.py          ← All Plotly charts
│   ├── recommendations.py        ← Cluster profiles & strategies
│   └── database.py               ← SQLite operations
└── models/                       ← Auto-created: scaler.pkl, kmeans_model.pkl, customers.db

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🔗 Pages

URL	Page
/	Home
/dataset	Dataset Overview
/eda	EDA Workflow
/cleaning	Data Cleaning
/scaling	Feature Scaling
/visualizations	EDA Charts
/elbow	Elbow Method
/silhouette	Silhouette Score
/clustering	K-Means Clustering
/cluster-visualization	Cluster Plots
/interpretation	Cluster Interpretation
/recommendations	Marketing Recommendations
/dashboard	Analytics Dashboard
/predict	Predict New Customer

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🛠️ Tech Stack

• Backend: Python 3.11, Flask 3.0
• ML: Scikit-learn (KMeans, StandardScaler, Silhouette Score)
• Data: Pandas, NumPy
• Charts: Plotly (interactive), Matplotlib, Seaborn
• Database: SQLite
• Frontend: Bootstrap 5, Vanilla CSS, Vanilla JS

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📊 Dataset

The Mall Customers Dataset (Kaggle) — 200 customers, 5 features:

• CustomerID, Gender, Age, Annual Income (k$), Spending Score (1-100)

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