E-Commerce Customer Analytics Dashboard

A full-stack analytics dashboard that segments e-commerce customers using RFM analysis and K-Means clustering, built with Python and React.

Overview

Raw transaction data is transformed into customer-level RFM metrics (Recency, Frequency, Monetary), then clustered using K-Means to identify distinct behavioral segments. Results are served via a Flask REST API and visualized in an interactive React dashboard.

Project Structure

purchase-behavior-analyse/
├── data/
│   ├── raw/                        # Downloaded dataset
│   ├── processed/                  # Cleaned data, RFM tables, cluster results
│   └── download_data.py            # Downloads and samples the UCI dataset
├── notebooks/
│   ├── 01_eda_cleaning.ipynb       # Exploratory data analysis & cleaning
│   ├── 02_rfm_features.ipynb       # RFM feature engineering & normalization
│   └── 03_clustering.ipynb         # K-Means clustering & evaluation
├── backend/
│   └── app.py                      # Flask REST API
├── frontend/                       # React + Vite dashboard
├── requirements.txt                # Python dependencies
└── README.md

Dataset

UCI Online Retail Dataset — real transactional data from a UK-based online retailer (2010–2011). A 5000-row sample is used for this project.

Methodology

1. Data Cleaning

• Removed duplicates, null CustomerIDs, returns (negative quantities), and zero-price items
• Added a Revenue column (Quantity × UnitPrice)

2. RFM Feature Engineering

Per-customer metrics computed from transaction history:

• Recency — days since last purchase (lower = more recent)
• Frequency — number of distinct orders
• Monetary — total spend

3. Clustering

• Applied log transformation + StandardScaler to handle skewed distributions
• Used Elbow method and Silhouette score to determine optimal k
• Fitted K-Means and labeled each cluster based on RFM profile

Cluster Results

Segment	Customers	Avg Recency	Avg Frequency	Avg Spend
Champions	587	46.2 days	3.8 orders	£126.8
Lost / Low Engagement	1422	150.3 days	1.2 orders	£21.3

Tech Stack

Layer	Technology
Data & ML	Python, pandas, scikit-learn
Visualization	matplotlib, seaborn, Recharts
Backend	Flask, Flask-CORS
Frontend	React, Vite

Setup & Run

Prerequisites

• Python 3.8+
• Node.js 18+

Backend

# Install dependencies
pip install -r requirements.txt

# Download and prepare data
python data/download_data.py

# Run notebooks in order (in Jupyter)
jupyter notebook

# Start Flask API
python backend/app.py

API runs at http://localhost:5000

Frontend

cd frontend
npm install
npm run dev

Dashboard runs at http://localhost:5173

API Endpoints

Endpoint	Description
GET /api/summary	KPI totals (customers, revenue, frequency, recency)
GET /api/segments	Cluster profiles with RFM averages and labels
GET /api/customers	Full customer-level RFM + cluster assignment
GET /api/monthly-revenue	Revenue aggregated by month