Ecommerce Analytics & Forecasting

A complete ETL pipeline that transforms ecommerce transaction data into actionable business insights. Perfect for analyzing customer behavior, sales trends, and business performance.

Quick Start

Prerequisites

• Docker & Docker Compose installed
• Git (optional, for cloning)
• 4GB+ RAM available for Docker

Getting Started

1. Clone or Download

   git clone https://github.com/supremkc05/ecommerce-analytics-forecasting.git
   cd ecommerce-analytics-forecasting

2. Add Your Data

   • Place your CSV file in the data/ directory
   • Name it cleaned_data.csv (or update the config)

3. Run Everything

   # Start all services
   docker-compose up --build -d
   
   # Watch the ETL process
   docker-compose logs -f etl_app
   
   # Access database admin at http://localhost:8080
   # Username: admin@example.com #use your own
   # Password: admin123

That's it! Your data is now processed and ready for analysis.

What You'll Get

After running the pipeline, you'll see outputs like:

ETL Pipeline Results

Starting Ecommerce Analytics ETL Pipeline...
Connected to PostgreSQL database successfully
Extracted 358,277 rows from data/cleaned_data.csv
Data transformation completed
Successfully loaded 4,314 customers
Successfully loaded 2,785 products
ETL Pipeline completed successfully!

Business Analytics

• Customer Segmentation: VIP, Premium, Regular, and Basic customer tiers
• Top Performers: Highest spending customers and best-selling products
• Geographic Analysis: Revenue breakdown by country
• Time Trends: Monthly sales patterns and seasonal insights

Analytics Overview

Customer Segmentation

• VIP (5.4%): 232 customers, £17K+ avg spend - Drive 52% of revenue
• Premium (30%): 1,298 customers, £2K avg spend - Growth potential
• Regular (59%): 2,550 customers, £429 avg spend - Volume base
• Basic (5.4%): 234 customers, £68 avg spend - New buyers

Geographic Distribution

• 🇬🇧 UK: £6.6M (82.6%) - Dominant market
• 🇳🇱 Netherlands: £269K (3.4%) - Growth opportunity
• 🇮🇪 Ireland: £242K (3.0%) - Stable market
• 🇩🇪 Germany: £190K (2.4%) - Expansion potential

Top Products

1. Paper Craft Little Birdie: £168K revenue
2. Regency Cakestand 3 Tier: £142K revenue
3. Medium Ceramic Storage Jar: £81K revenue

Key Metrics

• Total Revenue: £7.9M across 358K+ transactions
• Average Order: £22.45 per transaction
• Peak Season: November-December (holiday surge)
• Product Range: 2,785 unique items

Project Structure

ecommerce-analytics-forecasting/
├── scripts/
│   ├── run_etl.py              # Main ETL runner
│   └── run_analysis.py         # Analytics generator
├── src/
│   ├── etl/                    # ETL logic
│   ├── database/               # Database settings
│   └── analysis/               # Analytics queries
├── docker/
│   ├── docker-compose.yml      # Container setup
│   └── Dockerfile              # Application container
├── data/
│   └── cleaned_data.csv        # Your source data
└── .env                        # Database configuration

Usage Examples

Running ETL Pipeline

# Full pipeline execution
python scripts/run_etl.py

# Output shows successful data loading

Generating Business Reports

# Run analytics to see insights
python scripts/run_analysis.py

# You'll see reports on:
# - Top customers and products
# - Sales by country and month
# - Customer segmentation analysis

Need Help?

Common Issues

Database Connection Failed

# Check if PostgreSQL is running
docker-compose ps

# View database logs  
docker-compose logs postgres

ETL Pipeline Errors

# Check application logs
docker-compose logs etl_app

# Run manually for debugging
python scripts/run_etl.py

Port 5432 Already in Use

# Stop existing PostgreSQL services
taskkill /F /IM postgres.exe

# Or change port in docker-compose.yml

Tips for Success

• Ensure Docker has at least 4GB RAM allocated
• Place your CSV file in the data/ directory
• Check logs if something goes wrong: docker-compose logs
• Use docker-compose down -v for a clean restart

---