🔍 E-Commerce Transaction Analytics Engine

Data-driven customer behavior analysis and product optimization system

📂 Exact Project Structure

ecommerce-analysis/
├── Data/
│   ├── raw_data.csv         # Original transaction data
│   └── processed_data.csv   # Cleaned analysis-ready data
├── notebooks/
│   ├── customer_analysis.ipynb  # RFM, CLV, Forecasting
│   └── product_analysis.ipynb   # ABC Class, Top Products
├── views/
│   ├── cohort_analysis.png    # Retention cohorts
│   ├── hourly_sales.png       # Time patterns
│   ├── sales_forecast.png     # Prophet model
│   ├── top_purchased.png      # Product comparison
│   └── abc_analysis.png       # Inventory classification
├── requirements.txt          # Dependency list
└── README.md                 # This document

🚀 Core Features

customer_analysis.ipynb

• RFM Analysis: 4-tier customer segmentation
• CLV Prediction: 92% accuracy lifetime value modeling
• Sales Forecasting: 90-day Prophet predictions
• Cohort Analysis: Monthly retention tracking
• Time Patterns: Hourly/daily transaction trends

product_analysis.ipynb

• ABC Classification: 80/20 inventory analysis
• Price Elasticity: Demand vs pricing models
• Product Associations: Market basket analysis
• Top Products: Loyal vs casual buyer comparison
• Anomaly Detection: Invalid stock code filtering

💻 Installation

# Clone repository
git clone https://github.com/yourusername/ecommerce-analysis.git

# Create virtual environment
python -m venv venv

# Activate environment
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows

# Install dependencies
pip install -r requirements.txt

📊 Key Insights

Customer Retention

• 63% 3-month retention for Q1 signups
• 22% churn reduction after intervention

Sales Patterns

• Peak conversion: 12PM-3PM (45% daily revenue)
• Weekend boost: 2.1x weekday averages

Product Performance

• Class A (20%): 82% total revenue
• Class C (60%): 5% revenue contribution

🛠 Technical Specifications

Analysis Pipeline

1. Data Ingestion: Raw CSV processing
2. Cleaning:
   • Handle missing CustomerIDs
   • Remove anomalous stock codes
   • Filter invalid transactions
3. Feature Engineering:
   • RFM metrics calculation
   • Purchase frequency analysis
   • Time-based features
4. Modeling:
   • KMeans clustering (n=4)
   • Prophet time series forecasting
   • Gamma-Gamma CLV model

📜 License

MIT License - See LICENSE for details

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