A production-ready machine learning pipeline for predicting e-commerce sales using advanced time series techniques and ensemble methods. Built with enterprise-grade code quality and comprehensive error handling.

This project implements a comprehensive sales forecasting system for e-commerce platforms, utilizing multiple machine learning algorithms to predict future sales with high accuracy. The pipeline processes over 18,000 sales records across 10 products and 4 regions, generating 30-day forecasts with detailed performance analytics.

• 52.5% variance explained (R² = 0.525)
• 3.79 RMSE on sales prediction
• Linear Regression outperformed complex models (avoiding overfitting)
• 42 engineered features including lag, rolling statistics, and business logic
• Production-ready code with comprehensive error handling and model persistence

E-Commerce-Sales-Prediction/
├── 📊 Data Files
│   ├── ecommerce_sales.csv              # Main dataset (1.2MB, 18,260 records)
│   ├── future_sales_predictions.csv     # 30-day forecasts (300 predictions)
│   └── model_analysis_report.txt        # Detailed performance report
│
├── 🤖 Models & Pipeline
│   ├── main_code.py                     # Complete ML pipeline (56KB)
│   └── models/
│       ├── best_model.pkl              # Trained Linear Regression (2KB)
│       ├── all_models.pkl              # All 4 trained models (94MB)
│       ├── feature_names.pkl           # Feature list (42 features)
│       ├── model_metadata.pkl          # Performance metrics & metadata
│       └── pipeline_config.pkl         # Complete pipeline state (101KB)
│
├── 📈 Visualizations
│   ├── Figure_1.png                    # Data overview & distributions (115KB)
│   ├── Figure_2.png                    # Model performance comparison (142KB)  
│   ├── Figure_3.png                    # Individual product forecasts (83KB)
│   ├── Figure_4.png                    # Feature correlation matrix (125KB)
│   └── future_predictions_visualization.png  # 30-day forecast plots (313KB)
│
├── 📋 Documentation
│   ├── README.md                       # This file
│   └── output.txt                      # Console output log (8KB)

Source: E-commerce transaction data (April 2020 - April 2025) Size: 18,260 records × 11 columns (1.2MB) Products: 10 unique products across 3 categories Regions: 4 geographical regions (North, South, East, West)

Python 3.8+
pandas >= 1.5.0
numpy >= 1.21.0
scikit-learn >= 1.2.0
xgboost >= 1.6.0
lightgbm >= 3.3.0
matplotlib >= 3.5.0
seaborn >= 0.11.0
joblib >= 1.0.0

# Clone the repository
git clone https://github.com/KurKigal/E-Commerce-Sales-Prediction.git
cd E-Commerce-Sales-Prediction

# Install dependencies
pip install pandas numpy scikit-learn xgboost lightgbm matplotlib seaborn joblib

# Run the complete pipeline
python main_code.py

• Linear Regression won - Simple models sometimes beat complex ones!
• No overfitting - Cross-validation scores are consistent
• Fast inference - 1000+ predictions per second
• Robust performance - Handles missing values and outliers

The pipeline creates 42 sophisticated features from the original 11 columns:

• Cyclical encoding: month_sin, month_cos, day_sin, day_cos
• Calendar features: year, month, day, quarter, week_of_year
• Weekend indicator: is_weekend

• Sales lags: 1, 7, 14, 30 days (sales_lag_1, sales_lag_7, etc.)
• Rolling statistics: 7, 14, 30-day means and standard deviations
• Trend indicators: Short and long-term momentum

• Profit margin: (price - cost) / price
• Effective price: price * (1 - discount)
• Price quartiles: Relative pricing position (0-3)
• Discount indicators: Binary and categorical discount levels
• Price-cost ratio: Markup analysis

• Product statistics: product_avg_sales, product_std_sales
• Category statistics: category_avg_sales, category_std_sales

• One-hot encoded: Category and region dummy variables

• Sales distribution analysis
• Time series trends
• Category and region performance breakdowns

• RMSE and R² comparisons across models
• Actual vs Predicted scatter plots
• Residual analysis for best model

• 30-day individual product forecasts
• Seasonal patterns and trends
• Top-performing product predictions

• Feature correlation heatmap
• Target variable relationships
• Multicollinearity analysis

• Comprehensive 30-day forecast visualization
• Top 3 products detailed forecasting
• Trend continuation analysis

1. Category-based features are strongest predictors (correlation: 0.73)
2. Price impact is minimal (correlation: 0.014) - contrary to intuition
3. Lag features have low correlation (0.014) - daily volatility is high
4. Linear models work best - data has strong linear relationships

Top 10 Most Important Features:
1. category_avg_sales     (0.727) - Category performance
2. category_std_sales     (0.726) - Category volatility  
3. revenue               (0.515) - Revenue relationship
4. product_avg_sales     (0.018) - Product performance
5. effective_price       (0.015) - Discounted price
6. cost                  (0.015) - Product cost
7. sales_lag_1          (0.014) - Previous day sales
8. price_normalized     (0.014) - Relative pricing
9. price                (0.014) - Base price
10. profit_margin       (0.013) - Profitability

• Focus on category-level strategies rather than individual pricing
• Product mix optimization has higher impact than price optimization
• Cross-category effects are significant for forecasting
• Weekly/monthly trends matter more than daily patterns

from main_code import EcommerceSalesPipeline

# Train new model
pipeline = EcommerceSalesPipeline('ecommerce_sales.csv')
results = pipeline.run_pipeline()

# Generate 30-day forecast
predictions = pipeline.predict_future_sales(days=30)
predictions.to_csv('my_predictions.csv', index=False)

from main_code import load_and_predict

# Use existing model on new data
predictions = load_and_predict('new_data.csv', 'models/best_model.pkl')

from main_code import analyze_model_errors, feature_correlation_analysis

# Detailed error analysis
analyze_model_errors(pipeline, threshold=10)

# Feature relationship analysis  
feature_correlation_analysis(pipeline)

• best_model.pkl (2KB): Trained Linear Regression model - ready for production
• all_models.pkl (94MB): All 4 trained models for comparison
• feature_names.pkl (667B): List of 42 feature names for prediction
• model_metadata.pkl (1KB): Performance metrics, training date, model info
• pipeline_config.pkl (101KB): Complete pipeline state for reproducibility

• future_sales_predictions.csv (11KB): 300 predictions (10 products × 30 days)
• model_analysis_report.txt (810B): Detailed performance summary

• High-resolution PNG files (83-313KB each) for presentations and reports
• Publication-ready quality (300 DPI) for academic or business use

• Time Series Split: 5-fold time-aware cross-validation
• No data leakage: Future information never used for past predictions
• Consistent performance: CV scores align with test scores

• Average error: 3.01 units
• Maximum error: 15.54 units
• High errors (>100 units): 0 occurrences
• Error distribution: Well-behaved, no systematic bias

• ✅ Comprehensive error handling
• ✅ Data validation and quality checks
• ✅ Model persistence and versioning
• ✅ Logging and monitoring
• ✅ Modular, testable design

• Hyperparameter optimization using Optuna/GridSearchCV
• External data integration (weather, holidays, economic indicators)
• Deep learning models (LSTM, Transformer) for complex patterns
• Model monitoring with drift detection and auto-retraining
• API deployment with FastAPI for real-time predictions
• Docker containerization for easy deployment

• Customer segmentation for personalized forecasting
• Inventory optimization recommendations
• A/B testing framework for model comparison
• Confidence intervals for prediction uncertainty
• Seasonal decomposition for trend analysis

• Training time: ~2 minutes on 18K records
• Memory usage: ~500MB for full pipeline
• Prediction speed: 1,000+ predictions/second
• Model size: 2KB (best model) / 94MB (all models)

• RAM: Minimum 2GB, recommended 4GB
• Storage: 100MB for full project
• CPU: Any modern processor (models are not computationally intensive)

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

• Follow PEP 8 style guidelines
• Add comprehensive docstrings
• Include unit tests for new features
• Update documentation for changes

This project is licensed under the MIT License - see the LICENSE file for details.

Author: [Emirhan Keser]

• 📧 Email: [email protected]
• 💼 LinkedIn: Emirhan Keser

Project Repository: https://github.com/KurKigal/E-Commerce-Sales-Prediction/

• scikit-learn for robust ML algorithms
• XGBoost and LightGBM for gradient boosting
• Pandas for efficient data manipulation
• Matplotlib & Seaborn for beautiful visualizations
• Joblib for model persistence

⭐ Star this repository if it helped you! ⭐

This project demonstrates production-ready machine learning engineering with business impact focus.