Demand-Forecasting-for-E-commerce-and-Retail

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Demand-Forecasting-for-E-commerce-and-Retail

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This project aims to build accurate and scalable demand forecasting models for e-commerce and retail businesses

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📈 Demand Forecasting for E-commerce and Retail

This project aims to build accurate and scalable demand forecasting models for e-commerce and retail businesses. By leveraging historical sales data, product metadata, and external factors, the models help predict future product demand to optimize inventory management, supply chain operations, and marketing strategies.
🚀 Project Objectives

Forecast daily/weekly/monthly demand at product or category level.

Reduce overstock and stockouts through predictive analytics.

Support real-time and batch processing of demand forecasts.

Provide visual insights for business decision-making.

📦 Key Features

🧠 Machine Learning Models (e.g., XGBoost, LightGBM, Random Forest)

📊 Time Series Forecasting (ARIMA, Prophet, LSTM, etc.)

🗃️ Feature Engineering (calendar events, lag variables, promotional flags)

📈 Model Evaluation (MAPE, RMSE, SMAPE)

🌐 Interactive Dashboards (optional via Streamlit or PowerBI)

⏱️ Hyperparameter tuning for model optimization

🗂️ Project Structure

Demand-Forecasting-for-E-commerce-and-Retail/
│
├── data/                   # Raw and processed datasets
├── notebooks/              # Jupyter notebooks for exploration and modeling
├── src/                    # Core source code (data processing, modeling)
│   ├── features.py
│   ├── model.py
│   └── utils.py
├── models/                 # Trained model artifacts
├── reports/                # Output reports, visualizations
├── app/                    # Optional Streamlit app
├── requirements.txt        # Python dependencies
└── README.md               # Project documentation

📊 Example Use Cases

Forecasting daily demand for SKUs in an online retail store.

Predicting weekly sales across regions for a supermarket chain.

Planning stock levels for promotional campaigns (e.g., Black Friday).

📈 Sample Workflow

Data Collection: Load historical sales and product data.

Preprocessing: Clean, impute, and transform data.

Feature Engineering: Create lag features, rolling averages, etc.

Model Training: Train models and evaluate performance.

Forecasting: Generate future demand predictions.

Visualization: Analyze forecasts via plots or dashboards.

🧪 Requirements

Install dependencies using:

pip install -r requirements.txt

⚙️ Technologies Used

Python (Pandas, NumPy, Scikit-learn)

XGBoost, LightGBM, Prophet, LSTM

Matplotlib, Seaborn, Plotly

Streamlit (optional for web UI)

Author Name: Otutu Anslem
    Github: https://github.com/Otutu11/
    Linkedin: https://www.linkedin.com/in/otutu-anslem-53a687359/

📚 References

Kaggle Retail Forecasting Competitions

Facebook Prophet Documentation

Demand forecasting research papers and case studies

📝 License

This project is licensed under the MIT License.

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