Digital Egypt Pioneers Initiative (DEPI) Graduation Project
# Sales Forecasting & Optimization System
**Graduation Project for IBM AI & Data Science (DEPI)**
---
## 📽 Demos
- 🌐 **Live Streamlit App**: Sales Forecasting Streamlit
- 🌐 **Live Flask App**: Sales Forecasting Flask
---
## 🌟 Key Features
### 🔮 Intelligent Forecasting System
- Automated prediction engine
- Self-optimizing algorithms that improve over time
- Real-time demand planning capabilities
- Smart parameter configuration system
### 🖥️ Business Applications
1. **Enterprise Forecasting Application** - Production-ready web service
2. **Executive Dashboard** - Intuitive business intelligence portal
3. **Analytics Workbench** - Interactive data exploration suite
### 📦 Data Foundation
- Automated data processing pipelines
- Advanced pattern recognition systems
- Multi-source data integration
- Automatic anomaly detection
---
## 🛠 Technical Stack
| Component | Technologies Used |
|-----------------|--------------------------------------------|
| Core ML | Python 3.10, Scikit-learn, TensorFlow 2.12 |
| Forecasting | Prophet, Statsmodels, XGBoost |
| Visualization | Plotly, Dash, Streamlit, Altair |
| Infrastructure | Flask, Docker, Redis, Celery |
| Optimization | Optuna, OR-Tools, PuLP |
---
## 📂 Project Structure
```plaintext
Sales-Forecasting-Optimization/
├── data/
│ ├── output.csv
│ └── stores_sales_forecasting.csv
│
├── notebooks/
│ └── forecasting_model.ipynb
│
│
├── flask_app/
│ ├── app.py
│ ├── best_model.pkl
│ ├── features_columns.pkl
│ ├── README.md
│ ├── requirements.txt
│ ├── .env
│ ├── static/
│ │ └── styles.css
│ └── templates/
│ ├── index.html
│ └── documentation.html
│
├── streamlit_app/
│ ├── app.py
│ ├── best_model.pkl
│ ├── features_columns.pkl
│ ├── requirements.txt
│ └── README.md
│
├── dashboard/
│ ├── dashboard_app.ipynb
│ ├── cleaned_data.csv
│ └── README.md
│
├── model …