End-to-End AI Demand Prediction System | 2.77% Accuracy | SARIMA Model | Production-Ready
Built with Python • ARIMA/SARIMA • 90-Day Predictions • Confidence Intervals
- Generates realistic daily sales data (trend + seasonality + noise)
- AI Model Search: Finds best SARIMA model using AIC
- Predicts 90-day future demand with 95% confidence intervals
- Validates accuracy: RMSE 2.11 | MAPE 2.77%
- Visualizes predictions + residual diagnostics
| Metric | Value | Status |
|---|---|---|
| Prediction Accuracy | 2.77% MAPE | ✅ Excellent |
| RMSE | 2.11 | ✅ Excellent |
| AI Model | SARIMA(2,1,2)x(0,1,1,7) | ✅ Optimal |
| AIC Score | 2836.7 | ✅ Best Fit |
| Prediction Horizon | 90 Days | ✅ Production |
# 1. Clone
git clone https://github.com/bravon/demand_prediction.git
cd demand_prediction
# 2. Setup (Ubuntu)
sudo apt install python3-distutils -y
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
# 3. Generate Data
python3 data/generate_timeseries.py --seed 42
# 4. Predict Demand
python3 src/predict_demand.py --horizon 90
# 5. View Results
ls outputs/Outputs Generated:
📊 metrics.json # 2.77% MAPE
📈 prediction.csv # 90-day forecast
📊 fig_history_prediction.png # Main chart
🔍 fig_residuals.png # Diagnostics
demand-prediction/
├── README.md # 📖 You're reading it!
├── requirements.txt # 📦 pip install
├── data/
│ └── generate_timeseries.py # 🧪 Synthetic data
├── src/
│ ├── predict_demand.py # 🤖 MAIN AI SCRIPT
│ └── metrics.py # 📏 RMSE/MAPE
└── outputs/ # 📈 Results (gitignored)
| Column | Type | Description |
|---|---|---|
date |
Date | Daily timestamp |
sales |
int | Units sold |
prediction |
float | AI Forecast |
lower_ci |
float | 95% Lower bound |
upper_ci |
float | 95% Upper bound |
| Category | Tools |
|---|---|
| Language | Python 3.12 |
| AI Model | SARIMA (Seasonal ARIMA) |
| Data | Pandas • NumPy |
| Metrics | RMSE • MAPE |
| Plots | Matplotlib • Seaborn |
| Optimization | AIC Score |
date,prediction,lower_ci,upper_ci
2025-01-01,245.3,232.1,258.5
2025-01-02,247.8,234.2,261.4
...- Fork the repo
- Create feature branch (
git checkout -b feature/amazing) - Commit changes (
git commit -m '💥 Add amazing feature') - Push (
git push origin feature/amazing) - Open Pull Request
MIT License - Free to use in portfolios/commercial!
---