Loading forecast_app/README.md +3 −3 Original line number Diff line number Diff line Loading @@ -16,9 +16,9 @@ Students analyze 10 years of historical sales data for 3 watch models and predic ## Watch Models 1. **Luxury Classic** ($500) - High-end watches with holiday seasonality 2. **Sport Pro** ($220) - Athletic watches popular in spring/summer 3. **Casual Style** ($120) - Everyday watches with steady demand 1. **Luxury Classic** (CHF 500) - High-end watches with holiday seasonality 2. **Sport Pro** (CHF 220) - Athletic watches popular in spring/summer 3. **Casual Style** (CHF 120) - Everyday watches with steady demand ## Installation Loading forecast_app/app.py +1 −1 Original line number Diff line number Diff line Loading @@ -300,7 +300,7 @@ def download_excel(): ws_summary[f'A{idx}'] = watch['id'] ws_summary[f'B{idx}'] = watch['name'] ws_summary[f'C{idx}'] = watch['category'] ws_summary[f'D{idx}'] = f"${watch['sell_price']}" ws_summary[f'D{idx}'] = f"CHF {watch['sell_price']}" # Adjust column widths ws_summary.column_dimensions['A'].width = 8 Loading forecast_app/data/supply_chain_data_full.json +1 −1 Original line number Diff line number Diff line { "metadata": { "generated_date": "2025-10-29T12:48:44.648537", "generated_date": "2025-10-29T14:15:25.431926", "years": 11, "total_months": 132, "start_date": "2014-01-01T00:00:00", Loading forecast_app/data/supply_chain_data_training.json +1 −1 Original line number Diff line number Diff line { "metadata": { "generated_date": "2025-10-29T12:48:44.648537", "generated_date": "2025-10-29T14:15:25.431926", "years": 10, "total_months": 120, "start_date": "2014-01-01T00:00:00", Loading forecast_app/data_generator.py +2 −2 Original line number Diff line number Diff line Loading @@ -312,8 +312,8 @@ def main(): print(f"\n{watch_name}:") print(f" Avg Monthly Demand: {np.mean(demands):.1f} units") print(f" Avg Monthly Revenue: ${np.mean(revenues):,.2f}") print(f" Annual Revenue: ${np.sum(revenues):,.2f}") print(f" Avg Monthly Revenue: CHF {np.mean(revenues):,.2f}") print(f" Annual Revenue: CHF {np.sum(revenues):,.2f}") if __name__ == "__main__": Loading Loading
forecast_app/README.md +3 −3 Original line number Diff line number Diff line Loading @@ -16,9 +16,9 @@ Students analyze 10 years of historical sales data for 3 watch models and predic ## Watch Models 1. **Luxury Classic** ($500) - High-end watches with holiday seasonality 2. **Sport Pro** ($220) - Athletic watches popular in spring/summer 3. **Casual Style** ($120) - Everyday watches with steady demand 1. **Luxury Classic** (CHF 500) - High-end watches with holiday seasonality 2. **Sport Pro** (CHF 220) - Athletic watches popular in spring/summer 3. **Casual Style** (CHF 120) - Everyday watches with steady demand ## Installation Loading
forecast_app/app.py +1 −1 Original line number Diff line number Diff line Loading @@ -300,7 +300,7 @@ def download_excel(): ws_summary[f'A{idx}'] = watch['id'] ws_summary[f'B{idx}'] = watch['name'] ws_summary[f'C{idx}'] = watch['category'] ws_summary[f'D{idx}'] = f"${watch['sell_price']}" ws_summary[f'D{idx}'] = f"CHF {watch['sell_price']}" # Adjust column widths ws_summary.column_dimensions['A'].width = 8 Loading
forecast_app/data/supply_chain_data_full.json +1 −1 Original line number Diff line number Diff line { "metadata": { "generated_date": "2025-10-29T12:48:44.648537", "generated_date": "2025-10-29T14:15:25.431926", "years": 11, "total_months": 132, "start_date": "2014-01-01T00:00:00", Loading
forecast_app/data/supply_chain_data_training.json +1 −1 Original line number Diff line number Diff line { "metadata": { "generated_date": "2025-10-29T12:48:44.648537", "generated_date": "2025-10-29T14:15:25.431926", "years": 10, "total_months": 120, "start_date": "2014-01-01T00:00:00", Loading
forecast_app/data_generator.py +2 −2 Original line number Diff line number Diff line Loading @@ -312,8 +312,8 @@ def main(): print(f"\n{watch_name}:") print(f" Avg Monthly Demand: {np.mean(demands):.1f} units") print(f" Avg Monthly Revenue: ${np.mean(revenues):,.2f}") print(f" Annual Revenue: ${np.sum(revenues):,.2f}") print(f" Avg Monthly Revenue: CHF {np.mean(revenues):,.2f}") print(f" Annual Revenue: CHF {np.sum(revenues):,.2f}") if __name__ == "__main__": Loading