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GDGVIT · Oct 24, 2024 · 3f01e17 · 3f01e17
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diff --git a/demand_forecast/__pycache__/bokeh_forecast.cpython-310.pyc b/demand_forecast/__pycache__/bokeh_forecast.cpython-310.pyc
diff --git a/demand_forecast/__pycache__/forecasting.cpython-310.pyc b/demand_forecast/__pycache__/forecasting.cpython-310.pyc
diff --git a/demand_forecast/best_model.keras b/demand_forecast/best_model.keras
diff --git a/demand_forecast/bokeh_forecast.py b/demand_forecast/bokeh_forecast.py
@@ -1,33 +1,22 @@
 from bokeh.plotting import figure, show, output_file
 from bokeh.layouts import column
 from bokeh.models import ColumnDataSource
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 def create_bokeh_plots(df, item_id, future_months, predicted_demand):
     item_data = df[df['item_id'] == item_id].copy()
     item_data['year_month'] = item_data['transaction_date'].dt.to_period('M')
     actual_demand = item_data.groupby('year_month')['quantity'].sum().reset_index()
-
     actual_source = ColumnDataSource(
         data=dict(month=actual_demand['year_month'].dt.to_timestamp(), quantity=actual_demand['quantity']))
     predicted_source = ColumnDataSource(data=dict(month=future_months, quantity=predicted_demand))
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     actual_plot = figure(title=f'Actual Demand for Item ID {item_id}', x_axis_label='Date', y_axis_label='Quantity',
                          x_axis_type='datetime')
     actual_plot.line('month', 'quantity', source=actual_source, line_width=2, color='blue',
                      legend_label='Actual Demand')
     actual_plot.scatter('month', 'quantity', source=actual_source, size=8, color='blue')
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     predicted_plot = figure(title=f'Predicted Demand for Item ID {item_id}', x_axis_label='Date',
                             y_axis_label='Quantity', x_axis_type='datetime')
     predicted_plot.line('month', 'quantity', source=predicted_source, line_width=2, color='orange',
                         legend_label='Predicted Demand')
     predicted_plot.scatter('month', 'quantity', source=predicted_source, size=8, color='orange')
-
     output_file("demand_forecasting.html")
     show(column(actual_plot, predicted_plot))