update

demand_forecast/bokeh_forecast.py

@@ -0,0 +1,33 @@
+from bokeh.plotting import figure, show, output_file
+from bokeh.layouts import column
+from bokeh.models import ColumnDataSource
+
+
+
+
+
+
+
+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))
+
+    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')
+
+    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))