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predictor/chart_plotter.py

@@ -10,16 +10,19 @@ def plot_actual_vs_predicted(df: pd.DataFrame):
 
     plot_df = df.dropna(subset=["Product_Store_Sales_Total", "Predicted_Sales"])
 
-    scatter = alt.Chart(plot_df).mark_point(filled=True, size=70).encode(
-        x=alt.X("Product_Store_Sales_Total", title="Actual Sales"),
-        y=alt.Y("Predicted_Sales", title="Predicted Sales"),
-        tooltip=["Product_Store_Sales_Total", "Predicted_Sales"]
+    df["Error"] = abs(df["Product_Store_Sales_Total"] - df["Predicted_Sales"])
+
+    scatter = alt.Chart(df).mark_circle(size=60).encode(
+        x=alt.X('Product_Store_Sales_Total', title='Actual Sales'),
+        y=alt.Y('Predicted_Sales', title='Predicted Sales'),
+        color=alt.Color('Error:Q', scale=alt.Scale(scheme='redyellowblue'), legend=alt.Legend(title="Error")),
+        tooltip=['Product_Store_Sales_Total', 'Predicted_Sales', 'Error']
     ).properties(
-        title="Actual vs Predicted Sales",
+        title='Actual vs Predicted Sales',
         width=600,
         height=400
     )
-
+    
     line = alt.Chart(pd.DataFrame({
         'x': [plot_df["Product_Store_Sales_Total"].min(), plot_df["Product_Store_Sales_Total"].max()],
         'y': [plot_df["Product_Store_Sales_Total"].min(), plot_df["Product_Store_Sales_Total"].max()]