Refine dupes calculation in predict_dupes function by adjusting the formula to prioritize Contest_Size and ownership metrics, enhancing the accuracy of duplicate predictions.

global_func/predict_dupes.py

@@ -527,8 +527,8 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
                 print(portfolio[['Own', 'own_product', 'own_average', 'own_sum', 'avg_own_rank']].head(10))
                 
                 # Calculate dupes formula
-                portfolio['dupes_calc'] = (portfolio['own_product'] * portfolio['avg_own_rank']) * (portfolio['Own'] / (100 + (Contest_Size / 1000))) * Contest_Size + ((portfolio['salary'] - (50000 - portfolio['Own'])) / 100) - ((49500 - portfolio['salary']) / 100)
-                portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier
+                portfolio['dupes_calc'] = (portfolio['own_product'] * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (50000 - portfolio['Own'])) / 100) - ((50000 - portfolio['salary']) / 100)
+                portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier * (portfolio['Own'] / (100 + (Contest_Size / 1000)))
 
                 # Round and handle negative values
                 portfolio['Dupes'] = np.where(