Update dupes calculation in 'predict_dupes.py' by changing the denominator in the scaling factor for CPT_Own_percent_rank from 100 to 90, aiming to further enhance prediction accuracy.

global_func/predict_dupes.py

@@ -194,7 +194,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
         portfolio['dupes_calc'] = ((portfolio['own_product'] + ((portfolio['CPT_Own_percent_rank'] - .50) / 1000) + ((portfolio['Own'] / 6) / (max_salary / 2))) * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (max_salary - portfolio['Own'])) / 100) - ((max_salary - portfolio['salary']) / 100)
         portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier * (portfolio['Own'] / (own_baseline + (Contest_Size / 1000)))
         portfolio['dupes_calc'] = ((((portfolio['salary'] / (max_salary * 0.98)) - 1)*(max_salary / 10000)) + 1) * portfolio['dupes_calc']
-        portfolio['dupes_calc'] = portfolio['dupes_calc'] * ((portfolio['CPT_Own_percent_rank'] + .50) / (portfolio['Own'] / 100))
+        portfolio['dupes_calc'] = portfolio['dupes_calc'] * ((portfolio['CPT_Own_percent_rank'] + .50) / (portfolio['Own'] / 90))
         
         # Round and handle negative values
         portfolio['Dupes'] = np.where(