xgboost.ubj

app.py

@@ -9,7 +9,7 @@ import pandas as pd
 # xgb = pickle.load(open(filename, 'rb'))
 
 xgb = XGBRegressor(n_estimators=1000, learning_rate=0.04, objective='reg:squarederror') #xgboost. # or which ever sklearn booster you're are using
-xgb.load_model("xgboost_model_all.json")
+xgb.load_model("xgboost_model_all.ubj")
 
 def predict_price(area, year, rooms, floor_number, distance_from_center, alarm_system,
                   security_system, terrasse, parking, high_ceiling, multy_storey_apartment, private_yard,