Model changes

api.py

@@ -30,7 +30,7 @@
 # Load our model into memory.
 # Please update this path to reflect your own trained model.
 static_model = load_model(
-    path_to_model='assets/trained-models/load_shortfall_simple_lm_regression.pkl')
+    path_to_model='assets/trained-models/Random_Forest_Reg_Model.pkl')
 
 print ('-'*40)
 print ('Model successfully loaded')

model.py

@@ -48,6 +48,21 @@ def _preprocess_data(data):
     feature_vector_dict = json.loads(data)
     # Load the dictionary as a Pandas DataFrame.
     feature_vector_df = pd.DataFrame.from_dict([feature_vector_dict])
+    new_features = ['Unnamed: 0', 'Madrid_wind_speed', 'Bilbao_rain_1h',
+            'Valencia_wind_speed', 'Seville_humidity', 'Madrid_humidity',
+            'Bilbao_clouds_all', 'Bilbao_wind_speed', 'Seville_clouds_all',
+            'Bilbao_wind_deg', 'Barcelona_wind_speed', 'Barcelona_wind_deg',
+            'Madrid_clouds_all', 'Seville_wind_speed', 'Barcelona_rain_1h',
+            'Seville_rain_1h', 'Bilbao_snow_3h', 'Barcelona_pressure',
+            'Seville_rain_3h', 'Madrid_rain_1h', 'Barcelona_rain_3h',
+            'Valencia_snow_3h', 'Madrid_weather_id', 'Barcelona_weather_id',
+            'Bilbao_pressure', 'Seville_weather_id', 'Seville_temp_max',
+            'Madrid_pressure', 'Valencia_temp_max', 'Valencia_temp',
+            'Bilbao_weather_id', 'Seville_temp', 'Valencia_humidity',
+            'Valencia_temp_min', 'Barcelona_temp_max', 'Madrid_temp_max',
+            'Barcelona_temp', 'Bilbao_temp_min', 'Bilbao_temp',
+            'Barcelona_temp_min', 'Bilbao_temp_max', 'Seville_temp_min',
+            'Madrid_temp', 'Madrid_temp_min']
 
     # ---------------------------------------------------------------
     # NOTE: You will need to swap the lines below for your own data
@@ -58,7 +73,7 @@ def _preprocess_data(data):
     # ---------------------------------------------------------------
 
     # ----------- Replace this code with your own preprocessing steps --------
-    predict_vector = feature_vector_df[['Madrid_wind_speed','Bilbao_rain_1h','Valencia_wind_speed']]
+    predict_vector = feature_vector_df[new_features]
     # ------------------------------------------------------------------------
 
     return predict_vector