Update app.py

app.py

@@ -16,9 +16,9 @@ df.dropna(inplace=True)
 m = NeuralProphet(n_forecasts=3,
     n_lags=12, 
     changepoints_range=9, num_hidden_layers=6, daily_seasonality= False, weekly_seasonality = False, yearly_seasonality = True, ar_reg=True,
-    n_changepoints=450, trend_reg_threshold=True, d_hidden=9, global_normalization=True, global_time_normalization=True, seasonality_reg=1, unknown_data_normalization=True,
+    n_changepoints=350, trend_reg_threshold=True, d_hidden=9, global_normalization=True, global_time_normalization=True, seasonality_reg=1, unknown_data_normalization=True,
     seasonality_mode="multiplicative", drop_missing=True,
-    learning_rate=0.01
+    learning_rate=0.1
 )
 
 m.fit(df, freq='M')