Keras.Predict into Dataframe

[Finpyth]

The colon is on the opposite side in train_features vs X[train_size:], so those are not the same thing.
After correcting that, do

predictions.shape()

and post

You are right about X[train_size:] and train_features is not the same. My bad.

I tried your solution but it did not work, but i kinda found a strange 2-step solution.
maybe you know why it works?

predictions = pd.DataFrame()
for i in target_list:
   y = np.array(period[i])
   split = sm.add_constant(X)
   train_size = int(0.75*y.shape[0])

   train_features = X[:train_size]
   train_targets = y[:train_size]
   test_features = X[train_size:]
   test_targets = y[train_size:]

   model = Sequential()
   model.add(Dense(50,input_dim=train_features.shape[1],activation='relu'))
   model.add(Dense(50,activation='relu'))
   model.add(Dense(1,activation='linear'))
   model.compile(optimizer='adam', loss='mse')
   model.fit(X, y, epochs=1, verbose=1)
   print(i)
   predictions = pd.DataFrame(model.predict(X[train_size:]), columns=[i])

then i remove "predictions = pd.DataFrame()" and add [i] in the last line and get this:

for i in target_list:
   y = np.array(period[i])
   split = sm.add_constant(X)
   train_size = int(0.75*y.shape[0])

   train_features = X[:train_size]
   train_targets = y[:train_size]
   test_features = X[train_size:]
   test_targets = y[train_size:]

   model = Sequential()
   model.add(Dense(50,input_dim=train_features.shape[1],activation='relu'))
   model.add(Dense(50,activation='relu'))
   model.add(Dense(1,activation='linear'))
   model.compile(optimizer='adam', loss='mse')
   model.fit(X, y, epochs=1, verbose=1)
   print(i)
   predictions[i] = pd.DataFrame(model.predict(X[train_size:]), columns=[i])

and now it returns the dataframe, "predictions" with all the predictions for all my targets. If i don't edit the script, it doesn't run

best ragards