How to train a ML model efficiently without using a loop?

TensorFlow General Discussion
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I have a pandas dataframe X with 6 columns and a dataframe Y with 4 columns. I’d like to forecast every row from Y using X, but without using the row that I want to forecast during the training. I got that using a loop, but I think there should be more efficient ways to do that (maybe without using a loop). Any idea?

    from tensorflow.keras import Sequential
    from tensorflow.keras.layers import Dense
    model = Sequential()
    nn=X.shape[1]
    model.add(Dense(6, activation='relu',input_shape=(nn,)))
    model.add(Dense(4, activation='sigmoid'))
    model.compile(optimizer='adam',loss='mse',metrics=['accuracy'])
    yy=[]
    for i in range(len(X)):
     results=model.fit(X.drop(i),Y.drop(i),epochs=900,
              verbose=0)
     yy+=model.predict(X.iloc[[i]]).tolist()
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Hi @Okabe_Rintarou

Welcome to the TensroFlow Forum!

It seems you are using sequential data or timeseries data for the model training. You can use RNN model for the model building on this type of data. Please refer to this Tensorflow tutorial for working with RNNs on sequential data for your reference. Thank you.