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()