Inputing 5 Row Data,classifiying(predicting) 6th Row In Keras LSTM

I want to input 5 row of a dataset to LSTM and classify 6th row of Y. I made the input by reshaping the data:

X = X.reshape(6000,5,5)

But how can I predict Y of 6th row? What shape Y should have? I read a lot and search a lot on Google,but honestly I couldn't understand the code because they customize their data before using them.

My full code:

import pandas as pd
from sklearn.preprocessing import LabelEncoder,MinMaxScaler
from keras.layers import Dense,RNN,LSTM,Activation,Dropout,SimpleRNN,Bidirectional
from keras.optimizers import RMSprop,Adam
from keras.wrappers.scikit_learn import KerasClassifier
from keras.models import Sequential
from sklearn.model_selection import train_test_split
import numpy as np

df = pd.read_csv('./EURUSD_DATAFRAME.csv')
BinEncoder = LabelEncoder()
scalar = MinMaxScaler()

df['pos'] = df['pos'].astype('int')
dat = df.values




X = dat[0:30000,0:5]
Y = dat[0:6000,5]


X[:,4]= BinEncoder.fit_transform(X[:,4])
X[:,0:4] = scalar.fit_transform(X[:,0:4])
X[:,4] = X[:,4].astype('int')
Y = BinEncoder.fit_transform(Y)

X = X.reshape(6000,5,5)
#MODEL 3
 model = Sequential()
 model.add(LSTM(1024,input_shape=(5,5),return_sequences=True,kernel_initializer='normal'))
 #model.add(Activation('tanh'))
 model.add(Dropout(0.2))
 model.add(LSTM(512))
 model.add(Dropout(0.3))
 model.add(Dense(1))
 model.add(Activation('sigmoid'))
 model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
 model.fit(X,Y,batch_size=100,epochs=10,validation_split=0.2)
 p = model.predict(X)
# print(classifier.score(X,Y))
# ----

I am classifiying Y which is Encoded as 1 or 0.

Sorry, but I don't understand really what do you mean by this? Your input shape is (6000, 5, 5) and your output shape is (6000, 2)? So what do you mean by sixth row?