I want to create a model with LSTM to predict a user the next purchase value. For this I have used I used a user's purchase history. I have created the model and it works well, but honestly, I don't know I do the Train/Test split on the proper way or not.
To do this, I have used (univariate) user's purchase history.(X-purchase history values, y-target purchase value) As a first step, I have created a sliding-window process that creates new data. (As you can see in the pic) In the original dataset, I had 1000 users with 2820 timestamps and 1 feature (purchase values), with the Sliding-window process I got 1000*2320 users with 500 timestamps and 1 feature.
X.shape -> OriginalDataShape (1000, 2820, 1)
# model model = Sequential() model.add(LSTM(50, activation='relu', input_shape=(500,1))) model.add(Dense(1)) model.compile(loss='mae', optimizer='adam') # train repeats = range(3) scores = list() for i in repeats: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42) model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=32) pred = model.predict(X_test) score.append(metrics.mean_squared_error(pred,y_test)) print('Final score (Mse):') print(score)
My questions are: It is the proper way or not? If it is not, do you have a suggestion or GitHub link for the solution?