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colleagues, actually I am kind'a new to NN, but hard trying..

I have data:

Index: 40073 entries (excluded from training, UID)

Columns: 484 entries dtypes: bool(468), float64(2), int64(13), object(1)

I used only 478 arguments. The Y is moneySpend which can be >= 0

The code is below:

newDropped = df.drop(["moneySpend","userAgent", "secondsToBuy", "hoursToBuy", "daysToBuy", "platform"], axis = 1)

x_train, x_test, y_train, y_test = train_test_split(newDropped, df["moneySpend"], test_size=0.25, random_state=547)

model = Sequential()
dnn1.add(Dense(16, input_dim=478, activation='relu'))
dnn1.add(Dense(8, activation='relu'))
dnn1.add(Dense(1, activation='linear'))


model.compile(loss='mse', optimizer='adam', metrics=['accuracy'])
tb_callback = tf.keras.callbacks.TensorBoard('./logs', update_freq=1)
    
history = model.fit(x_train.astype('int'),  y_train.astype('int'), validation_split=0.33, epochs=100, batch_size=5, callbacks=[tb_callback])

I have kind'a big losses, and no idea what number of neurons to use at each Dense layer (should I use Dense or LSTM?) and what proper number of layers and activation functions to use.

Regularization is making training worse, so I turned it off.

Any good advices for me, if you can?

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