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I am very new to Keras, neural networks and machine learning having just started to learn yesterday. I decided to try predicting the experience over an hour (0 to 23) (for a game and my own generated data-set) that a user would earn. Currently running what I have the predictions seem to be very low and very poor. I have tried a relu activation, which produced predictions all to be zero and from a bit of research, LeakyReLU.

This is the code I have for the prediction model so far:

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LeakyReLU
import numpy

numpy.random.seed(7)
dataset = numpy.loadtxt("experience.csv", delimiter=",")

X = dataset[: ,0]
Y = dataset[: ,1]

model = Sequential()
model.add(Dense(12, input_dim = 1, activation=LeakyReLU(0.3)))
model.add(Dense(8, activation=LeakyReLU(0.3)))
model.add(Dense(1, activation=LeakyReLU(0.3)))

model.compile(loss = 'mean_absolute_error', optimizer='adam', metrics = ['accuracy'])

model.fit(X, Y, epochs=120, batch_size=10, verbose = 0)

predictions = model.predict(X)

rounded = [round(x[0]) for x in predictions]

print(rounded)

I have also tried playing around with the hidden levels of the network, but honestly have no idea how many there should be or a good way to justify an amount.

If it helps here is the data-set I have been using:

https://raw.githubusercontent.com/NightShadeII/xpPredictor/master/experience.csv

Thankyou for any help

Looking at your data it does not seem like a classification problem.
You have two options:

-> Look at the second column and bucket them depending on the ranges and make classes that can be predicted, for instance: 0, 1, 2 etc. Now it tries to train but does not have enough examples for millions of classes that it thinks you are trying to predict.

-> If you want real valued output and not classes, try using linear regression.