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import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor

#CSV of data
filepath = 'H:\\Data\\'
file = 'ScaledData'

#Read in data
features = pd.read_csv(filepath + file + '.csv')

#Features that we want to use are called the labels, we remove them from the features
labels = np.array([features['xVector'], features['yVector'], features['zVector'], features['length'], features['timeFromBeginning']])
features= features.drop('xVector', axis = 1)
features= features.drop('yVector', axis = 1)
features= features.drop('zVector', axis = 1)
features= features.drop('length', axis = 1)
features= features.drop('timeFromBeginning', axis = 1)
feature_list = list(features.columns)
features = np.array(features)

# Instantiate model with 1000 layers
rf = MLPRegressor(100)

# Train the model on training data
rf.fit(features, labels.transpose());

# Predict future events
futureEvents = np.array([521553,521554,521800])
predictions = rf.predict(futureEvents.reshape(-1, 1))
print(predictions)

I am trying to predict values of future events. There are 521553 data points that I am trying to learn based on and I am trying to predict future behavior. The data is all scaled to be between -1 and 1. Changing the event number that I am predicting the location and length and time of, I barely get a change in the predicted values even though the input values have a range over all values (and are rapidly varying). I have read somewhere that I should have a final linear layer though I didn't understand why to do this. I have simplified the code to what I think is the bare minimum required to show the problem.

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