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I have a Dataset, where I need to predict the Energy Consumption. I have the September data, and need to predict the October values.

I need to predict the values of KWH for Oct. How do I write a python code, where September data would be my train data, and OCt would be my test data.

Please let me know, how to go about it in Python.

Dataset:

Timestamp                   KWH
9/7/2016 11:58:00 PM        3985.438677
9/8/2016 11:59:00 PM        3839.610784
9/9/2016 5:58:00 AM         4201.410989
9/10/2016 11:58:00 AM       4212.720572
9/11/2016 5:58:00 PM        4155.837292
9/12/2016 5:58:00 PM        4156.520936
09/13/2016 12:18            4165.410207
Anagha
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  • Could you narrow the field of your question a bit more, right now it seems as though you don't even know how to use pip.To start you off what format is the data in are you looking to manipulate a DataFrame, NumpyArray, List of Dicts...This matters due to the indexing conventions used by each one. On a side note, analytically speaking one month predict another introduces several undesired biases and I would fear that my test set was not a good representation (sample) of past occurrences. – Dan Temkin Mar 06 '17 at 10:09
  • I am using Azure ML, where I write my python code. I would prefer to use Pandas. If not splitting into Train and Test, what is the alternative that would give me the required result – Anagha Mar 06 '17 at 10:22

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