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Individual household electric-power consumption Data Set (LSTM) [tutorial]
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Jupyter notebook (tutorial for the beginners in Deep Learning and time-series data analysis)
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README.md

Individual household electric-power consumption Data Set

This Notebook is a sort of tutorial for the beginners in Deep-Learning and time-series data analysis.

The aim is just to show how to build the simplest Long short-term memory (LSTM) recurrent neural network for the data.

The description of data can be found here:

http://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption

Attribute Information:

1.date: Date in format dd/mm/yyyy

2.time: time in format hh:mm:ss

3.global_active_power: household global minute-averaged active power (in kilowatt)

4.global_reactive_power: household global minute-averaged reactive power (in kilowatt)

5.voltage: minute-averaged voltage (in volt)

6.global_intensity: household global minute-averaged current intensity (in ampere)

7.sub_metering_1: energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered).

8.sub_metering_2: energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light.

9.sub_metering_3: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner.

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