This assignment uses data from the UC Irvine Machine Learning Repository, a popular repository for machine learning datasets. In particular, we will be using the “Individual household electric power consumption Data Set” which I have made available on the course web site:
Dataset: Electric power consumption [20Mb] Description: Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available. The following descriptions of the 9 variables in the dataset are taken from the UCI web site:
Date: Date in format dd/mm/yyyy Time: time in format hh:mm:ss Global_active_power: household global minute-averaged active power (in kilowatt) Global_reactive_power: household global minute-averaged reactive power (in kilowatt) Voltage: minute-averaged voltage (in volt) Global_intensity: household global minute-averaged current intensity (in ampere) 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). 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. 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.