GaloPinzonChz's repositories
Measurement-of-Electric-Power-Consumption-in-Household
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.
Electric-Power-Consumption
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.
Final-Assigment_ExploratoryData-Analysis_Week4
Fine particulate matter (PM2.5) is an ambient air pollutant for which there is strong evidence that it is harmful to human health. In the United States, the Environmental Protection Agency (EPA) is tasked with setting national ambient air quality standards for fine PM and for tracking the emissions of this pollutant into the atmosphere