We are utilizing information from the UC Irvine Machine Learning Repository, a famous archive for AI datasets. Specifically, we will utilize the “individual household electric power consumption Data Set”.
Data Set :
The accompanying portrayals of the 9 factors in the dataset are taken from the UCI web site.
Data Set Information:
This archive contains 2075259 measurements gathered in a house located in Sceaux (7km of Paris, France) between December 2006 and November 2010 (47 months).
1.(global_active_power*1000/60 – sub_metering_1 – sub_metering_2 – sub_metering_3) represents the active energy consumed every minute (in watt-hour) in the household by electrical equipment not measured in sub-metering 1, 2 and 3.
2. The dataset contains some missing values in the measurements (nearly 1,25% of the rows). All calendar timestamps are present in the dataset but for some timestamps, the measurement values are missing: a missing value is represented by the absence of value between two consecutive semi-colon attribute separators. For instance, the dataset shows missing values on April 28, 2007.
What We Will Do:
Our general objective here is basically to analyze how family unit vitality utilization differs over a 2-day time span in February 2007. We will reproduce the accompanying plots beneath, which were all developed utilizing the base plotting framework.
- Constructing the plot and spare it to a PNG record with a width of 480 pixels and a stature of 480 pixels.
- Naming every one of the plot records as ‘plot1.png’, ‘plot2.png’, and so on.
- Creating a different R code document (‘plot1.R’, ‘plot2.R’, and so on.) that builds the comparing plot, for example, code in ‘plot1.R’ develops the ‘plot1.png’ plot.
The four plots will look like this as an output:
1. Constructing the First Plot
2. Constructing 2nd Plot
3. Constructing 3rd Plot
4. Constructing Final Plot
For More Information Please Visit the UC Irvine Machine Learning Repository
You can also check my Github Page For reference Here